@book{Lamport:Book:1989,
 author = {Lamport, Leslie},
 title = {Latex: A Document Preparation System},
 year = {1986},
 isbn = {0-201-15790-X},
 publisher = {Addison-Wesley Longman Publishing Co., Inc.},
 address = {Boston, MA, USA},
}

@misc{Hinton:arXiv:2015:Distilling,
 title={Distilling the knowledge in a neural network},
 author={Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
 OPTeprinttype={arXiv},
 OPTeprint={1503.02531},
 howpublished={Eprint \href{http://arxiv.org/abs/1503.02531}{arXiv:1503.02531}},
 OPTurl={http://arxiv.org/abs/1503.02531},
 year=2015
}
@misc{LPIPS,
      title={The Unreasonable Effectiveness of Deep Features as a Perceptual Metric}, 
      author={Richard Zhang and Phillip Isola and Alexei A. Efros and Eli Shechtman and Oliver Wang},
      year={2018},
      eprint={1801.03924},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1801.03924}, 
}

@ARTICLE{SSIM,
  author={Zhou Wang and Bovik, A.C. and Sheikh, H.R. and Simoncelli, E.P.},
  journal={IEEE Transactions on Image Processing}, 
  title={Image quality assessment: from error visibility to structural similarity}, 
  year={2004},
  volume={13},
  number={4},
  pages={600-612},
  keywords={Image quality;Humans;Transform coding;Visual system;Visual perception;Data mining;Layout;Quality assessment;Degradation;Indexes},
  doi={10.1109/TIP.2003.819861}}

@misc{Ronneberger.2015,
 abstract = {There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .

conditionally accepted at MICCAI 2015},
 author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
 year = {2015},
 title = {U-Net: Convolutional Networks for Biomedical Image Segmentation},
 publisher = {arXiv},
 doi = {10.48550/arXiv.1505.04597}
}

@article{Leclerc.2019,
 abstract = {Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6{\%}. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.},
 author = {Leclerc, Sarah and Smistad, Erik and Pedrosa, Joao and Ostvik, Andreas and Cervenansky, Frederic and Espinosa, Florian and Espeland, Torvald and Berg, Erik Andreas Rye and Jodoin, Pierre-Marc and Grenier, Thomas and Lartizien, Carole and Dhooge, Jan and Lovstakken, Lasse and Bernard, Olivier},
 year = {2019},
 title = {Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography},
 pages = {2198--2210},
 volume = {38},
 number = {9},
 journal = {IEEE transactions on medical imaging},
 doi = {10.1109/TMI.2019.2900516}
}

@misc{WilfridoGomezFlores.2023,
 abstract = {The BUS-BRA Dataset is a publicly available dataset of anonymized breast ultrasound (BUS) images of 1,064 patients that underwent routinary breast studies. The BUS dataset contains biopsy-proven tumor cases and BI-RADS annotations in categories 2, 3, 4, and 5. In addition, the dataset also contains ground truth delineations that divide the BUS images into tumoral and normal regions. A full description of the BUS-BRA dataset can be found in the following article:

Wilfrido G{\'o}mez-Flores, Maria Julia Gregorio-Calas, and Wagner Coelho de Albuquerque Pereira, {\textquotedbl}BUS-BRA: A Breast Ultrasound Dataset for Assessing Computer-aided Diagnosis Systems,{\textquotedbl} Medical Physics, vol. 51, pp. 3110-3123, 2024, DOI: 10.1002/mp.16812.

Any research originating from its usage is required to cite this paper.

The Program of Biomedical Engineering of the Federal University of Rio de Janeiro (PEB/COPPE-UFRJ, Brazil) has a copyright on the data and is the principal distributor of the BUS-BRA Dataset. In addition, the Centro de Investigaci{\'o}n y de Estudios Avanzados (Cinvestav, Mexico) is involved in an ongoing effort to develop this dataset to aid research efforts in developing, testing, and evaluating algorithms for breast ultrasound analysis.

