@misc{sam,
      title={Segment Anything}, 
      author={Alexander Kirillov and Eric Mintun and Nikhila Ravi and Hanzi Mao and Chloe Rolland and Laura Gustafson and Tete Xiao and Spencer Whitehead and Alexander C. Berg and Wan-Yen Lo and Piotr Dollár and Ross Girshick},
      year={2023},
      eprint={2304.02643},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2304.02643}, 
}

@article{medsam_efficient_encoder,
  title = {An efficient segment anything model for the segmentation of medical images},
  volume = {14},
  ISSN = {2045-2322},
  url = {http://dx.doi.org/10.1038/s41598-024-70288-8},
  DOI = {10.1038/s41598-024-70288-8},
  number = {1},
  journal = {Scientific Reports},
  publisher = {Springer Science and Business Media LLC},
  author = {Dong,  Guanliang and Wang,  Zhangquan and Chen,  Yourong and Sun,  Yuliang and Song,  Hongbo and Liu,  Liyuan and Cui,  Haidong},
  year = {2024},
  month = aug 
}

@article{medsam_evaluation,
  title = {Segment anything model for medical images?},
  volume = {92},
  ISSN = {1361-8415},
  url = {http://dx.doi.org/10.1016/j.media.2023.103061},
  DOI = {10.1016/j.media.2023.103061},
  journal = {Medical Image Analysis},
  publisher = {Elsevier BV},
  author = {Huang,  Yuhao and Yang,  Xin and Liu,  Lian and Zhou,  Han and Chang,  Ao and Zhou,  Xinrui and Chen,  Rusi and Yu,  Junxuan and Chen,  Jiongquan and Chen,  Chaoyu and Liu,  Sijing and Chi,  Haozhe and Hu,  Xindi and Yue,  Kejuan and Li,  Lei and Grau,  Vicente and Fan,  Deng-Ping and Dong,  Fajin and Ni,  Dong},
  year = {2024},
  month = feb,
  pages = {103061}
}

@misc{medsam_registration_prompt,
      title={Segmentation by registration-enabled SAM prompt engineering using five reference images}, 
      author={Yaxi Chen and Aleksandra Ivanova and Shaheer U. Saeed and Rikin Hargunani and Jie Huang and Chaozong Liu and Yipeng Hu},
      year={2024},
      eprint={2407.17933},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2407.17933}, 
}

@INPROCEEDINGS{medsam_prompt_engineering,
  author={Ali, Hazrat and Bulbul, Mohammad Farhad and Shah, Zubair},
  booktitle={2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)}, 
  title={Prompt Engineering in Medical Image Segmentation: An Overview of the Paradigm Shift}, 
  year={2023},
  volume={},
  number={},
  pages={1-4},
  keywords={Image segmentation;Computer vision;Analytical models;Computational modeling;Natural language processing;Lesions;Artificial intelligence;Foundation AI;Prompt Engineering;Segmentation;Medical Imaging;Healthcare;Large Language Models;Medical Artificial Intelligence;Natural Language Processing},
  doi={10.1109/AIBThings58340.2023.10292475}}

@article{totalsegmentor,
  title = {TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images},
  volume = {5},
  ISSN = {2638-6100},
  url = {http://dx.doi.org/10.1148/ryai.230024},
  DOI = {10.1148/ryai.230024},
  number = {5},
  journal = {Radiology: Artificial Intelligence},
  publisher = {Radiological Society of North America (RSNA)},
  author = {Wasserthal,  Jakob and Breit,  Hanns-Christian and Meyer,  Manfred T. and Pradella,  Maurice and Hinck,  Daniel and Sauter,  Alexander W. and Heye,  Tobias and Boll,  Daniel T. and Cyriac,  Joshy and Yang,  Shan and Bach,  Michael and Segeroth,  Martin},
  year = {2023},
  month = sep 
}

@misc{vfa,
      title={Vector Field Attention for Deformable Image Registration}, 
      author={Yihao Liu and Junyu Chen and Lianrui Zuo and Aaron Carass and Jerry L. Prince},
      year={2024},
      eprint={2407.10209},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2407.10209}, 
}

@misc{deepsdf,
      title={DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation}, 
      author={Jeong Joon Park and Peter Florence and Julian Straub and Richard Newcombe and Steven Lovegrove},
      year={2019},
      eprint={1901.05103},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1901.05103}, 
}

