
@InCollection{	  Ronneberger2015,
  title		= {U-{Net}: {Convolutional} {Networks} for {Biomedical}
		  {Image} {Segmentation}},
  isbn		= {978-3-319-24573-7},
  url		= {http://link.springer.com/10.1007/978-3-319-24574-4_28},
  booktitle	= {{MICCAI}},
  author	= {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  year		= {2015},
  pmid		= {23285570},
  doi		= {10.1007/978-3-319-24574-4\_28},
  note		= {ISSN: 16113349},
  pages		= {234--241},
  file		= {PDF:/Users/davisvigneault/Zotero/storage/SKGE5QNJ/2015-U-Net_Convolutional_Networks_for_Biomedical_Image_Segmentation.pdf:application/pdf}
}

@InProceedings{	  Milletari2016,
  title		= {V-{Net}: {Fully} {Convolutional} {Neural} {Networks} for
		  {Volumetric} {Medical} {Image} {Segmentation}},
  isbn		= {978-1-5090-5407-7},
  url		= {http://arxiv.org/abs/1606.04797},
  doi		= {10.1109/3DV.2016.79},
  booktitle	= {{CVPR}},
  author	= {Milletari, Fausto and Navab, Nassir and Ahmadi,
		  Seyed-Ahmad},
  year		= {2016},
  pages		= {1--11}
}

@Article{	  Yushkevich2019,
  title		= {User-{Guided} {Segmentation} of {Multi}-modality {Medical}
		  {Imaging} {Datasets} with {ITK}-{SNAP}},
  volume	= {17},
  issn		= {15392791},
  doi		= {10.1007/s12021-018-9385-x},
  abstract	= {ITK-SNAP is an interactive software tool for manual and
		  semi-automatic segmentation of 3D medical images. This
		  paper summarizes major new features added to ITK-SNAP over
		  the last decade. The main focus of the paper is on new
		  features that support semi-automatic segmentation of
		  multi-modality imaging datasets, such as MRI scans acquired
		  using different contrast mechanisms (e.g., T1, T2, FLAIR).
		  The new functionality uses decision forest classifiers
		  trained interactively by the user to transform multiple
		  input image volumes into a foreground/background
		  probability map; this map is then input as the data term to
		  the active contour evolution algorithm, which yields
		  regularized surface representations of the segmented
		  objects of interest. The new functionality is evaluated in
		  the context of high-grade and low-grade glioma segmentation
		  by three expert neuroradiogists and a non-expert on a
		  reference dataset from the MICCAI 2013 Multi-Modal Brain
		  Tumor Segmentation Challenge (BRATS). The accuracy of
		  semi-automatic segmentation is competitive with the top
		  specialized brain tumor segmentation methods evaluated in
		  the BRATS challenge, with most results obtained in ITK-SNAP
		  being more accurate, relative to the BRATS reference manual
		  segmentation, than the second-best performer in the BRATS
		  challenge; and all results being more accurate than the
		  fourth-best performer. Segmentation time is reduced over
		  manual segmentation by 2.5 and 5 times, depending on the
		  rater. Additional experiments in interactive placenta
		  segmentation in 3D fetal ultrasound illustrate the
		  generalizability of the new functionality to a different
		  problem domain.},
  number	= {1},
  journal	= {Neuroinformatics},
  author	= {Yushkevich, Paul A. and Pashchinskiy, Artem and Oguz, Ipek
		  and Mohan, Suyash and Schmitt, J. Eric and Stein, Joel M.
		  and Zukić, Dženan and Vicory, Jared and McCormick,
		  Matthew and Yushkevich, Natalie and Schwartz, Nadav and
		  Gao, Yang and Gerig, Guido},
  year		= {2019},
  pmid		= {29946897},
  keywords	= {Software, MRI, Image segmentation, Gliomas, Semi-automatic
		  segmentation},
  pages		= {83--102},
  file		= {PDF:/Users/davisvigneault/Zotero/storage/EBFJX96T/nihms-978131.pdf:application/pdf}
}

@Article{	  gulati_2021_2021,
  title		= {2021 {AHA}/{ACC}/{ASE}/{CHEST}/{SAEM}/{SCCT}/{SCMR}
		  {Guideline} for the {Evaluation} and {Diagnosis} of {Chest}
		  {Pain}: {Executive} {Summary}: {A} {Report} of the
		  {American} {College} of {Cardiology}/{American} {Heart}
		  {Association} {Joint} {Committee} on {Clinical} {Practice}
		  {Guidelines}},
  issn		= {0009-7322, 1524-4539},
  shorttitle	= {2021 {AHA}/{ACC}/{ASE}/{CHEST}/{SAEM}/{SCCT}/{SCMR}
		  {Guideline} for the {Evaluation} and {Diagnosis} of {Chest}
		  {Pain}},
  url		= {https://www.ahajournals.org/doi/10.1161/CIR.0000000000001030},
  doi		= {10.1161/CIR.0000000000001030},
  abstract	= {Aim: This executive summary of the clinical practice
		  guideline for the evaluation and diagnosis of chest pain
		  provides recommendations and algorithms for clinicians to
		  assess and diagnose chest pain in adult patients.
		  
		  Methods: A comprehensive literature search was conducted
		  from November 11, 2017, to May 1, 2020, encompassing
		  studies, reviews, and other evidence conducted on human
		  subjects that were published in English from PubMed,
		  EMBASE, the Cochrane Collaboration, Agency for Healthcare
		  Research and Quality reports, and other relevant databases.
		  Additional relevant studies, published through April 2021,
		  were also considered.
		  
		  Structure: Chest pain is a frequent cause for emergency
		  department visits in the United States. The “2021
		  AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the
		  Evaluation and Diagnosis of Chest Pain” provides
		  recommendations based on contemporary evidence on the
		  assessment and evaluation of chest pain. These guidelines
		  present an evidence-based approach to risk stratification
		  and the diagnostic workup for the evaluation of chest pain.
		  Cost-value considerations in diagnostic testing have been
		  incorporated and shared decision-making with patients is
		  recommended.},
  language	= {en},
  urldate	= {2021-11-15},
  journal	= {Circulation},
  author	= {Gulati, Martha and Levy, Phillip D. and Mukherjee,
		  Debabrata and Amsterdam, Ezra and Bhatt, Deepak L. and
		  Birtcher, Kim K. and Blankstein, Ron and Boyd, Jack and
		  Bullock-Palmer, Renee P. and Conejo, Theresa and Diercks,
		  Deborah B. and Gentile, Federico and Greenwood, John P. and
		  Hess, Erik P. and Hollenberg, Steven M. and Jaber, Wael A.
		  and Jneid, Hani and Joglar, José A. and Morrow, David A.
		  and O’Connor, Robert E. and Ross, Michael A. and Shaw,
		  Leslee J.},
  month		= oct,
  year		= {2021},
  pages		= {CIR.0000000000001030},
  file		= {Gulati et al. - 2021 - 2021 AHAACCASECHESTSAEMSCCTSCMR
		  Guideline
		  fo.pdf:/Users/davisvigneault/Zotero/storage/22UKDXVE/Gulati
		  et al. - 2021 - 2021 AHAACCASECHESTSAEMSCCTSCMR Guideline
		  fo.pdf:application/pdf}
}