The source codes to replicate these experiments in the article are found at https://github.com/wgomezf/BUS-BRA},
 author = {{Wilfrido G{\'o}mez-Flores} and {Maria Julia Gregorio-Calas} and {Wagner Coelho de Albuquerque Pereira}},
 year = {2023},
 title = {BUS-BRA: A Breast Ultrasound Dataset for Assessing Computer-aided Diagnosis Systems},
 publisher = {Zenodo},
 doi = {10.5281/zenodo.8231412}
}

@article{AlDhabyani.2020,
 abstract = {Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of early deaths. The data presented in this article reviews the medical images of breast cancer using ultrasound scan. Breast Ultrasound Dataset is categorized into three classes: normal, benign, and malignant images. Breast ultrasound images can produce great results in classification, detection, and segmentation of breast cancer when combined with machine learning.},
 author = {Al-Dhabyani, Walid and Gomaa, Mohammed and Khaled, Hussien and Fahmy, Aly},
 year = {2020},
 title = {Dataset of breast ultrasound images},
 pages = {104863},
 volume = {28},
 issn = {2352-3409},
 journal = {Data in brief},
 doi = {10.1016/j.dib.2019.104863}
}

@article{vandenHeuvel2018,
  title={Automated measurement of fetal head circumference using 2D ultrasound images},
  author={van den Heuvel, Thomas L A and de Bruijn, Dagmar and de Korte, Chris L and Ginneken, Bram van},
  journal={PLoS One},
  volume={13},
  number={8},
  pages={e0200412},
  year={2018},
  publisher={Public Library of Science},
  doi={10.1371/journal.pone.0200412},
  pmid={30138319},
  pmcid={PMC6107118},
  url={https://doi.org/10.1371/journal.pone.0200412},
  abstract={In this paper we present a computer aided detection (CAD) system for automated measurement of the fetal head circumference (HC) in 2D ultrasound images for all trimesters of the pregnancy. The HC can be used to estimate the gestational age and monitor growth of the fetus. Automated HC assessment could be valuable in developing countries, where there is a severe shortage of trained sonographers. The CAD system consists of two steps: First, Haar-like features were computed from the ultrasound images to train a random forest classifier to locate the fetal skull. Secondly, the HC was extracted using Hough transform, dynamic programming and an ellipse fit. The CAD system was trained on 999 images and validated on an independent test set of 335 images from all trimesters. The test set was manually annotated by an experienced sonographer and a medical researcher. The reference gestational age (GA) was estimated using the crown-rump length measurement (CRL). The mean difference between the reference GA and the GA estimated by the experienced sonographer was 0.8 ± 2.6, -0.0 ± 4.6 and 1.9 ± 11.0 days for the first, second and third trimester, respectively. The mean difference between the reference GA and the GA estimated by the medical researcher was 1.6 ± 2.7, 2.0 ± 4.8 and 3.9 ± 13.7 days. The mean difference between the reference GA and the GA estimated by the CAD system was 0.6 ± 4.3, 0.4 ± 4.7 and 2.5 ± 12.4 days. The results show that the CAD system performs comparable to an experienced sonographer. The presented system shows similar or superior results compared to systems published in literature. This is the first automated system for HC assessment evaluated on a large test set which contained data of all trimesters of the pregnancy.}
}

