@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
}
% Long form of conference & journal abbreviations -- especially for camera ready
@String(PAMI  = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV  = {Int. J. Comput. Vis.})
@String(CVPR  = {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV  = {Int. Conf. Comput. Vis.})
@String(ECCV  = {Eur. Conf. Comput. Vis.})
@String(NeurIPS = {Adv. Neural Inform. Process. Syst.})
@String(ICML  = {Int. Conf. Mach. Learn.})
@String(ICLR  = {Int. Conf. Learn. Represent.})
@String(ACCV  = {Asian Conf. Comput. Vis.})
@String(BMVC  = {Brit. Mach. Vis. Conf.})
@String(CVPRW = {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(AAAI  = {AAAI})
@String(MICCAI  = {Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention})
@String(MICCAIW  = {Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention Workshop})
@String(MIDL  = {Proceedings of the International Conference on Medical Imaging with Deep Learning})

@String(IJCAI = {IJCAI})
@String(ICIP  = {IEEE Int. Conf. Image Process.})
@String(ICPR  = {Int. Conf. Pattern Recog.})
@String(ICASSP=	{ICASSP})
@String(ICME  = {Int. Conf. Multimedia and Expo})
@String(JMLR  = {J. Mach. Learn. Res.})
@String(TMLR  = {Trans. Mach. Learn Res.})
@String(TOG   = {ACM Trans. Graph.})
@String(TIP   = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TCSVT = {IEEE Trans. Circuit Syst. Video Technol.})
@String(TMM   = {IEEE Trans. Multimedia})
@String(ACMMM = {ACM Int. Conf. Multimedia})
@String(PR    = {Pattern Recognition})

@String(MNI	  = {Nature Mach. Intell.})
@String(SPL	  = {IEEE Sign. Process. Letters})
@String(VR    = {Vis. Res.})
@String(JOV	  = {J. Vis.})
@String(TVC   = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF   = {Comput. Graph. Forum})
@String(CVM   = {Computational Visual Media})


% Short form of conference & journal abbreviations -- especially for submission version
% if desired, remove these macros in favor of the above ones
@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition})
@String(ICCV  = {Proceedings of the IEEE/CVF International Conference on Computer Vision})
@String(ECCV  = {Proceedings of the European Conference on Computer Vision})
@String(NeurIPS = {NeurIPS})
@String(ICML  = {ICML})
@String(ICLR  = {ICLR})
@String(ACCV  = {ACCV})
@String(BMVC  =	{BMVC})
@String(CVPRW = {CVPRW})
@String(AAAI  = {AAAI})
@String(IJCAI = {IJCAI})
@String(ICIP  = {ICIP})
@String(ICPR  = {ICPR})
@String(ICASSP=	{ICASSP})
@String(ICME  =	{ICME})
@String(JMLR  = {JMLR})
@String(TMLR  = {TMLR})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(PR    = {PR})

@article{chen_2020FCM_deep,
  title={Deep learning for cardiac image segmentation: a review},
  author={Chen, Chen and Qin, Chen and Qiu, Huaqi and Tarroni, Giacomo and Duan, Jinming and Bai, Wenjia and Rueckert, Daniel},
  journal={Frontiers in Cardiovascular Medicine},
  volume={7},
  pages={25},
  year={2020},
  publisher={Frontiers Media SA}
}

@article{zeng_2024SR_pretrained,
  title={Pretrained subtraction and segmentation model for coronary angiograms},
  author={Zeng, Yunjie and Liu, Han and Hu, Juan and Zhao, Zhengbo and She, Qiang},
  journal={Scientific Reports},
  volume={14},
  number={1},
  pages={19888},
  year={2024},
  publisher={Nature Publishing Group UK London}
}

@article{sweeney_2024AS_unsupervised,
  title={Unsupervised segmentation of 3D microvascular photoacoustic images using deep generative learning},
  author={Sweeney, Paul W and Hacker, Lina and Lefebvre, Thierry L and Brown, Emma L and Gr{\"o}hl, Janek and Bohndiek, Sarah E},
  journal={Advanced Science},
  volume={11},
  number={32},
  pages={2402195},
  year={2024},
  publisher={Wiley Online Library}
}