@InProceedings{	  he_deep_2016,
  address	= {Las Vegas, NV, USA},
  title		= {Deep {Residual} {Learning} for {Image} {Recognition}},
  isbn		= {978-1-4673-8851-1},
  url		= {http://ieeexplore.ieee.org/document/7780459/},
  doi		= {10.1109/CVPR.2016.90},
  abstract	= {Deeper neural networks are more difﬁcult to train. We
		  present a residual learning framework to ease the training
		  of networks that are substantially deeper than those used
		  previously. We explicitly reformulate the layers as
		  learning residual functions with reference to the layer
		  inputs, instead of learning unreferenced functions. We
		  provide comprehensive empirical evidence showing that these
		  residual networks are easier to optimize, and can gain
		  accuracy from considerably increased depth. On the ImageNet
		  dataset we evaluate residual nets with a depth of up to 152
		  layers—8× deeper than VGG nets [40] but still having
		  lower complexity. An ensemble of these residual nets
		  achieves 3.57\% error on the ImageNet test set. This result
		  won the 1st place on the ILSVRC 2015 classiﬁcation task.
		  We also present analysis on CIFAR-10 with 100 and 1000
		  layers.},
  language	= {en},
  urldate	= {2023-01-13},
  booktitle	= {2016 {IEEE} {Conference} on {Computer} {Vision} and
		  {Pattern} {Recognition} ({CVPR})},
  publisher	= {IEEE},
  author	= {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun,
		  Jian},
  month		= jun,
  year		= {2016},
  pages		= {770--778},
  file		= {He et al. - 2016 - Deep Residual Learning for Image
		  Recognition.pdf:/Users/davisvigneault/Zotero/storage/Y64P8DPP/He
		  et al. - 2016 - Deep Residual Learning for Image
		  Recognition.pdf:application/pdf}
}

@Article{	  chen_automated_2021,
  title		= {Automated cardiac volume assessment and cardiac long- and
		  short-axis imaging plane prediction from
		  electrocardiogram-gated computed tomography volumes enabled
		  by deep learning},
  volume	= {2},
  issn		= {2634-3916},
  url		= {https://academic.oup.com/ehjdh/article/2/2/311/6179804},
  doi		= {10.1093/ehjdh/ztab033},
  language	= {en},
  number	= {2},
  urldate	= {2023-08-11},
  journal	= {European Heart Journal - Digital Health},
  author	= {Chen, Zhennong and Rigolli, Marzia and Vigneault, Davis
		  Marc and Kligerman, Seth and Hahn, Lewis and Narezkina,
		  Anna and Craine, Amanda and Lowe, Katherine and Contijoch,
		  Francisco},
  year		= {2021},
  note		= {author+an: 3=gras},
  pages		= {311--322},
  file		= {Chen et al. - 2021 - Automated cardiac volume assessment
		  and cardiac
		  lo.pdf:/Users/davisvigneault/Zotero/storage/W7C7Z94J/Chen
		  et al. - 2021 - Automated cardiac volume assessment and
		  cardiac lo.pdf:application/pdf}
}

@Article{	  bruns_deep_2020,
  title		= {Deep learning from dual‐energy information for
		  whole‐heart segmentation in dual‐energy and
		  single‐energy non‐contrast‐enhanced cardiac {CT}},
  volume	= {47},
  issn		= {0094-2405, 2473-4209},
  url		= {https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.14451},
  doi		= {10.1002/mp.14451},
  abstract	= {Purpose: Deep learning-based whole-heart segmentation in
		  coronary computed tomography angiography (CCTA) allows the
		  extraction of quantitative imaging measures for
		  cardiovascular risk prediction. Automatic extraction of
		  these measures in patients undergoing only
		  non-contrast-enhanced CT (NCCT) scanning would be valuable,
		  but defining a manual reference standard that would allow
		  training a deep learning-based method for whole-heart
		  segmentation in NCCT is challenging, if not impossible. In
		  this work, we leverage dual-energy information provided by
		  a dual-layer detector CT scanner to obtain a reference
		  standard in virtual non-contrast (VNC) CT images mimicking
		  NCCT images, and train a three-dimensional (3D)
		  convolutional neural network (CNN) for the segmentation of
		  VNC as well as NCCT images. Methods: Eighteen patients were
		  scanned with and without contrast enhancement on a
		  dual-layer detector CT scanner. Contrast-enhanced
		  acquisitions were reconstructed into a CCTA and a perfectly
		  aligned VNC image. In each CCTA image, manual reference
		  segmentations of the left ventricular (LV) myocardium, LV
		  cavity, right ventricle, left atrium, right atrium,
		  ascending aorta, and pulmonary artery trunk were obtained
		  and propagated to the corresponding VNC image. These VNC
		  images and reference segmentations were used to train 3D
		  CNNs in a sixfold cross-validation for automatic
		  segmentation in either VNC images or NCCT images
		  reconstructed from the non-contrastenhanced acquisition.
		  Automatic segmentation in VNC images was evaluated using
		  the Dice similarity coefficient (DSC) and average symmetric
		  surface distance (ASSD). Automatically determined volumes
		  of the cardiac chambers and LV myocardium in NCCT were
		  compared to reference volumes of the same patient in CCTA
		  by Bland–Altman analysis. An additional independent
		  multivendor multicenter set of single-energy NCCT images
		  from 290 patients was used for qualitative analysis, in
		  which two observers graded segmentations on a five-point
		  scale. Results: Automatic segmentations in VNC images
		  showed good agreement with reference segmentations, with an
		  average DSC of 0.897 Æ 0.034 and an average ASSD of 1.42
		  Æ 0.45 mm. Volume differences [95\% confidence interval]
		  between automatic NCCT and reference CCTA segmentations
		  were À19 [À67; 30] mL for LV myocardium, À25 [À78; 29]
		  mL for LV cavity, À29 [À73; 14] mL for right ventricle,
		  À20 [À62; 21] mL for left atrium, and À19 [À73; 34] mL
		  for right atrium, respectively. In 214 (74\%) NCCT images
		  from the independent multivendor multicenter set, both
		  observers agreed that the automatic segmentation was mostly
		  accurate (grade 3) or better. Conclusion: Our automatic
		  method produced accurate whole-heart segmentations in NCCT
		  images using a CNN trained with VNC images from a
		  dual-layer detector CT scanner. This method might enable
		  quantification of additional cardiac measures from NCCT
		  images for improved cardiovascular risk prediction. © 2020
		  American Association of Physicists in Medicine
		  [https://doi.org/10.1002/ mp.14451]},
  language	= {en},
  number	= {10},
  urldate	= {2023-10-23},
  journal	= {Medical Physics},
  author	= {Bruns, Steffen and Wolterink, Jelmer M. and Takx, Richard
		  A. P. and Van Hamersvelt, Robbert W. and Suchá, Dominika
		  and Viergever, Max A. and Leiner, Tim and Išgum, Ivana},
  month		= oct,
  year		= {2020},
  pages		= {5048--5060},
  file		= {Bruns et al. - 2020 - Deep learning from dual‐energy
		  information for
		  who.pdf:/Users/davisvigneault/Zotero/storage/DIIILA32/Bruns
		  et al. - 2020 - Deep learning from dual‐energy
		  information for who.pdf:application/pdf}
}

@Misc{		  chen_transunet_2021,
  title		= {{TransUNet}: {Transformers} {Make} {Strong} {Encoders} for
		  {Medical} {Image} {Segmentation}},
  shorttitle	= {{TransUNet}},
  url		= {http://arxiv.org/abs/2102.04306},
  abstract	= {Medical image segmentation is an essential prerequisite
		  for developing healthcare systems, especially for disease
		  diagnosis and treatment planning. On various medical image
		  segmentation tasks, the ushaped architecture, also known as
		  U-Net, has become the de-facto standard and achieved
		  tremendous success. However, due to the intrinsic locality
		  of convolution operations, U-Net generally demonstrates
		  limitations in explicitly modeling long-range dependency.
		  Transformers, designed for sequence-to-sequence prediction,
		  have emerged as alternative architectures with innate
		  global self-attention mechanisms, but can result in limited
		  localization abilities due to insuﬃcient low-level
		  details. In this paper, we propose TransUNet, which merits
		  both Transformers and U-Net, as a strong alternative for
		  medical image segmentation. On one hand, the Transformer
		  encodes tokenized image patches from a convolution neural
		  network (CNN) feature map as the input sequence for
		  extracting global contexts. On the other hand, the decoder
		  upsamples the encoded features which are then combined with
		  the high-resolution CNN feature maps to enable precise
		  localization.},
  language	= {en},
  urldate	= {2023-11-08},
  publisher	= {arXiv},
  author	= {Chen, Jieneng and Lu, Yongyi and Yu, Qihang and Luo,
		  Xiangde and Adeli, Ehsan and Wang, Yan and Lu, Le and
		  Yuille, Alan L. and Zhou, Yuyin},
  month		= feb,
  year		= {2021},
  note		= {arXiv:2102.04306 [cs]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition},
  file		= {Chen et al. - 2021 - TransUNet Transformers Make Strong
		  Encoders for
		  M.pdf:/Users/davisvigneault/Zotero/storage/XXB4VVG2/Chen et
		  al. - 2021 - TransUNet Transformers Make Strong Encoders
		  for M.pdf:application/pdf}
}