@article{Lu.2022,
 abstract = {The use of transperineal ultrasound techniques for the assessment of fetal head descent and progression is an adjunct to clinical examination. Automatic identification of parameters based on ultrasound images will greatly reduce the subjectivity and non-repeatability of the clinician's judgment. However, the lack of a pubic symphysis-fetal head dataset hinders the development of algorithms. Here, we present an intrapartum transperineal ultrasound dataset of the Intelligent Fetal Monitoring Lab of Jinan University (named the JNU-IFM dataset), in which intrapartum transperineal ultrasound videos of 78 were recorded from 51 patients. These data were obtained with the Youkey D8 wireless 2D ultrasound probe with its corresponding supporting software by Wuhan Youkey Bio-Medical Electronics Co., Ltd., Wuhan, China. In these videos, 6224 high-quality images with four categories were selected to form the JNU- IFM dataset. These images were labelled using the Pair software and then validated by two experienced radiologists. We hope that this data set can be used in the segmentation of the pubic symphysis-fetal head.},
 author = {Lu, Yaosheng and Zhou, Mengqiang and Zhi, Dengjiang and Zhou, Minghong and Jiang, Xiaosong and Qiu, Ruiyu and Ou, Zhanhong and Wang, Huijin and {Di Qiu} and Zhong, Mei and Lu, Xiaoxing and Chen, Gaowen and Bai, Jieyun},
 year = {2022},
 title = {The JNU-IFM dataset for segmenting pubic symphysis-fetal head},
 pages = {107904},
 volume = {41},
 issn = {2352-3409},
 journal = {Data in brief},
 doi = {10.1016/j.dib.2022.107904}
}

@article{Wunderling2017,
  title = {Comparison of thyroid segmentation techniques for 3D ultrasound},
  author = {Wunderling, T. and Golla, B. and Poudel, P. and Arens, C. and Friebe, M. and Hansen, C.},
  journal = {Medical Imaging 2017: Image Processing},
  volume = {10133},
  pages = {1013317},
  year = {2017},
  publisher = {SPIE},
  doi = {10.1117/12.2254234},
  url = {https://doi.org/10.1117/12.2254234},
  abstract = {The segmentation of the thyroid in ultrasound images is a field of active research. The thyroid is a gland of the endocrine system and regulates several body functions. Measuring the volume of the thyroid is regular practice of diagnosing pathological changes. In this work, we compare three approaches for semi-automatic thyroid segmentation in freehand-tracked three-dimensional ultrasound images. The approaches are based on level set, graph cut and feature classification. For validation, sixteen 3D ultrasound records were created with ground truth segmentations, which we make publicly available. The properties analyzed are the Dice coefficient when compared against the ground truth reference and the effort of required interaction. Our results show that in terms of Dice coefficient, all algorithms perform similarly. For interaction, however, each algorithm has advantages over the other. The graph cut-based approach gives the practitioner direct influence on the final segmentation. Level set and feature classifier require less interaction, but offer less control over the result. All three compared methods show promising results for future work and provide several possible extensions.}
}

@article{gong2022thyroid,
  title={Thyroid Region Prior Guided Attention for Ultrasound Segmentation of Thyroid Nodules},
  author={Gong, Haifan and Chen, Jiaxin and Chen, Guanqi and Li, Haofeng and Chen, Fei and Li, Guanbin},
  journal={Computers in Biology and Medicine},
  volume={106389},
  pages={1--12},
  year={2022},
  publisher={Elsevier}
}

@misc{xia2017wnetdeepmodelfully,
      title={W-Net: A Deep Model for Fully Unsupervised Image Segmentation}, 
      author={Xide Xia and Brian Kulis},
      year={2017},
      eprint={1711.08506},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1711.08506}
}
@misc{he2015deepresiduallearningimage,
      title={Deep Residual Learning for Image Recognition}, 
      author={Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
      year={2015},
      eprint={1512.03385},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1512.03385}, 
}

@misc{panetfewshotimagesemantic,
      title={PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment}, 
      author={Kaixin Wang and Jun Hao Liew and Yingtian Zou and Daquan Zhou and Jiashi Feng},
      year={2020},
      eprint={1908.06391},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1908.06391}, 
}

@misc{filmvisualreasoninggeneral,
      title={FiLM: Visual Reasoning with a General Conditioning Layer}, 
      author={Ethan Perez and Florian Strub and Harm de Vries and Vincent Dumoulin and Aaron Courville},
      year={2017},
      eprint={1709.07871},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1709.07871}, 
}