@article{wang_2020NMI_augmenting,
  title={Augmenting vascular disease diagnosis by vasculature-aware unsupervised learning},
  author={Wang, Yong and Ji, Mengqi and Jiang, Shengwei and Wang, Xukang and Wu, Jiamin and Duan, Feng and Fan, Jingtao and Huang, Laiqiang and Ma, Shaohua and Fang, Lu and others},
  journal={Nature Machine Intelligence},
  volume={2},
  number={6},
  pages={337--346},
  year={2020},
}

@ARTICLE{Wang_2024JBHI_Unsupervised,
  author={Wang, Yinuo and Meng, Cai and Tang, Zhouping and Bai, Xiangzhuo and Ji, Ping and Bai, Xiangzhi},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={Unsupervised Domain Adaptation for Cross-Modality Cerebrovascular Segmentation}, 
  year={2024},
  volume={},
  number={},
  pages={1--14},
  doi={10.1109/JBHI.2024.3523103}
}

@ARTICLE{Shen_2023TMI_SCANet,
  author={Shen, Ning and Xu, Tingfa and Bian, Ziyang and Huang, Shiqi and Mu, Feng and Huang, Bo and Xiao, Yuze and Li, Jianan},
  journal={IEEE Transactions on Medical Imaging}, 
  title={SCANet: A Unified Semi-Supervised Learning Framework for Vessel Segmentation}, 
  year={2023},
  volume={42},
  number={9},
  pages={2476--2489},
  doi={10.1109/TMI.2022.3193150}}

@article{yao_2023CBM_enhancing,
  title={Enhancing percutaneous coronary intervention with heuristic path planning and deep-learning-based vascular segmentation},
  author={Yao, Tianliang and Wang, Chengjia and Wang, Xinyi and Li, Xiang and Jiang, Zhaolei and Qi, Peng},
  journal={Computers in Biology and Medicine},
  volume={166},
  pages={107540},
  year={2023},
  publisher={Elsevier}
}

@article{du_2024JIM_review,
  title={Deep learning technology in vascular image segmentation and disease diagnosis},
  author={Du, Chengyang and Zhuang, Jie and Huang, Xinglu},
  journal={Journal of Intelligent Medicine},
  year={2024},
  publisher={Wiley Online Library}
}

@InProceedings{Shi_Centerline_MICCAI2024,
    author = { Shi, Pengcheng and Hu, Jiesi and Yang, Yanwu and Gao, Zilve and Liu, Wei and Ma, Ting},
    title = { { Centerline Boundary Dice Loss for Vascular Segmentation } },
    booktitle = MICCAI,
    year = {2024},
    pages = {46 -- 56}
}

@InProceedings{Xie_DSNet_MICCAI2024,
    author = { Xie, Qihang and Zhang, Dan and Mou, Lei and Wang, Shanshan and Zhao, Yitian and Guo, Mengguo and Zhang, Jiong},
    title = { { DSNet: A Spatio-Temporal Consistency Network for Cerebrovascular Segmentation in Digital Subtraction Angiography Sequences } },
    booktitle = MICCAI,
    year = {2024},
    pages = {199 -- 208}
}

@InProceedings{Gen_Force_MICCAI2024,
    author = { Geng, Yimeng and Meng, Gaofeng and Chen, Mingcong and Cao, Guanglin and Zhao, Mingyang and Zhao, Jianbo and Liu, Hongbin},
    title = { { Force Sensing Guided Artery-Vein Segmentation via Sequential Ultrasound Images } },
    booktitle = MICCAI,
    year = {2024},
    pages = {656 -- 666}
}
@InProceedings{Dai_RIPAV_MICCAI2024,
    author = { Dai, Wei and Yao, Yinghao and Kong, Hengte and Chen, Zhen Ji and Wang, Sheng and Bai, Qingshi and Sun, Haojun and Yang, Yongxin and Su, Jianzhong},
    title = { { RIP-AV: Joint Representative Instance Pre-training with Context Aware Network for Retinal Artery/Vein Segmentation } },
    booktitle = MICCAI,
    year = {2024},
    pages = {764 -- 774}
}

@InProceedings{Ace_The_MICCAI2024,
    author = { Acebes, Cesar and Moustafa, Abdel Hakim and Camara, Oscar and Galdran, Adrian},
    title = { { The Centerline-Cross Entropy Loss for Vessel-Like Structure Segmentation: Better Topology Consistency Without Sacrificing Accuracy } },
    booktitle = MICCAI,
    year = {2024},
    pages = {710 -- 720}
}
@InProceedings{Zha_XASim2Real_MICCAI2024,
    author = { Zhang, Baochang and Zhang, Zichen and Liu, Shuting and Faghihroohi, Shahrooz and Schunkert, Heribert and Navab, Nassir},
    title = { { XA-Sim2Real: Adaptive Representation Learning for Vessel Segmentation in X-ray Angiography } },
    booktitle =  MICCAI,
    year = {2024},
    pages = {747 -- 756}
}