@Article{	  manohar_quantitative_2023,
  title		= {Quantitative metrics of the {LV} trabeculated layer by
		  cardiac {CT} and cardiac {MRI} in patients with suspected
		  noncompaction cardiomyopathy},
  issn		= {1432-1084},
  url		= {https://link.springer.com/10.1007/s00330-023-10526-1},
  doi		= {10.1007/s00330-023-10526-1},
  abstract	= {Objectives  To compare cardiac computed tomography (CCT)
		  and cardiac magnetic resonance (CMR) for the
		  quanti‑tative assessment of the left ventricular (LV)
		  trabeculated layer in patients with suspected noncompaction
		  cardiomyo‑pathy (NCCM). Materials and methods  Subjects
		  with LV excessive trabeculation who underwent both CMR and
		  CCT imaging as part of the prospective international
		  multicenter NONCOMPACT clinical study were included. For
		  each subject, short-axis CCT and CMR slices were matched.
		  Four quantitative metrics were estimated: 1D
		  noncompacted-tocompacted ratio (NCC),
		  trabecular-to-myocardial area ratio (TMA),
		  trabecular-to-endocardial cavity area ratio (TCA), and
		  trabecular-to-myocardial volume ratio (TMV). In 20
		  subjects, end-diastolic and mid-diastolic CCT images were
		  compared for the quantification of the trabeculated layer.
		  Relationships between the metrics were investigated using
		  linear regression models and Bland-Altman analyses.
		  Results  Forty-eight subjects (49.9 ± 12.8 years; 28
		  female) were included in this study. NCC was moderately
		  corre‑lated (r = 0.62), TMA and TMV were strongly
		  correlated (r = 0.78 and 0.78), and TCA had excellent
		  correlation (r = 0.92) between CMR and CCT, with an
		  underestimation bias from CCT of 0.3 units, and 5.1, 4.8,
		  and 5.4 percent-points for the 4 metrics, respectively.
		  TMA, TCA, and TMV had excellent correlations (r = 0.93,
		  0.96, 0.94) and low biases (− 3.8, 0.8,  − 3.8
		  percent-points) between the end-diastolic and mid-diastolic
		  CCT images. Conclusions  TMA, TCA, and TMV metrics of the
		  LV trabeculated layer in patients with suspected NCCM
		  demonstrated high concordance between CCT and CMR images.
		  TMA and TCA were highly reproducible and demonstrated
		  minimal differences between mid-diastolic and end-diastolic
		  CCT images.},
  language	= {en},
  urldate	= {2024-01-15},
  journal	= {European Radiology},
  author	= {Manohar, Ashish and Vigneault, Davis M. and Kwon, Deborah
		  H. and Caliskan, Kadir and Budde, Ricardo P. J. and Hirsch,
		  Alexander and Lee, Seung-Pyo and Lee, Whal and Owens,
		  Anjali and Litt, Harold and Haddad, Francois and
		  Mistelbauer, Gabriel and Wheeler, Matthew and Rubin, Daniel
		  and Tang, W. H. Wilson and Nieman, Koen},
  month		= dec,
  year		= {2023},
  file		= {Manohar et al. - 2023 - Quantitative metrics of the LV
		  trabeculated layer
		  .pdf:/Users/davisvigneault/Zotero/storage/INPDFQ3Y/Manohar
		  et al. - 2023 - Quantitative metrics of the LV trabeculated
		  layer .pdf:application/pdf}
}

@Misc{		  cao_swin-unet_2021,
  title		= {Swin-{Unet}: {Unet}-like {Pure} {Transformer} for
		  {Medical} {Image} {Segmentation}},
  shorttitle	= {Swin-{Unet}},
  url		= {http://arxiv.org/abs/2105.05537},
  abstract	= {In the past few years, convolutional neural networks
		  (CNNs) have achieved milestones in medical image analysis.
		  Especially, the deep neural networks based on U-shaped
		  architecture and skip-connections have been widely applied
		  in a variety of medical image tasks. However, although CNN
		  has achieved excellent performance, it cannot learn global
		  and long-range semantic information interaction well due to
		  the locality of convolution operation. In this paper, we
		  propose Swin-Unet, which is a Unet-like pure Transformer
		  for medical image segmentation. The tokenized image patches
		  are fed into the Transformer-based Ushaped Encoder-Decoder
		  architecture with skip-connections for localglobal semantic
		  feature learning. Speciﬁcally, we use hierarchical Swin
		  Transformer with shifted windows as the encoder to extract
		  context features. And a symmetric Swin Transformer-based
		  decoder with patch expanding layer is designed to perform
		  the up-sampling operation to restore the spatial resolution
		  of the feature maps. Under the direct downsampling and
		  up-sampling of the inputs and outputs by 4×, experiments
		  on multi-organ and cardiac segmentation tasks demonstrate
		  that the pure Transformer-based U-shaped Encoder-Decoder
		  network outperforms those methods with full-convolution or
		  the combination of transformer and convolution. The codes
		  and trained models will be publicly available at
		  https://github.com/HuCaoFighting/Swin-Unet.},
  language	= {en},
  urldate	= {2024-02-01},
  publisher	= {arXiv},
  author	= {Cao, Hu and Wang, Yueyue and Chen, Joy and Jiang,
		  Dongsheng and Zhang, Xiaopeng and Tian, Qi and Wang,
		  Manning},
  month		= may,
  year		= {2021},
  note		= {arXiv:2105.05537 [cs, eess]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition, Electrical Engineering and Systems Science -
		  Image and Video Processing},
  file		= {Cao et al. - 2021 - Swin-Unet Unet-like Pure Transformer
		  for Medical
		  .pdf:/Users/davisvigneault/Zotero/storage/LVMIMRVV/Cao et
		  al. - 2021 - Swin-Unet Unet-like Pure Transformer for
		  Medical .pdf:application/pdf}
}

@Misc{		  wu_group_2018,
  title		= {Group {Normalization}},
  url		= {http://arxiv.org/abs/1803.08494},
  abstract	= {Batch Normalization (BN) is a milestone technique in the
		  development of deep learning, enabling various networks to
		  train. However, normalizing along the batch dimension
		  introduces problems — BN’s error increases rapidly when
		  the batch size becomes smaller, caused by inaccurate batch
		  statistics estimation. This limits BN’s usage for
		  training larger models and transferring features to
		  computer vision tasks including detection, segmentation,
		  and video, which require small batches constrained by
		  memory consumption. In this paper, we present Group
		  Normalization (GN) as a simple alternative to BN. GN
		  divides the channels into groups and computes within each
		  group the mean and variance for normalization. GN’s
		  computation is independent of batch sizes, and its accuracy
		  is stable in a wide range of batch sizes. On ResNet-50
		  trained in ImageNet, GN has 10.6\% lower error than its BN
		  counterpart when using a batch size of 2; when using
		  typical batch sizes, GN is comparably good with BN and
		  outperforms other normalization variants. Moreover, GN can
		  be naturally transferred from pre-training to ﬁne-tuning.
		  GN can outperform its BNbased counterparts for object
		  detection and segmentation in COCO,1 and for video
		  classiﬁcation in Kinetics, showing that GN can
		  effectively replace the powerful BN in a variety of tasks.
		  GN can be easily implemented by a few lines of code in
		  modern libraries.},
  language	= {en},
  urldate	= {2024-02-01},
  publisher	= {arXiv},
  author	= {Wu, Yuxin and He, Kaiming},
  month		= jun,
  year		= {2018},
  note		= {arXiv:1803.08494 [cs]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition, Computer Science - Machine Learning},
  file		= {Wu and He - 2018 - Group
		  Normalization.pdf:/Users/davisvigneault/Zotero/storage/XW6JK5DL/Wu
		  and He - 2018 - Group Normalization.pdf:application/pdf}
}