@misc{mixstyle,
      title={Domain Generalization with MixStyle}, 
      author={Kaiyang Zhou and Yongxin Yang and Yu Qiao and Tao Xiang},
      year={2021},
      eprint={2104.02008},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2104.02008}, 
}

@inproceedings{cbn,
author = {Harm de Vries and Florian Strub and J\'er\'emie Mary and Hugo Larochelle and Olivier Pietquin and Aaron C. Courville},
title = {Modulating early visual processing by language},
booktitle = {Advances in Neural Information Processing Systems 30},
year = {2017},
url = {https://arxiv.org/abs/1707.00683}
}
@misc{kirillov2023segment,
      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{Ma.2024,
 abstract = {Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, often tailored to specific modalities or disease types, lack generalizability across the diverse spectrum of medical image segmentation tasks. Here we present MedSAM, a foundation model designed for bridging this gap by enabling universal medical image segmentation. The model is developed on a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. We conduct a comprehensive evaluation on 86 internal validation tasks and 60 external validation tasks, demonstrating better accuracy and robustness than modality-wise specialist models. By delivering accurate and efficient segmentation across a wide spectrum of tasks, MedSAM holds significant potential to expedite the evolution of diagnostic tools and the personalization of treatment plans.},
 author = {Ma, Jun and He, Yuting and Li, Feifei and Han, Lin and You, Chenyu and Wang, Bo},
 year = {2024},
 title = {Segment anything in medical images},
 pages = {654},
 volume = {15},
 number = {1},
 journal = {Nature communications},
 doi = {10.1038/s41467-024-44824-z}
}

@article{Meyer_2025,
   title={Ultrasam: a foundation model for ultrasound using large open-access segmentation datasets},
   ISSN={1861-6429},
   url={http://dx.doi.org/10.1007/s11548-025-03517-8},
   DOI={10.1007/s11548-025-03517-8},
   journal={International Journal of Computer Assisted Radiology and Surgery},
   publisher={Springer Science and Business Media LLC},
   author={Meyer, Adrien and Murali, Aditya and Zarin, Farahdiba and Mutter, Didier and Padoy, Nicolas},
   year={2025},
   month=sep }

@misc{unetr3d,
      title={UNETR: Transformers for 3D Medical Image Segmentation}, 
      author={Ali Hatamizadeh and Yucheng Tang and Vishwesh Nath and Dong Yang and Andriy Myronenko and Bennett Landman and Holger Roth and Daguang Xu},
      year={2021},
      eprint={2103.10504},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2103.10504}, 
}

@misc{cao2021swinunetunetlikepuretransformer,
      title={Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation}, 
      author={Hu Cao and Yueyue Wang and Joy Chen and Dongsheng Jiang and Xiaopeng Zhang and Qi Tian and Manning Wang},
      year={2021},
      eprint={2105.05537},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2105.05537}, 
}

@misc{wong2025multiversegscalableinteractivesegmentation,
      title={MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance}, 
      author={Hallee E. Wong and Jose Javier Gonzalez Ortiz and John Guttag and Adrian V. Dalca},
      year={2025},
      eprint={2412.15058},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.15058}, 
}

@misc{isensee2018nnunetselfadaptingframeworkunetbased,
      title={nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation}, 
      author={Fabian Isensee and Jens Petersen and Andre Klein and David Zimmerer and Paul F. Jaeger and Simon Kohl and Jakob Wasserthal and Gregor Koehler and Tobias Norajitra and Sebastian Wirkert and Klaus H. Maier-Hein},
      year={2018},
      eprint={1809.10486},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1809.10486}, 
}

@misc{ravi2024sam2segmentimages,
      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{ma2025medsam2segment3dmedical,
      title={MedSAM2: Segment Anything in 3D Medical Images and Videos}, 
      author={Jun Ma and Zongxin Yang and Sumin Kim and Bihui Chen and Mohammed Baharoon and Adibvafa Fallahpour and Reza Asakereh and Hongwei Lyu and Bo Wang},
      year={2025},
      eprint={2504.03600},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2504.03600}, 
}
@misc{xing2025sam2sgpenhancingsam2medical,
      title={SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting}, 
      author={Yang Xing and Jiong Wu and Yuheng Bu and Kuang Gong},
      year={2025},
      eprint={2506.19658},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2506.19658}, 
}