@inproceedings{dima20233d,
  title={3D arterial segmentation via single 2D projections and depth supervision in contrast-enhanced CT images},
  author={Dima, Alina F and Zimmer, Veronika A and Menten, Martin J and Li, Hongwei Bran and Graf, Markus and Lemke, Tristan and Raffler, Philipp and Graf, Robert and Kirschke, Jan S and Braren, Rickmer and others},
  booktitle=MICCAI,
  pages={141--151},
  year={2023},
}
@inproceedings{qiu2023corsegrec,
  title={Corsegrec: a topology-preserving scheme for extracting fully-connected coronary arteries from ct angiography},
  author={Qiu, Yuehui and Li, Zihan and Wang, Yining and Dong, Pei and Wu, Dijia and Yang, Xinnian and Hong, Qingqi and Shen, Dinggang},
  booktitle=MICCAI,
  pages={670--680},
  year={2023},
}
@inproceedings{zhou2023henet,
  title={HENet: Hierarchical Enhancement Network for Pulmonary Vessel Segmentation in Non-contrast CT Images},
  author={Zhou, Wenqi and Zhang, Xiao and Gu, Dongdong and Wang, Sheng and Huo, Jiayu and Zhang, Rui and Jiang, Zhihao and Shi, Feng and Xue, Zhong and Zhan, Yiqiang and others},
  booktitle=MICCAI,
  pages={551--560},
  year={2023},
}
@inproceedings{deng2023shape,
  title={Shape-Aware 3D Small Vessel Segmentation with Local Contrast Guided Attention},
  author={Deng, Zhiwei and Xu, Songnan and Zhang, Jianwei and Zhang, Jiong and Wang, Danny J and Yan, Lirong and Shi, Yonggang},
  booktitle=MICCAI,
  pages={354--363},
  year={2023},
}
@inproceedings{zhang2023topology,
  title={Topology-preserving automatic labeling of coronary arteries via anatomy-aware connection classifier},
  author={Zhang, Zhixing and Zhao, Ziwei and Wang, Dong and Zhao, Shishuang and Liu, Yuhang and Liu, Jia and Wang, Liwei},
  booktitle=MICCAI,
  pages={759--769},
  year={2023},
}

@inproceedings{lin2022geometry,
  title={A geometry-constrained deformable attention network for aortic segmentation},
  author={Lin, Weiyuan and Liu, Hui and Gu, Lin and Gao, Zhifan},
  booktitle=MICCAI,
  pages={287--296},
  year={2022},
}
@inproceedings{liu2022edge,
  title={Edge-oriented point-cloud transformer for 3D intracranial aneurysm segmentation},
  author={Liu, Yifan and Liu, Jie and Yuan, Yixuan},
  booktitle=MICCAI,
  pages={97--106},
  year={2022},
}

@article{ghodrati_2022MRM_automatic,
  title={Automatic segmentation of peripheral arteries and veins in ferumoxytol-enhanced MR angiography},
  author={Ghodrati, Vahid and Rivenson, Yair and Prosper, Ashley and de Haan, Kevin and Ali, Fadil and Yoshida, Takegawa and Bedayat, Arash and Nguyen, Kim-Lien and Finn, J Paul and Hu, Peng},
  journal={Magnetic Resonance in Medicine},
  volume={87},
  number={2},
  pages={984--998},
  year={2022},
}

@article{chen_2024MIA_MASAM,
  title={{MA-SAM}: Modality-Agnostic SAM Adaptation for 3D Medical Image Segmentation},
  author={Chen, Cheng and Miao, Juzheng and Wu, Dufan and Zhong, Aoxiao and Yan, Zhiling and Kim, Sekeun and Hu, Jiang and Liu, Zhengliang and Sun, Lichao and Li, Xiang and others},
  journal={Medical Image Analysis},
  volume={98},
  pages={103310},
  year={2024},
}