@Misc{		  oktay_attention_2018,
  title		= {Attention {U}-{Net}: {Learning} {Where} to {Look} for the
		  {Pancreas}},
  shorttitle	= {Attention {U}-{Net}},
  url		= {http://arxiv.org/abs/1804.03999},
  doi		= {10.48550/arXiv.1804.03999},
  abstract	= {We propose a novel attention gate (AG) model for medical
		  imaging that automatically learns to focus on target
		  structures of varying shapes and sizes. Models trained with
		  AGs implicitly learn to suppress irrelevant regions in an
		  input image while highlighting salient features useful for
		  a speciﬁc task. This enables us to eliminate the
		  necessity of using explicit external tissue/organ
		  localisation modules of cascaded convolutional neural
		  networks (CNNs). AGs can be easily integrated into standard
		  CNN architectures such as the U-Net model with minimal
		  computational overhead while increasing the model
		  sensitivity and prediction accuracy. The proposed Attention
		  U-Net architecture is evaluated on two large CT abdominal
		  datasets for multi-class image segmentation. Experimental
		  results show that AGs consistently improve the prediction
		  performance of U-Net across different datasets and training
		  sizes while preserving computational efﬁciency. The
		  source code for the proposed architecture is publicly
		  available.},
  language	= {en},
  urldate	= {2024-12-10},
  publisher	= {arXiv},
  author	= {Oktay, Ozan and Schlemper, Jo and Folgoc, Loic Le and Lee,
		  Matthew and Heinrich, Mattias and Misawa, Kazunari and
		  Mori, Kensaku and McDonagh, Steven and Hammerla, Nils Y.
		  and Kainz, Bernhard and Glocker, Ben and Rueckert, Daniel},
  month		= may,
  year		= {2018},
  note		= {arXiv:1804.03999 [cs]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition},
  file		= {Oktay et al. - 2018 - Attention U-Net Learning Where to
		  Look for the
		  Pa.pdf:/Users/davisvigneault/Zotero/storage/QH3L796I/Oktay
		  et al. - 2018 - Attention U-Net Learning Where to Look for
		  the Pa.pdf:application/pdf}
}

@Article{	  reeves_cardiac_2021,
  title		= {Cardiac {Imaging} {Trends} from 2010 to 2019 in the
		  {Medicare} {Population}},
  volume	= {3},
  issn		= {2638-6135},
  url		= {http://pubs.rsna.org/doi/10.1148/ryct.2021210156},
  doi		= {10.1148/ryct.2021210156},
  abstract	= {Purpose:  To evaluate changes in utilization of cardiac
		  imaging—transthoracic, transesophageal, and stress
		  echocardiography (TTE, TEE, and SE), coronary CT
		  angiography (cCTA), cardiac MRI (cMRI), myocardial
		  perfusion imaging (MPI), and cardiac positron emission
		  tomography (cPET). Materials and Methods:  The
		  2010–2019 Physician/Supplier Procedure Summary files were
		  used to find imaging utilization per 100 000 Medicare
		  beneficiaries. Global and professional claims were
		  aggregated, representing total interpretive services.
		  Specialty codes identified provider specialty. Results were
		  stratified by physician offices, hospital outpatient
		  departments (HOPDs), inpatient setting, and the emergency
		  department. Results:  From 2010 to 2019, there was a
		  partial shift from cardiologist offices to the HOPD for TTE
		  (office: −23\%; HOPD: 1107\%) and SE (office: −44\%;
		  HOPD: 111\%). Cardiologist cCTA also shifted from the
		  office (−57\%) to the HOPD (1211\%). Radiologistperformed
		  cCTA grew in all locations but most in the HOPD (1355\%),
		  with radiologists performing more cCTA than cardiologists
		  in all settings. cMRI rates remain low but rose in the HOPD
		  for both cardiologists (1209\%) and radiologists (1207\%).
		  Cardiologist MPI rates dropped dramatically in the office
		  (−52\%), with a smaller absolute rate increase in the
		  HOPD (171\%). cPET nearly tripled in the cardiology office
		  (1193\%), but rates remained steady for radiologists.
		  Conclusion:  While most cardiologist in-office imaging
		  has shifted to the HOPD, there has been an increase in
		  in-office cPET, likely due to a combination of
		  technological advances, interpretation familiarity, and
		  financial incentives. Radiologist cCTA rates continue to
		  increase, representing a growing opportunity for
		  radiologists to collaborate in cardiac imaging.},
  language	= {en},
  number	= {5},
  urldate	= {2025-01-12},
  journal	= {Radiology: Cardiothoracic Imaging},
  author	= {Reeves, Russell A. and Halpern, Ethan J. and Rao, Vijay
		  M.},
  month		= oct,
  year		= {2021},
  pages		= {e210156},
  file		= {Reeves et al. - 2021 - Cardiac Imaging Trends from 2010 to
		  2019 in the
		  Me.pdf:/Users/davisvigneault/Zotero/storage/S4Z3SUAR/Reeves
		  et al. - 2021 - Cardiac Imaging Trends from 2010 to 2019 in
		  the Me.pdf:application/pdf}
}

@Misc{		  maxwell_coronary_2024,
  title		= {Coronary {CTA} {Reimbursement} for {US} {Hospitals} to
		  {Double} in 2025},
  url		= {https://www.tctmd.com/news/coronary-cta-reimbursement-us-hospitals-double-2025},
  urldate	= {2025-01-11},
  journal	= {TCTMD},
  author	= {Maxwell, Yael},
  month		= nov,
  year		= {2024}
}