@article{voxelmorph,
   title={VoxelMorph: A Learning Framework for Deformable Medical Image Registration},
   volume={38},
   ISSN={1558-254X},
   url={http://dx.doi.org/10.1109/TMI.2019.2897538},
   DOI={10.1109/tmi.2019.2897538},
   number={8},
   journal={IEEE Transactions on Medical Imaging},
   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
   author={Balakrishnan, Guha and Zhao, Amy and Sabuncu, Mert R. and Guttag, John and Dalca, Adrian V.},
   year={2019},
   month=aug, pages={1788–1800} }

@misc{hyperchamfer,
      title={Hyperbolic Chamfer Distance for Point Cloud Completion and Beyond}, 
      author={Fangzhou Lin and Songlin Hou and Haotian Liu and Shang Gao and Kazunori D Yamada and Haichong K. Zhang and Ziming Zhang},
      year={2024},
      eprint={2412.17951},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.17951}, 
}

@misc{mambamorph,
      title={MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration}, 
      author={Tao Guo and Yinuo Wang and Shihao Shu and Diansheng Chen and Zhouping Tang and Cai Meng and Xiangzhi Bai},
      year={2024},
      eprint={2401.13934},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2401.13934}, 
}

@article{transmorph,
   title={TransMorph: Transformer for unsupervised medical image registration},
   volume={82},
   ISSN={1361-8415},
   url={http://dx.doi.org/10.1016/j.media.2022.102615},
   DOI={10.1016/j.media.2022.102615},
   journal={Medical Image Analysis},
   publisher={Elsevier BV},
   author={Chen, Junyu and Frey, Eric C. and He, Yufan and Segars, William P. and Li, Ye and Du, Yong},
   year={2022},
   month=nov, pages={102615} }

@article{nnunet,
  title = {nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
  volume = {18},
  ISSN = {1548-7105},
  url = {http://dx.doi.org/10.1038/s41592-020-01008-z},
  DOI = {10.1038/s41592-020-01008-z},
  number = {2},
  journal = {Nature Methods},
  publisher = {Springer Science and Business Media LLC},
  author = {Isensee,  Fabian and Jaeger,  Paul F. and Kohl,  Simon A. A. and Petersen,  Jens and Maier-Hein,  Klaus H.},
  year = {2020},
  month = dec,
  pages = {203–211}
}

@inproceedings{kcl,
   title={Label-driven weakly-supervised learning for multimodal deformarle image registration},
   url={http://dx.doi.org/10.1109/ISBI.2018.8363756},
   DOI={10.1109/isbi.2018.8363756},
   booktitle={2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)},
   publisher={IEEE},
   author={Hu, Yipeng and Modat, Marc and Gibson, Eli and Ghavami, Nooshin and Bonmati, Ester and Moore, Caroline M. and Emberton, Mark and Noble, J. Alison and Barratt, Dean C. and Vercauteren, Tom},
   year={2018},
   month=apr }



@article{quickfps,
  title={QuickFPS: Architecture and Algorithm Co-Design for Farthest Point Sampling in Large-Scale Point Clouds},
  author={Han, Meng and Wang, Liang and Xiao, Limin and Zhang, Hao and Zhang, Chenhao and Xu, Xiangrong and Zhu, Jianfeng},
  journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},
  year={2023},
  publisher={IEEE}
}

@misc{attention,
      title={Attention Is All You Need}, 
      author={Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
      year={2023},
      eprint={1706.03762},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/1706.03762}, 
}

@misc{same,
      title={SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images}, 
      author={Ke Yan and Jinzheng Cai and Dakai Jin and Shun Miao and Dazhou Guo and Adam P. Harrison and Youbao Tang and Jing Xiao and Jingjing Lu and Le Lu},
      year={2023},
      eprint={2012.02383},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2012.02383}, 
}

@inproceedings{headsam,
author = {Dingjie Su and Yubo Fan and Benoit M. Dawant},
title = {{Localization of Ear Landmarks in Head CT images using Self-Supervised Anatomical Embeddings }},
booktitle = {Medical Imaging 2025: Image Processing},
publisher = {SPIE},
year = {2025}
}

@misc{medsam2,
      title={Medical SAM 2: Segment medical images as video via Segment Anything Model 2}, 
      author={Jiayuan Zhu and Abdullah Hamdi and Yunli Qi and Yueming Jin and Junde Wu},
      year={2024},
      eprint={2408.00874},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2408.00874}, 
}

@article{medsam_nature,
   title={Segment anything in medical images},
   volume={15},
   ISSN={2041-1723},
   url={http://dx.doi.org/10.1038/s41467-024-44824-z},
   DOI={10.1038/s41467-024-44824-z},
   number={1},
   journal={Nature Communications},
   publisher={Springer Science and Business Media LLC},
   author={Ma, Jun and He, Yuting and Li, Feifei and Han, Lin and You, Chenyu and Wang, Bo},
   year={2024},
   month=jan }