@misc{neuralizer,
      title={Neuralizer: General Neuroimage Analysis without Re-Training}, 
      author={Steffen Czolbe and Adrian V. Dalca},
      year={2023},
      eprint={2305.02644},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2305.02644}, 
}
@misc{zhou2018unetnestedunetarchitecture,
      title={UNet++: A Nested U-Net Architecture for Medical Image Segmentation}, 
      author={Zongwei Zhou and Md Mahfuzur Rahman Siddiquee and Nima Tajbakhsh and Jianming Liang},
      year={2018},
      eprint={1807.10165},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1807.10165}, 
}
@article{Xiao.2025,
 abstract = {The intricate imaging structures, artifacts, and noise present in ultrasound images and videos pose significant challenges for accurate segmentation. Deep learning has recently emerged as a prominent field, playing a crucial role in medical image processing. This paper reviews ultrasound image and video segmentation methods based on deep learning techniques, summarizing the latest developments in this field, such as diffusion and segment anything models as well as classical methods. These methods are classified into four main categories based on the characteristics of the segmentation methods. Each category is outlined and evaluated in the corresponding section. We provide a comprehensive overview of deep learning-based ultrasound image segmentation methods, evaluation metrics, and common ultrasound datasets, hoping to explain the advantages and disadvantages of each method, summarize its achievements, and discuss challenges and future trends.},
 author = {Xiao, Xiaolong and Zhang, Jianfeng and Shao, Yuan and Liu, Jialong and Shi, Kaibing and He, Chunlei and Kong, Dexing},
 year = {2025},
 title = {Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges},
 keywords = {Algorithms;Deep Learning;Humans;Image Processing, Computer-Assisted/methods;Ultrasonography/methods;Video Recording},
 volume = {25},
 number = {8},
 issn = {1424-8220},
 journal = {Sensors (Basel, Switzerland)},
 doi = {10.3390/s25082361}
}
@misc{chen2021transunettransformersmakestrong,
      title={TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation}, 
      author={Jieneng Chen and Yongyi Lu and Qihang Yu and Xiangde Luo and Ehsan Adeli and Yan Wang and Le Lu and Alan L. Yuille and Yuyin Zhou},
      year={2021},
      eprint={2102.04306},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2102.04306}, 
}
@misc{butoi,
      title={UniverSeg: Universal Medical Image Segmentation}, 
      author={Victor Ion Butoi and Jose Javier Gonzalez Ortiz and Tianyu Ma and Mert R. Sabuncu and John Guttag and Adrian V. Dalca},
      year={2023},
      eprint={2304.06131},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2304.06131}, 
}
@misc{wu2024efficientincontextmedicalsegmentation,
      title={Efficient In-Context Medical Segmentation with Meta-driven Visual Prompt Selection}, 
      author={Chenwei Wu and David Restrepo and Zitao Shuai and Zhongming Liu and Liyue Shen},
      year={2024},
      eprint={2407.11188},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2407.11188}, 
}
@misc{hu2025cyclecontextverificationincontext,
      title={Cycle Context Verification for In-Context Medical Image Segmentation}, 
      author={Shishuai Hu and Zehui Liao and Liangli Zhen and Huazhu Fu and Yong Xia},
      year={2025},
      eprint={2507.08357},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2507.08357}, 
}
@misc{gao2025segmentuniversalmedicalimage,
      title={Show and Segment: Universal Medical Image Segmentation via In-Context Learning}, 
      author={Yunhe Gao and Di Liu and Zhuowei Li and Yunsheng Li and Dongdong Chen and Mu Zhou and Dimitris N. Metaxas},
      year={2025},
      eprint={2503.19359},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.19359}, 
}