@Article{	  li_8-layer_2021,
  title		= {An 8-layer residual {U}-{Net} with deep supervision for
		  segmentation of the left ventricle in cardiac {CT}
		  angiography},
  volume	= {200},
  issn		= {01692607},
  url		= {https://linkinghub.elsevier.com/retrieve/pii/S0169260720317090},
  doi		= {10.1016/j.cmpb.2020.105876},
  abstract	= {Background and Objectives: Accurate segmentation of left
		  ventricle (LV) is a fundamental step in evaluation of
		  cardiac function. Cardiac CT angiography (CCTA) has become
		  an important clinical diagnostic method for cardio-vascular
		  disease (CVD) due to its non-invasive, short exam time, and
		  low cost. To obtain the segmentation of the LV in CCTA
		  scans, we present a deep learning method based on an
		  8-layer residual U-Net with deep supervision. Methods:
		  Based on the original 4-layer U-Net, our method deepened
		  the network to eight layers, which increased the ﬁtting
		  capacity of the network, thus greatly improved its LV
		  recognition capability. Residual blocks were incorporated
		  to optimize the network from the increased depth. Auxiliary
		  paths as deep supervision were introduced to supervise the
		  intermediate information to improve the segmentation
		  quality. In this study, we collected CCTA scans of 100
		  patients. Eighty patients with 1600 discrete slices were
		  used to train the LV segmentation and the remaining 20
		  patients with 400 discrete slices were used for testing our
		  method. An interactive graph cut algorithm was utilized
		  reliably to annotate the LV reference standard that was
		  further conﬁrmed by cardiologists. Online data
		  augmentation was performed in the training process to
		  improve the generalization and robustness of our method.
		  Results: Compared with the segmentation results from the
		  original U-Net and FC-DenseNet56 with Dice similarity
		  coeﬃcient (DSC) of 0.878±0.230 and 0.897±0.189,
		  respectively, our method demonstrated higher segmentation
		  accuracy and robustness for varying LV shape, size, and
		  contrast, achieving DSC of 0.927±0.139. Without online
		  data augmentation, our method resulted in inferior
		  performance with DSC of 0.911±0.170. In addition, compared
		  with the provided results from other existing studies in
		  the LV segmentation of cardiac CT images, our method
		  achieved a competitive performance for the LV segmentation.
		  Conclusions: The proposed 8-layer residual U-Net with deep
		  supervision accurately and eﬃciently segments the LV in
		  CCTA scans. This method has potential advantages to be a
		  reliable segmentation method and useful for the evaluation
		  of cardiac function in the future study.},
  language	= {en},
  urldate	= {2025-01-12},
  journal	= {Computer Methods and Programs in Biomedicine},
  author	= {Li, Changling and Song, Xiangfen and Zhao, Hang and Feng,
		  Li and Hu, Tao and Zhang, Yuchen and Jiang, Jun and Wang,
		  Jianan and Xiang, Jianping and Sun, Yong},
  month		= mar,
  year		= {2021},
  pages		= {105876},
  file		= {Li et al. - 2021 - An 8-layer residual U-Net with deep
		  supervision
		  fo.pdf:/Users/davisvigneault/Zotero/storage/YNUFVR9Y/Li et
		  al. - 2021 - An 8-layer residual U-Net with deep
		  supervision fo.pdf:application/pdf}
}

@Article{	  wang_auto_2022,
  title		= {The auto segmentation for cardiac structures using a
		  dual‐input deep learning network based on vision saliency
		  and transformer},
  volume	= {23},
  issn		= {1526-9914, 1526-9914},
  url		= {https://aapm.onlinelibrary.wiley.com/doi/10.1002/acm2.13597},
  doi		= {10.1002/acm2.13597},
  abstract	= {Purpose: Accurate segmentation of cardiac structures on
		  coronary CT angiography (CCTA) images is crucial for the
		  morphological analysis, measurement, and functional
		  evaluation. In this study, we achieve accurate automatic
		  segmentation of cardiac structures on CCTA image by
		  adopting an innovative deep learning method based on visual
		  attention mechanism and transformer network, and its
		  practical application value is discussed. Methods: We
		  developed a dual-input deep learning network based on
		  visual saliency and transformer (VST), which consists of
		  self -attention mechanism for cardiac structures
		  segmentation. Sixty patients’ CCTA subjects were randomly
		  selected as a development set, which were manual marked by
		  an experienced technician. The proposed vision attention
		  and transformer mode was trained on the patients CCTA
		  images, with a manual contour-derived binary mask used as
		  the learning-based target.We also used the deep supervision
		  strategy by adding auxiliary losses. The loss function of
		  our model was the sum of the Dice loss and cross-entropy
		  loss. To quantitatively evaluate the segmentation results,
		  we calculated the Dice similarity coefﬁcient (DSC) and
		  Hausdorff distance (HD). Meanwhile, we compare the volume
		  of automatic segmentation and manual segmentation to
		  analyze whether there is statistical difference. Results:
		  Fivefold cross-validation was used to benchmark the
		  segmentation method. The results showed the left
		  ventricular myocardium (LVM, DSC = 0.87), the left
		  ventricular (LV, DSC = 0.94), the left atrial (LA, DSC =
		  0.90), the right ventricular (RV,DSC = 0.92),the right
		  atrial (RA,DSC = 0.91),and the aortic (AO, DSC = 0.96). The
		  average DSC was 0.92, and HD was 7.2 ± 2.1 mm. In volume
		  comparison, except LVM and LA (p {\textless} 0.05), there
		  was no signiﬁcant statistical difference in other
		  structures. Proposed method for structural segmentation
		  ﬁt well with the true proﬁle of the cardiac
		  substructure, and the model prediction results closed to
		  the manual annotation. Conclusions: The adoption of the
		  dual-input and transformer architecture based on visual
		  saliency has high sensitivity and speciﬁcity to cardiac
		  structures segmentation, which can obviously improve the
		  accuracy of automatic substructure segmentation.},
  language	= {en},
  number	= {5},
  urldate	= {2025-01-12},
  journal	= {Journal of Applied Clinical Medical Physics},
  author	= {Wang, Jing and Wang, Shuyu and Liang, Wei and Zhang, Nan
		  and Zhang, Yan},
  month		= may,
  year		= {2022},
  pages		= {e13597},
  file		= {Wang et al. - 2022 - The auto segmentation for cardiac
		  structures
		  using.pdf:/Users/davisvigneault/Zotero/storage/PHRVQYMI/Wang
		  et al. - 2022 - The auto segmentation for cardiac
		  structures using.pdf:application/pdf}
}

@Article{	  jun_guo_automated_2020,
  title		= {Automated left ventricular myocardium segmentation using
		  {3D} deeply supervised attention {U}‐net for coronary
		  computed tomography angiography; {CT} myocardium
		  segmentation},
  volume	= {47},
  issn		= {0094-2405, 2473-4209},
  url		= {https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.14066},
  doi		= {10.1002/mp.14066},
  abstract	= {Purpose: Segmentation of left ventricular myocardium (LVM)
		  in coronary computed tomography angiography (CCTA) is
		  important for diagnosis of cardiovascular diseases. Due to
		  poor image contrast and large variation in intensity and
		  shapes, LVM segmentation for CCTA is a challenging task.
		  The purpose of this work is to develop a region-based deep
		  learning method to automatically detect and segment the LVM
		  solely based on CCTA images. Methods: We developed a 3D
		  deeply supervised U-Net, which incorporates attention gates
		  (AGs) to focus on the myocardial boundary structures, to
		  segment LVM contours from CCTA. The deep attention U-Net
		  (DAU-Net) was trained on the patients’ CCTA images, with
		  a manual contour-derived binary mask used as the
		  learning-based target. The network was supervised by a
		  hybrid loss function, which combined logistic loss and Dice
		  loss to simultaneously measure the similarities and
		  discrepancies between the prediction and training datasets.
		  To evaluate the accuracy of the segmentation, we
		  retrospectively investigated 100 patients with suspected or
		  confirmed coronary artery disease (CAD). The LVM volume was
		  segmented by the proposed method and compared with
		  physician-approved clinical contours. Quantitative metrics
		  used were Dice similarity coefficient (DSC), Hausdorff
		  distance (HD), mean surface distance (MSD), residual mean
		  square distance (RMSD), the center of mass distance (CMD),
		  and volume difference (VOD). Results: The proposed method
		  created contours with very good agreement to the ground
		  truth contours. Our proposed segmentation approach is
		  benchmarked primarily using fivefold cross validation.
		  Model prediction correlated and agreed well with manual
		  contour. The mean DSC of the contours delineated by our
		  method was 91.6\% among all patients. The resultant HD was
		  6.840 Æ 4.410 mm. The proposed method also resulted in a
		  small CMD (1.058 Æ 1.245 mm) and VOD (1.640 Æ 1.777 cc).
		  Among all patients, the MSD and RMSD were 0.433 Æ 0.209 mm
		  and 0.724 Æ 0.375 mm, respectively, between ground truth
		  and LVM volume resulting from the proposed method.
		  Conclusions: We developed a novel deep learning-based
		  approach for the automated segmentation of the LVM on CCTA
		  images. We demonstrated the high accuracy of the proposed
		  learning-based segmentation method through comparison with
		  ground truth contour of 100 clinical patient cases using
		  six quantitative metrics. These results show the potential
		  of using automated LVM segmentation for computer-aided
		  delineation of CADs in the clinical setting. © 2020
		  American Association of Physicists in Medicine
		  [https://doi.org/10.1002/mp.14066]},
  language	= {en},
  number	= {4},
  urldate	= {2025-01-12},
  journal	= {Medical Physics},
  author	= {Jun Guo, Bang and He, Xiuxiu and Lei, Yang and Harms,
		  Joseph and Wang, Tonghe and Curran, Walter J. and Liu, Tian
		  and Jiang Zhang, Long and Yang, Xiaofeng},
  month		= apr,
  year		= {2020},
  pages		= {1775--1785},
  file		= {Jun Guo et al. - 2020 - Automated left ventricular
		  myocardium
		  segmentation.pdf:/Users/davisvigneault/Zotero/storage/889YKC3R/Jun
		  Guo et al. - 2020 - Automated left ventricular myocardium
		  segmentation.pdf:application/pdf}
}