@article{ma_2024NC_MedSAM,
  title={Segment anything in medical images},
  author={Ma, Jun and He, Yuting and Li, Feifei and Han, Lin and You, Chenyu and Wang, Bo},
  journal={Nature Communications},
  volume={15},
  number={1},
  pages={654},
  year={2024},
}

@inproceedings{ronneberger_2015MICCAI_UNet,
  title={U-Net: Convolutional Networks for Biomedical Image Segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  booktitle=MICCAI,
  pages={234--241},
  year={2015},
}

@article{isensee_2021NM_nnUNet,
  title={nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
  author={Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
  journal={Nature Methods},
  volume={18},
  number={2},
  pages={203--211},
  year={2021},
}
@inproceedings{wong_2024ECCV_scribbleprompt,
  title={{ScribblePrompt}: Fast and Flexible Interactive Segmentation for Any Biomedical Image},
  author={Wong, Hallee E and Rakic, Marianne and Guttag, John and Dalca, Adrian V},
  booktitle=ECCV,
  pages={207--229},
  year={2024},
}
@article{cheng_2023arxiv_sammed2d,
      title={SAM-Med2D}, 
      author={Junlong Cheng and Jin Ye and Zhongying Deng and Jianpin Chen and Tianbin Li and Haoyu Wang and Yanzhou Su and
              Ziyan Huang and Jilong Chen and Lei Jiang and Hui Sun and Junjun He and Shaoting Zhang and Min Zhu and Yu Qiao},
      year={2023},
      eprint={2308.16184},
        journal={arXiv preprint arXiv: 2308.16184},
      archivePrefix={arXiv},
}
@article{zeng_2024arxiv_samvmnet,
  title={SAM-VMNet: deep neural networks for coronary angiography vessel segmentation},
  author={Zeng, Xueying and Huang, Baixiang and Luo, Yu and Wei, Guangyu and He, Songyan and Shao, Yushuang},
  archivePrefix={arXiv},
  journal={arXiv preprint arXiv: 2406.00492},
  year={2024},
  eprint={2406.00492},
}
@article{luo_2021MIA_mideepseg,
  title={MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning},
  author={Luo, Xiangde and Wang, Guotai and Song, Tao and Zhang, Jingyang and Aertsen, Michael and Deprest, Jan and Ourselin, Sebastien and Vercauteren, Tom and Zhang, Shaoting},
  journal={Medical Image Analysis},
  volume={72},
  pages={102102},
  year={2021},
}
@article{gong_2024mia_3dsam,
  title={3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation},
  author={Gong, Shizhan and Zhong, Yuan and Ma, Wenao and Li, Jinpeng and Wang, Zhao and Zhang, Jingyang and Heng, Pheng-Ann and Dou, Qi},
  journal={Medical Image Analysis},
  volume={98},
  pages={103324},
  year={2024},
}
@article{si_2025MJMRI_exploring,
  title={Exploring Prolonged Efficacy of Ferumoxytol-Enhanced Whole-Body MR Angiography: A Preliminary Study in Healthy Male Subjects},
  author={Si, Guangxiang and Liu, Yuehong and Sheng, Jingyi and Ma, Gao and Gao, Zhenyue and Li, Zhenyu and Jia, Zhaochen and Xue, Jinling and Mu, Dan and Sun, Bin and others},
  journal={Journal of Magnetic Resonance Imaging},
  volume={61},
  number={3},
  pages={1515--1518},
  year={2025}
}
@article{si_2024NSR_unveiling,
  title={Unveiling the next generation of MRI contrast agents: current insights and perspectives on ferumoxytol-enhanced MRI},
  author={Si, Guangxiang and Du, Yue and Tang, Peng and Ma, Gao and Jia, Zhaochen and Zhou, Xiaoyue and Mu, Dan and Shen, Yan and Lu, Yi and Mao, Yu and others},
  journal={National Science Review},
  volume={11},
  number={5},
  pages={nwae057},
  year={2024},
}

@inproceedings{shit_2021_CVPR_cldice,
  title={{clDice}: A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation},
  author={Shit, Suprosanna and Paetzold, Johannes C and Sekuboyina, Anjany and Ezhov, Ivan and Unger, Alexander and Zhylka, Andrey and Pluim, Josien PW and Bauer, Ulrich and Menze, Bjoern H},
  booktitle=CVPR,
  pages={16560--16569},
  year={2021}
}
@inproceedings{qi_2023ICCV_snake,
  title={Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation},
  author={Qi, Yaolei and He, Yuting and Qi, Xiaoming and Zhang, Yuan and Yang, Guanyu},
  booktitle=ICCV,
  pages={6070--6079},
  year={2023}
}