@misc{medsam_LSAM,
      title={MedLSAM: Localize and Segment Anything Model for 3D CT Images}, 
      author={Wenhui Lei and Xu Wei and Xiaofan Zhang and Kang Li and Shaoting Zhang},
      year={2024},
      eprint={2306.14752},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2306.14752}, 
}

@misc{sam2,
      title={SAM 2: Segment Anything in Images and Videos}, 
      author={Nikhila Ravi and Valentin Gabeur and Yuan-Ting Hu and Ronghang Hu and Chaitanya Ryali and Tengyu Ma and Haitham Khedr and Roman Rädle and Chloe Rolland and Laura Gustafson and Eric Mintun and Junting Pan and Kalyan Vasudev Alwala and Nicolas Carion and Chao-Yuan Wu and Ross Girshick and Piotr Dollár and Christoph Feichtenhofer},
      year={2024},
      eprint={2408.00714},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2408.00714}, 
}

@misc{pointnet,
      title={PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation}, 
      author={Charles R. Qi and Hao Su and Kaichun Mo and Leonidas J. Guibas},
      year={2017},
      eprint={1612.00593},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1612.00593}, 
}

@misc{contrastive_learning,
      title={Representation Learning with Contrastive Predictive Coding}, 
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748}, 
}

@article{jianing,
	title = {Atlas-based {Segmentation} of {Intracochlear} {Anatomy} in {Metal} {Artifact} {Affected} {CT} {Images} of the {Ear} with {Co}-trained {Deep} {Neural} {Networks}},
	volume = {12904},
	doi = {10.1007/978-3-030-87202-1_2},
	abstract = {We propose an atlas-based method to segment the intracochlear anatomy (ICA) in the post-implantation CT (Post-CT) images of cochlear implant (CI) recipients that preserves the point-to-point correspondence between the meshes in the atlas and the segmented volumes. To solve this problem, which is challenging because of the strong artifacts produced by the implant, we use a pair of co-trained deep networks that generate dense deformation fields (DDFs) in opposite directions. One network is tasked with registering an atlas image to the Post-CT images and the other network is tasked with registering the Post-CT images to the atlas image. The networks are trained using loss functions based on voxel-wise labels, image content, fiducial registration error, and cycle-consistency constraint. The segmentation of the ICA in the Post-CT images is subsequently obtained by transferring the predefined segmentation meshes of the ICA in the atlas image to the Post-CT images using the corresponding DDFs generated by the trained registration networks. Our model can learn the underlying geometric features of the ICA even though they are obscured by the metal artifacts. We show that our end-to-end network produces results that are comparable to the current state of the art (SOTA) that relies on a two-steps approach that first uses conditional generative adversarial networks to synthesize artifact-free images from the Post-CT images and then uses an active shape model-based method to segment the ICA in the synthetic images. Our method requires a fraction of the time needed by the SOTA, which is important for end-user acceptance.},
	language = {eng},
	journal = {Medical image computing and computer-assisted intervention: MICCAI. International Conference on Medical Image Computing and Computer-Assisted Intervention},
	author = {Wang, Jianing and Su, Dingjie and Fan, Yubo and Chakravorti, Srijata and Noble, Jack H. and Dawant, Benoit M.},
	year = {2021},
	pmid = {35360271},
	pmcid = {PMC8964077},
	keywords = {Atlas-based segmentation, Cochlear implant, Metal artifact, Non-rigid registration},
	pages = {14--23},
	file = {Accepted Version:C\:\\Users\\sud\\Zotero\\storage\\ESS4BX7W\\Wang et al. - 2021 - Atlas-based Segmentation of Intracochlear Anatomy .pdf:application/pdf},
}