@Article{	  zhou_unet_2020,
  title		= {{UNet}++: {Redesigning} {Skip} {Connections} to {Exploit}
		  {Multiscale} {Features} in {Image} {Segmentation}},
  volume	= {39},
  copyright	= {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
  issn		= {0278-0062, 1558-254X},
  shorttitle	= {{UNet}++},
  url		= {https://ieeexplore.ieee.org/document/8932614/},
  doi		= {10.1109/TMI.2019.2959609},
  abstract	= {The state-of-the-art models for medical image segmentation
		  are variants of U-Net and fully convolutional networks
		  (FCN). Despite their success, these models have two
		  limitations: (1) their optimal depth is apriori unknown,
		  requiring extensive architecture search or inefﬁcient
		  ensemble of models of varying depths; and (2) their skip
		  connections impose an unnecessarily restrictive fusion
		  scheme, forcing aggregation only at the same-scale feature
		  maps of the encoder and decoder sub-networks. To overcome
		  these two limitations, we propose UNet++, a new neural
		  architecture for semantic and instance segmentation, by (1)
		  alleviating the unknown network depth with an efﬁcient
		  ensemble of U-Nets of varying depths, which partially share
		  an encoder and co-learn simultaneously using deep
		  supervision; (2) redesigning skip connections to aggregate
		  features of varying semantic scales at the decoder
		  sub-networks, leading to a highly ﬂexible feature fusion
		  scheme; and (3) devising a pruning scheme to accelerate the
		  inference speed of UNet++. We have evaluated UNet++ using
		  six different medical image segmentation datasets, covering
		  multiple imaging modalities such as computed tomography
		  (CT), magnetic resonance imaging (MRI), and electron
		  microscopy (EM), and demonstrating that (1) UNet++
		  consistently outperforms the baseline models for the task
		  of semantic segmentation across different datasets and
		  backbone architectures; (2) UNet++ enhances segmentation
		  quality of varying-size objects—an improvement over the
		  ﬁxed-depth U-Net; (3) Mask RCNN++ (Mask R-CNN with UNet++
		  design) outperforms the original Mask R-CNN for the task of
		  instance segmentation; and (4) pruned UNet++ models achieve
		  signiﬁcant speedup while showing only modest performance
		  degradation. Our implementation and pre-trained models are
		  available at https://github.com/MrGiovanni/UNetPlusPlus.},
  language	= {en},
  number	= {6},
  urldate	= {2025-01-19},
  journal	= {IEEE Transactions on Medical Imaging},
  author	= {Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and
		  Tajbakhsh, Nima and Liang, Jianming},
  month		= jun,
  year		= {2020},
  pages		= {1856--1867},
  file		= {Zhou et al. - 2020 - UNet++ Redesigning Skip Connections
		  to Exploit
		  Mu.pdf:/Users/davisvigneault/Zotero/storage/RGBTHNPH/Zhou
		  et al. - 2020 - UNet++ Redesigning Skip Connections to
		  Exploit Mu.pdf:application/pdf}
}

@Article{	  alom_recurrent_2019,
  title		= {Recurrent residual {U}-{Net} for medical image
		  segmentation},
  volume	= {6},
  issn		= {2329-4302},
  url		= {https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-6/issue-01/014006/Recurrent-residual-U-Net-for-medical-image-segmentation/10.1117/1.JMI.6.1.014006.full},
  doi		= {10.1117/1.JMI.6.1.014006},
  language	= {en},
  number	= {01},
  urldate	= {2025-01-19},
  journal	= {Journal of Medical Imaging},
  author	= {Alom, Md Zahangir and Yakopcic, Chris and Hasan, Mahmudul
		  and Taha, Tarek M. and Asari, Vijayan K.},
  month		= mar,
  year		= {2019},
  pages		= {1},
  file		= {Alom et al. - 2019 - Recurrent residual U-Net for medical
		  image
		  segment.pdf:/Users/davisvigneault/Zotero/storage/BYM3TCZC/Alom
		  et al. - 2019 - Recurrent residual U-Net for medical image
		  segment.pdf:application/pdf}
}

@InProceedings{	  huang_densely_2017,
  address	= {Honolulu, HI},
  title		= {Densely {Connected} {Convolutional} {Networks}},
  isbn		= {978-1-5386-0457-1},
  url		= {https://ieeexplore.ieee.org/document/8099726/},
  doi		= {10.1109/CVPR.2017.243},
  language	= {en},
  urldate	= {2025-01-19},
  booktitle	= {2017 {IEEE} {Conference} on {Computer} {Vision} and
		  {Pattern} {Recognition} ({CVPR})},
  publisher	= {IEEE},
  author	= {Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and
		  Weinberger, Kilian Q.},
  month		= jul,
  year		= {2017},
  pages		= {2261--2269},
  file		= {Huang et al. - 2017 - Densely Connected Convolutional
		  Networks.pdf:/Users/davisvigneault/Zotero/storage/ZCGZSQSR/Huang
		  et al. - 2017 - Densely Connected Convolutional
		  Networks.pdf:application/pdf}
}

@InProceedings{	  jegou_one_2017,
  address	= {Honolulu, HI, USA},
  title		= {The {One} {Hundred} {Layers} {Tiramisu}: {Fully}
		  {Convolutional} {DenseNets} for {Semantic} {Segmentation}},
  isbn		= {978-1-5386-0733-6},
  shorttitle	= {The {One} {Hundred} {Layers} {Tiramisu}},
  url		= {http://ieeexplore.ieee.org/document/8014890/},
  doi		= {10.1109/CVPRW.2017.156},
  abstract	= {State-of-the-art approaches for semantic image
		  segmentation are built on Convolutional Neural Networks
		  (CNNs). The typical segmentation architecture is composed
		  of (a) a downsampling path responsible for extracting
		  coarse semantic features, followed by (b) an upsampling
		  path trained to recover the input image resolution at the
		  output of the model and, optionally, (c) a post-processing
		  module (e.g. Conditional Random Fields) to reﬁne the
		  model predictions. Recently, a new CNN architecture,
		  Densely Connected Convolutional Networks (DenseNets), has
		  shown excellent results on image classiﬁcation tasks. The
		  idea of DenseNets is based on the observation that if each
		  layer is directly connected to every other layer in a
		  feed-forward fashion then the network will be more accurate
		  and easier to train.},
  language	= {en},
  urldate	= {2025-01-19},
  booktitle	= {2017 {IEEE} {Conference} on {Computer} {Vision} and
		  {Pattern} {Recognition} {Workshops} ({CVPRW})},
  publisher	= {IEEE},
  author	= {Jegou, Simon and Drozdzal, Michal and Vazquez, David and
		  Romero, Adriana and Bengio, Yoshua},
  month		= jul,
  year		= {2017},
  pages		= {1175--1183},
  file		= {Jegou et al. - 2017 - The One Hundred Layers Tiramisu
		  Fully
		  Convolution.pdf:/Users/davisvigneault/Zotero/storage/IBILBT5M/Jegou
		  et al. - 2017 - The One Hundred Layers Tiramisu Fully
		  Convolution.pdf:application/pdf}
}

