Yucheng Li 1, Xiaofan Wang1, Junyi Wang1, Yijie Li1, Xi Zhu1, Mubai Du1, Dian Sheng1, Wei Zhang1, Fan Zhang1
1University of Electronic Science and Technology of China, Chengdu, China
Presenting Author: Yucheng Li
Synopsis
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1. Fischl B, Salat D H, Busa E, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain[J]. Neuron, 2002, 33(3): 341-355.
2. Huo Y, Xu Z, Xiong Y, et al. 3D whole brain segmentation using spatially localized atlas network tiles[J]. NeuroImage, 2019, 194: 105-119.
3. Makropoulos A, Gousias I S, Ledig C, et al. Automatic whole brain MRI segmentation of the developing neonatal brain[J]. IEEE transactions on medical imaging, 2014, 33(9): 1818-1831.
4. Henschel L, Conjeti S, Estrada S, et al. Fastsurfer-a fast and accurate deep learning based neuroimaging pipeline[J]. NeuroImage, 2020, 219: 117012.
5. Kirillov A, Mintun E, Ravi N, et al. Segment anything[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2023: 4015-4026.
6. Huang Y, Yang X, Liu L, et al. Segment anything model for medical images?[J]. Medical Image Analysis, 2024, 92: 103061.
7. Ma J, He Y, Li F, et al. Segment anything in medical images[J]. Nature Communications, 2024, 15(1): 654.
8. Shi P, Qiu J, Abaxi S M D, et al. Generalist vision foundation models for medical imaging: A case study of segment anything model on zero-shot medical segmentation[J]. Diagnostics, 2023, 13(11): 1947.
9. Zhang P, Wang Y. Segment anything model for brain tumor segmentation[J]. arXiv preprint arXiv:2309.08434, 2023.
10. Ali L, Alnajjar F, Swavaf M, et al. Evaluating segment anything model (SAM) on MRI scans of brain tumors[J]. Scientific reports, 2024, 14(1): 21659.
11. Kaur P, Kaushik A, Singhal I, et al. Advancing Brain MRI Segmentation using Segment Anything Model[J]. Procedia Computer Science, 2025, 260: 110-117.
12. Mazurowski M A, Dong H, Gu H, et al. Segment anything model for medical image analysis: an experimental study[J]. Medical Image Analysis, 2023, 89: 102918.
13. Isensee F, Jaeger P F, Kohl S A A, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nature methods, 2021, 18(2): 203-211.
14. Lian X, Pang Y, Han J, et al. Cascaded hierarchical atrous spatial pyramid pooling module for semantic segmentation[J]. Pattern Recognition, 2021, 110: 107622.
15. Liu Y, Shao Z, Hoffmann N. Global attention mechanism: Retain information to enhance channel-spatial interactions[J]. arXiv preprint arXiv:2112.05561, 2021.
16. Liu Q, Wang Z. Progressive boundary refinement network for temporal action detection[C]//Proceedings of the AAAI conference on artificial intelligence. 2020, 34(07): 11612-11619.
17. Van Essen D C, Ugurbil K, Auerbach E, et al. The Human Connectome Project: a data acquisition perspective[J]. Neuroimage, 2012, 62(4): 2222-2231.
18. Fischl B. FreeSurfer[J]. Neuroimage, 2012, 62(2): 774-781.
19. Henschel L, Conjeti S, Estrada S, et al. Fastsurfer-a fast and accurate deep learning based neuroimaging pipeline[J]. NeuroImage, 2020, 219: 117012.
20. Chen C, Miao J, Wu D, et al. Ma-sam: Modality-agnostic sam adaptation for 3d medical image segmentation[J]. Medical Image Analysis, 2024, 98: 103310.