@article{noble_automatic_2011,
	title = {Automatic segmentation of intracochlear anatomy in conventional {CT}},
	volume = {58},
	issn = {1558-2531},
	doi = {10.1109/TBME.2011.2160262},
	abstract = {Cochlear implant surgery is a procedure performed to treat profound hearing loss. Clinical results suggest that implanting the electrode in the scala tympani, one of the two principal cavities inside the cochlea, may result in better hearing restoration. Segmentation of intracochlear cavities could thus aid the surgeon to choose the point of entry and angle of approach that maximize the likelihood of successful implant insertion, which may lead to more substantial hearing restoration. However, because the membrane that separates the intracochlear cavities is too thin to be seen in conventional in vivo imaging, traditional segmentation techniques are inadequate. In this paper, we circumvent this problem by creating an active shape model with micro CT (μCT) scans of the cochlea acquired ex vivo. We then use this model to segment conventional CT scans. The model is fitted to the partial information available in the conventional scans and used to estimate the position of structures not visible in these images. Quantitative evaluation of our method, made possible by the set of μCTs, results in Dice similarity coefficients averaging 0.75. Mean and maximum surface errors average 0.21 and 0.80  mm.},
	language = {eng},
	number = {9},
	journal = {IEEE transactions on bio-medical engineering},
	author = {Noble, Jack H. and Labadie, Robert F. and Majdani, Omid and Dawant, Benoit M.},
	month = sep,
	year = {2011},
	pmid = {21708495},
	pmcid = {PMC3804019},
	keywords = {Humans, Tomography, X-Ray Computed, Algorithms, Radiographic Image Enhancement, Reproducibility of Results, Scala Tympani, Scala Vestibuli, X-Ray Microtomography},
	pages = {2625--2632},
	file = {Accepted Version:C\:\\Users\\sud\\Zotero\\storage\\7RGDGZI2\\Noble et al. - 2011 - Automatic segmentation of intracochlear anatomy in.pdf:application/pdf},
}

@ARTICLE{sam_finetune1,
  title     = "Segment anything model for few-shot medical image segmentation
               with domain tuning",
  author    = "Shi, Weili and Zhang, Penglong and Li, Yuqin and Jiang, Zhengang",
  journal   = "Complex Intell. Syst.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  11,
  number    =  1,
  month     =  jan,
  year      =  2025,
  copyright = "https://creativecommons.org/licenses/by-nc-nd/4.0",
  language  = "en"
}

@article{pip,
author = {Shimrat, M.},
title = {Algorithm 112: Position of point relative to polygon},
year = {1962},
issue_date = {Aug. 1962},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {5},
number = {8},
issn = {0001-0782},
url = {https://doi.org/10.1145/368637.368653},
doi = {10.1145/368637.368653},
journal = {Commun. ACM},
month = aug,
pages = {434},
numpages = {7}
}

@ARTICLE{tetris,
  author={Lee, Matthew Chung Hai and Petersen, Kersten and Pawlowski, Nick and Glocker, Ben and Schaap, Michiel},
  journal={IEEE Transactions on Medical Imaging}, 
  title={TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors}, 
  year={2019},
  volume={38},
  number={11},
  pages={2596-2606},
  keywords={Shape;Neural networks;Image segmentation;Strain;Deformable models;Training;Task analysis;Image segmentation;shape priors;neural networks;template deformation;image registration},
  doi={10.1109/TMI.2019.2905990}}

@article{a-value1,
title = {Cochlear implantation: Predicting the scala tympani volume of the pediatric recipients},
journal = {International Journal of Pediatric Otorhinolaryngology},
volume = {165},
pages = {111432},
year = {2023},
issn = {0165-5876},
doi = {https://doi.org/10.1016/j.ijporl.2022.111432},
url = {https://www.sciencedirect.com/science/article/pii/S0165587622003937},
author = {Marzouqi Salamah and Yassin Abdelsamad and Asma Alahmadi and Abdulrahman Alsanosi},
keywords = {Cochlear implant, Scala tympani, Cochlear duct length, Cochlear volume, Temporal bone},
abstract = {Objectives
The main aim of this study was to estimate the volume of the Scala Tympani (ST) of our pediatric cochlear implant (CI) recipients from the computed tomography (CT) images. Then, to study the association between ST volume and both demographic characteristics and cochlear parameters.
Methods
A retrospective study on the CT scans of pediatric CI patients at a tertiary referral CI center. Congenital or acquired cochlear defects were excluded. Two reviewers, with the same level of experience, blindly measured the main cochlear parameters and studied its anatomy. Then, the interrater reliability was tested to measure any differences between the two readings. After that, the ST volume of the included patients was calculated and analyzed. Furthermore, the correlations between the main cochlear parameters and ST volume were studied to propose a formula for estimating the ST volume from the cochlear duct length (CDL).
Results
The mean predicted ST volume among our pediatric CI recipients was 38.51 ± 5.54 μl (range; 24.47–52.57 μl). The statistical analysis revealed that all cochlear parameters (A, B, H, and CDL values) could be significant predictors of the ST volume (p=<0.0001).
Conclusion
The main cochlear parameters along with the CDL are positively linked to the ST volume. There are considerable differences in cochlear size and scala tympani volume among our pediatric population. These findings confirm the importance of pre-operative planning for proper electrode array selection.}
}