@Article{	  wu_inner_2023,
  title		= {Inner {Cascaded} {U2}-{Net}: {An} {Improvement} to {Plain}
		  {Cascaded} {U}-{Net}},
  volume	= {134},
  issn		= {1526-1506},
  shorttitle	= {Inner {Cascaded} {U2}-{Net}},
  url		= {https://www.techscience.com/CMES/v134n2/49520},
  doi		= {10.32604/cmes.2022.020428},
  abstract	= {Deep neural networks are now widely used in the medical
		  image segmentation field for their performance superiority
		  and no need of manual feature extraction. U-Net has been
		  the baseline model since the very beginning due to a
		  symmetrical U-structure for better feature extraction and
		  fusing and suitable for small datasets. To enhance the
		  segmentation performance of U-Net, cascaded U-Net proposes
		  to put two U-Nets successively to segment targets from
		  coarse to fine. However, the plain cascaded U-Net faces the
		  problem of too less between connections so the contextual
		  information learned by the former U-Net cannot be fully
		  used by the latter one. In this article, we devise novel
		  Inner Cascaded U-Net and Inner Cascaded U2-Net as
		  improvements to plain cascaded U-Net for medical image
		  segmentation. The proposed Inner Cascaded U-Net adds inner
		  nested connections between two U-Nets to share more
		  contextual information. To further boost segmentation
		  performance, we propose Inner Cascaded U2-Net, which
		  applies residual U-block to capture more global contextual
		  information from different scales. The proposed models can
		  be trained from scratch in an end-to-end fashion and have
		  been evaluated on Multimodal Brain Tumor Segmentation
		  Challenge (BraTS) 2013 and ISBI Liver Tumor Segmentation
		  Challenge (LiTS) dataset in comparison to related U-Net,
		  cascaded U-Net, U-Net++, U2-Net and state-of-the-art
		  methods. Our experiments demonstrate that our proposed
		  Inner Cascaded U-Net and Inner Cascaded U2-Net achieve
		  better segmentation performance in terms of dice similarity
		  coefficient and hausdorff distance as well as get finer
		  outline segmentation.},
  language	= {en},
  number	= {2},
  urldate	= {2025-01-19},
  journal	= {Computer Modeling in Engineering \& Sciences},
  author	= {Wu, Wenbin and Liu, Guanjun and Liang, Kaiyi and Zhou,
		  Hui},
  year		= {2023},
  pages		= {1323--1335},
  file		= {Wu et al. - 2023 - Inner Cascaded U2-Net An Improvement to
		  Plain
		  Cas.pdf:/Users/davisvigneault/Zotero/storage/F9X4YVAW/Wu et
		  al. - 2023 - Inner Cascaded U2-Net An Improvement to Plain
		  Cas.pdf:application/pdf}
}

@Article{	  liu_cascaded_2021,
  title		= {Cascaded atrous dual attention {U}-{Net} for tumor
		  segmentation},
  volume	= {80},
  issn		= {1380-7501, 1573-7721},
  url		= {https://link.springer.com/10.1007/s11042-020-10078-2},
  doi		= {10.1007/s11042-020-10078-2},
  abstract	= {Automatic segmentation of the organ’s tumor and lesion
		  on biomedical imaging is an essential initiative towards
		  clinical study, treatment planning and digital biomedical
		  research. However, precise tumor segmentation on medical
		  imaging is still an open challenge due to the presence of
		  noise in the imaging sequence, the similar tumor pixel
		  intensity with its neighboring tissues, and heterogeneity
		  among human anatomy. Although most of the stateof-the-art
		  algorithms are architecturally dependent on deep
		  convolution networks (DCNs), like 2D and 3D U-Net, they act
		  as a foundation for many biomedical image segmentation.
		  However, 2D DCNs are incompetent to leverage context
		  information from inter-slice completely. At the same time,
		  3D DCNs can accumulate inter-slice contextual information
		  over the sizeable receptive texture in the organ, but it
		  consumes a considerable amount of GPU memory and burdens
		  with the high execution cost. In order to achieve a
		  promising solution, we proposed a segmentation network
		  called Cascaded Atrous Dual-Attention UNet. First, our
		  network structure concatenates features from 3D liver
		  segmentation to 2D tumor segmentation for preserving
		  volumetric information as well as enlarging resolution with
		  segmentation accuracy. Second, we embedded dual attention
		  gate in each skip connection layer of the 2D segmentation
		  model, which determines to concentrate on certain
		  discriminative features in order to find tumor segmentation
		  in different organs. Finally, we adopted atrous encoder
		  which extracts wider context features from computed
		  tomography as compared to normal encoder. Furthermore, we
		  tested the proposed method on four different datasets,
		  including liver tumor segmentation benchmark (LiTS), MSD
		  liver, pancreas tumor segmentation and Kidney tumor
		  segmentation (KiTS). Experimental results were compared
		  with the other state-of-the-art segmentation methods. Our
		  proposed approach performs remarkably better than existing
		  methods with around 4 ∼ 6\% improvement on each
		  benchmark.},
  language	= {en},
  number	= {20},
  urldate	= {2025-01-19},
  journal	= {Multimedia Tools and Applications},
  author	= {Liu, Yu-Cheng and Shahid, Mohammad and Sarapugdi,
		  Wannaporn and Lin, Yong-Xiang and Chen, Jyh-Cheng and Hua,
		  Kai-Lung},
  month		= aug,
  year		= {2021},
  pages		= {30007--30031},
  file		= {Liu et al. - 2021 - Cascaded atrous dual attention U-Net
		  for tumor
		  seg.pdf:/Users/davisvigneault/Zotero/storage/ISUXAUDE/Liu
		  et al. - 2021 - Cascaded atrous dual attention U-Net for
		  tumor seg.pdf:application/pdf}
}

@Article{	  kong_deep-learning_2021,
  title		= {A deep-learning approach for direct whole-heart mesh
		  reconstruction},
  volume	= {74},
  issn		= {13618415},
  url		= {https://linkinghub.elsevier.com/retrieve/pii/S136184152100267X},
  doi		= {10.1016/j.media.2021.102222},
  language	= {en},
  urldate	= {2025-01-26},
  journal	= {Medical Image Analysis},
  author	= {Kong, Fanwei and Wilson, Nathan and Shadden, Shawn},
  month		= dec,
  year		= {2021},
  pages		= {102222},
  file		= {Kong et al. - 2021 - A deep-learning approach for direct
		  whole-heart
		  me.pdf:/Users/davisvigneault/Zotero/storage/WUNJFQN2/Kong
		  et al. - 2021 - A deep-learning approach for direct
		  whole-heart me.pdf:application/pdf}
}

@Article{	  peper_functional_2020,
  title		= {Functional cardiac {CT}–{Going} beyond {Anatomical}
		  {Evaluation} of {Coronary} {Artery} {Disease} with {Cine}
		  {CT}, {CT}-{FFR}, {CT} {Perfusion} and {Machine}
		  {Learning}},
  volume	= {93},
  copyright	= {https://academic.oup.com/journals/pages/open\_access/funder\_policies/chorus/standard\_publication\_model},
  issn		= {0007-1285, 1748-880X},
  url		= {https://academic.oup.com/bjr/article/doi/10.1259/bjr.20200349/7452065},
  doi		= {10.1259/bjr.20200349},
  abstract	= {The aim of this review is to provide an overview of
		  different functional cardiac CT techniques which can be
		  used to supplement assessment of the coronary arteries to
		  establish the significance of coronary artery stenoses. We
		  focus on cine-­CT, CT-­FFR, CT-­myocardial perfusion and
		  how developments in machine learning can supplement these
		  techniques.},
  language	= {en},
  number	= {1113},
  urldate	= {2025-01-30},
  journal	= {The British Journal of Radiology},
  author	= {Peper, Joyce and Suchá, Dominika and Swaans, Martin and
		  Leiner, Tim},
  month		= sep,
  year		= {2020},
  pages		= {20200349},
  file		= {Peper et al. - 2020 - Functional cardiac CT–Going beyond
		  Anatomical
		  Eval.pdf:/Users/davisvigneault/Zotero/storage/JQLUB9LL/Peper
		  et al. - 2020 - Functional cardiac CT–Going beyond
		  Anatomical Eval.pdf:application/pdf}
}