@article{guo_2024tmi_3d,
  title={3D vascular segmentation supervised by 2D annotation of maximum intensity projection},
  author={Guo, Zhanqiang and Tan, Zimeng and Feng, Jianjiang and Zhou, Jie},
  journal={IEEE Transactions on Medical Imaging},
  volume={43},
  number={6},
  pages={2241--2253},
  year={2024},
}
@article{fu_2023TMI_robust,
  title={Robust vascular segmentation for raw complex images of laser speckle contrast based on weakly supervised learning},
  author={Fu, Suzhong and Xu, Jing and Chang, Shilong and Yang, Luyao and Ling, Shuting and Cai, Jinghan and Chen, Jiayin and Yuan, Jiacheng and Cai, Ying and Zhang, Bei and others},
  journal={IEEE Transactions on Medical Imaging},
  volume={43},
  number={1},
  pages={39--50},
  year={2023},
}

@article{radl2022Data_avt,
  title={AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks},
  author={Radl, Lukas and Jin, Yuan and Pepe, Antonio and Li, Jianning and Gsaxner, Christina and Zhao, Fen-hua and Egger, Jan},
  journal={Data in Brief},
  volume={40},
  pages={107801},
  year={2022},
}

@inproceedings{kang2025miccaiw_towards,
  title={Towards Affordable Tumor Segmentation and Visualization for 3D Breast MRI Using SAM2},
  author={Kang, Solha and Kim, Eugene and Vankerschaver, Joris and Ozbulak, Utku},
  booktitle=MICCAIW,
  pages={81--90},
  year={2025},
}

@inproceedings{magg2025midl_zero,
  title={Zero-shot capability of 2D SAM-family models for bone segmentation in CT scans},
  author={Magg, Caroline and Kervadec, Hoel and S{\'a}nchez, Clara I},
  booktitle=MIDL,
  year={2025}
}

@article{ma2025_medsam2,
  title={{MedSAM2}: Segment Anything in 3D Medical Images and Videos},
  author={Ma, Jun and Yang, Zongxin and Kim, Sumin and Chen, Bihui and Baharoon, Mohammed and Fallahpour, Adibvafa and Asakereh, Reza and Lyu, Hongwei and Wang, Bo},
  journal={arXiv preprint arXiv:2504.03600},
  year={2025}
}

@article{chen2025NPJDM_pam,
  title={PAM: a propagation-based model for segmenting any 3D objects across multi-modal medical images},
  author={Chen, Zifan and Nan, Xinyu and Li, Jiazheng and Zhao, Jie and Li, Haifeng and Lin, Ziling and Li, Haoshen and Chen, Heyun and Liu, Yiting and Tang, Lei and others},
  journal={npj Digital Medicine},
  year={2025},
  publisher={Nature Publishing Group UK London}
}

@article{archit2025NM_usam,
  title={Segment anything for microscopy},
  author={Archit, Anwai and Freckmann, Luca and Nair, Sushmita and Khalid, Nabeel and Hilt, Paul and Rajashekar, Vikas and Freitag, Marei and Teuber, Carolin and Spitzner, Melanie and Tapia Contreras, Constanza and others},
  journal={Nature Methods},
  volume={22},
  number={3},
  pages={579--591},
  year={2025},
}

@article{archit2025TMI_medicosam,
  title={MedicoSAM: Robust Improvement of SAM for Medical Imaging},
  author={Archit, Anwai and Freckmann, Luca and Pape, Constantin},
  journal={IEEE Transactions on Medical Imaging},
  year={2025},
  publisher={IEEE}
}

@inproceedings{zhang2025dcm,
  title={Rethinking RoI Strategy in Interactive 3D Segmentation for Medical Images},
  author={Ziyu Zhang and Yi Yu and Yuan Xue},
  booktitle={Proceedings of the CVPR 2025 Challenge on Foundation Models for 3D Biomedical Image Segmentation},
  year={2025},
}

@article{zhang2025atlas,
  title={Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation},
  author={Ziyu Zhang and Yi Yu and Simeng Zhu and Ahmed Aly and Yunhe Gao and Ning Gu and Yuan Xue},
  journal={arXiv preprint arXiv:2512.18176},
  year={2025},
}