@Article{	  seneviratne_incremental_2010,
  title		= {Incremental {Diagnostic} {Value} of {Regional} {Left}
		  {Ventricular} {Function} {Over} {Coronary} {Assessment} by
		  {Cardiac} {Computed} {Tomography} for the {Detection} of
		  {Acute} {Coronary} {Syndrome} in {Patients} {With} {Acute}
		  {Chest} {Pain}: {From} the {ROMICAT} {Trial}},
  volume	= {3},
  issn		= {1941-9651, 1942-0080},
  shorttitle	= {Incremental {Diagnostic} {Value} of {Regional} {Left}
		  {Ventricular} {Function} {Over} {Coronary} {Assessment} by
		  {Cardiac} {Computed} {Tomography} for the {Detection} of
		  {Acute} {Coronary} {Syndrome} in {Patients} {With} {Acute}
		  {Chest} {Pain}},
  url		= {https://www.ahajournals.org/doi/10.1161/CIRCIMAGING.109.892638},
  doi		= {10.1161/CIRCIMAGING.109.892638},
  abstract	= {Background—The incremental value of regional left
		  ventricular function (LVF) over coronary assessment to
		  detect acute coronary syndrome (ACS) is uncertain. Methods
		  and Results—We analyzed 356 patients (mean age, 53Ϯ12
		  years; 62\% men) with acute chest pain and inconclusive
		  initial emergency department evaluation. Patients underwent
		  64-slice contrast-enhanced cardiac computed tomography
		  before hospital admission. Caregivers and patients remained
		  blinded to the results. Regional LVF and presence of
		  coronary atherosclerotic plaque and significant stenosis
		  (Ͼ50\%) were separately assessed by 2 independent readers.
		  Incremental value of regional LVF to predict ACS was
		  determined in the entire cohort and in subgroups of
		  patients with nonobstructive coronary artery disease,
		  inconclusive assessment for stenosis (defined as inability
		  to exclude significant stenosis due to calcium or motion),
		  and significant stenosis. During their index
		  hospitalization, 31 patients were ultimately diagnosed with
		  ACS (8 myocardial infarction, 22 unstable angina), of which
		  74\% (23 patients) had regional LV dysfunction. Adding
		  regional LVF resulted in a 10\% increase in sensitivity to
		  detect ACS by cardiac computed tomography (87\%; 95\%
		  confidence interval, 70\% to 96\%) and significantly
		  improved the overall accuracy (c-statistic: 0.88 versus
		  0.94 and 0.79 versus 0.88, for extent of plaque and
		  presence of stenosis, respectively; both PϽ0.03). The
		  diagnostic accuracy of regional LVF for detection of ACS
		  has 89\% sensitivity and 86\% specificity in patients with
		  significant stenosis (nϭ33) and 60\% sensitivity and 86\%
		  specificity in patients with inconclusive coronary computed
		  tomographic angiography (nϭ33). Conclusions—Regional LVF
		  assessment at rest improves diagnostic accuracy for ACS in
		  patients with acute chest pain, especially in those with
		  coronary artery disease and thus may be helpful to guide
		  further management in patients at intermediate risk for ACS.},
  language	= {en},
  number	= {4},
  urldate	= {2025-01-30},
  journal	= {Circulation: Cardiovascular Imaging},
  author	= {Seneviratne, Sujith K. and Truong, Quynh A. and Bamberg,
		  Fabian and Rogers, Ian S. and Shapiro, Michael D. and
		  Schlett, Christopher L. and Chae, Claudia U. and Cury,
		  Ricardo and Abbara, Suhny and Brady, Thomas J. and
		  Nagurney, John T. and Hoffmann, Udo},
  month		= jul,
  year		= {2010},
  pages		= {375--383},
  file		= {Seneviratne et al. - 2010 - Incremental Diagnostic Value
		  of Regional Left
		  Vent.pdf:/Users/davisvigneault/Zotero/storage/J4SCZVP2/Seneviratne
		  et al. - 2010 - Incremental Diagnostic Value of Regional
		  Left Vent.pdf:application/pdf}
}

@Article{	  chen_myocardial_2023,
  title		= {Myocardial {Regional} {Shortening} from {4D} {Cardiac}
		  {CT} {Angiography} for the {Detection} of {Left}
		  {Ventricular} {Segmental} {Wall} {Motion} {Abnormality}},
  volume	= {5},
  issn		= {2638-6135},
  url		= {http://pubs.rsna.org/doi/10.1148/ryct.220134},
  doi		= {10.1148/ryct.220134},
  abstract	= {Purpose:  To investigate whether endocardial regional
		  shortening computed from four-dimensional (4D) CT
		  angiography (RSCT) can be used as a decision classifier to
		  detect the presence of left ventricular (LV) wall motion
		  abnormalities (WMAs). Materials and Methods:  One hundred
		  electrocardiographically gated cardiac 4D CT studies (mean
		  age, 59 years ± 14 [SD]; 61 male patients) conducted
		  between April 2018 and December 2020 were retrospectively
		  evaluated. Three experts labeled LV wall motion in each of
		  the 16 American Heart Association (AHA) segments as normal
		  or abnormal; they also measured peak RSCT across one
		  heartbeat in each segment. The data set was split evenly
		  into training and validation groups. During training,
		  interchangeability of RSCT thresholding with experts to
		  detect WMA was assessed using the individual equivalence
		  index (γ), and an optimal threshold of the peak RSCT
		  (RSCT*) that achieved maximum agreement was identified.
		  RSCT* was then validated using the validation group, and
		  the effect of AHA segment–specific thresholds was
		  evaluated. Agreement was assessed using κ statistics.
		  Results:  The optimal threshold, RSCT* of -0.19, when
		  applied to all AHA segments, led to high agreement
		  (agreement rate = 92.17\%, κ = 0.82) and
		  interchangeability with experts (γ = -2.58\%). The same
		  RSCT* also achieved high agreement in the validation group
		  (agreement rate = 90.29\%, κ = 0.76, γ = -0.38\%). The
		  use of AHA segment–specific thresholds (range: 0.16 to
		  -0.23 across AHA segments) slightly improved agreement
		  (1.79\% increase). Conclusion:  RSCT thresholding was
		  interchangeable with expert visual analysis in detecting
		  segmental WMA from 4D CT and may be used as an objective
		  decision classifier.},
  language	= {en},
  number	= {2},
  urldate	= {2025-04-22},
  journal	= {Radiology: Cardiothoracic Imaging},
  author	= {Chen, Zhennong and Contijoch, Francisco and Kahn, Andrew
		  M. and Kligerman, Seth and Narayan, Hari K. and Manohar,
		  Ashish and McVeigh, Elliot},
  month		= apr,
  year		= {2023},
  pages		= {e220134},
  file		= {Chen et al. - 2023 - Myocardial Regional Shortening from
		  4D Cardiac CT
		  .pdf:/Users/davisvigneault/Zotero/storage/AVLAQYV7/Chen et
		  al. - 2023 - Myocardial Regional Shortening from 4D Cardiac
		  CT .pdf:application/pdf}
}
