VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912130840657920 |
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| author | He, Yufan Guo, Pengfei Tang, Yucheng Myronenko, Andriy Nath, Vishwesh Xu, Ziyue Yang, Dong Zhao, Can Simon, Benjamin Belue, Mason Harmon, Stephanie Turkbey, Baris Xu, Daguang Li, Wenqi |
| author_facet | He, Yufan Guo, Pengfei Tang, Yucheng Myronenko, Andriy Nath, Vishwesh Xu, Ziyue Yang, Dong Zhao, Can Simon, Benjamin Belue, Mason Harmon, Stephanie Turkbey, Baris Xu, Daguang Li, Wenqi |
| contents | Foundation models for interactive segmentation in 2D natural images and videos have sparked significant interest in building 3D foundation models for medical imaging. However, the domain gaps and clinical use cases for 3D medical imaging require a dedicated model that diverges from existing 2D solutions. Specifically, such foundation models should support a full workflow that can actually reduce human effort. Treating 3D medical images as sequences of 2D slices and reusing interactive 2D foundation models seems straightforward, but 2D annotation is too time-consuming for 3D tasks. Moreover, for large cohort analysis, it's the highly accurate automatic segmentation models that reduce the most human effort. However, these models lack support for interactive corrections and lack zero-shot ability for novel structures, which is a key feature of "foundation". While reusing pre-trained 2D backbones in 3D enhances zero-shot potential, their performance on complex 3D structures still lags behind leading 3D models. To address these issues, we present VISTA3D, Versatile Imaging SegmenTation and Annotation model, that targets to solve all these challenges and requirements with one unified foundation model. VISTA3D is built on top of the well-established 3D segmentation pipeline, and it is the first model to achieve state-of-the-art performance in both 3D automatic (supporting 127 classes) and 3D interactive segmentation, even when compared with top 3D expert models on large and diverse benchmarks. Additionally, VISTA3D's 3D interactive design allows efficient human correction, and a novel 3D supervoxel method that distills 2D pretrained backbones grants VISTA3D top 3D zero-shot performance. We believe the model, recipe, and insights represent a promising step towards a clinically useful 3D foundation model. Code and weights are publicly available at https://github.com/Project-MONAI/VISTA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05285 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging He, Yufan Guo, Pengfei Tang, Yucheng Myronenko, Andriy Nath, Vishwesh Xu, Ziyue Yang, Dong Zhao, Can Simon, Benjamin Belue, Mason Harmon, Stephanie Turkbey, Baris Xu, Daguang Li, Wenqi Computer Vision and Pattern Recognition Foundation models for interactive segmentation in 2D natural images and videos have sparked significant interest in building 3D foundation models for medical imaging. However, the domain gaps and clinical use cases for 3D medical imaging require a dedicated model that diverges from existing 2D solutions. Specifically, such foundation models should support a full workflow that can actually reduce human effort. Treating 3D medical images as sequences of 2D slices and reusing interactive 2D foundation models seems straightforward, but 2D annotation is too time-consuming for 3D tasks. Moreover, for large cohort analysis, it's the highly accurate automatic segmentation models that reduce the most human effort. However, these models lack support for interactive corrections and lack zero-shot ability for novel structures, which is a key feature of "foundation". While reusing pre-trained 2D backbones in 3D enhances zero-shot potential, their performance on complex 3D structures still lags behind leading 3D models. To address these issues, we present VISTA3D, Versatile Imaging SegmenTation and Annotation model, that targets to solve all these challenges and requirements with one unified foundation model. VISTA3D is built on top of the well-established 3D segmentation pipeline, and it is the first model to achieve state-of-the-art performance in both 3D automatic (supporting 127 classes) and 3D interactive segmentation, even when compared with top 3D expert models on large and diverse benchmarks. Additionally, VISTA3D's 3D interactive design allows efficient human correction, and a novel 3D supervoxel method that distills 2D pretrained backbones grants VISTA3D top 3D zero-shot performance. We believe the model, recipe, and insights represent a promising step towards a clinically useful 3D foundation model. Code and weights are publicly available at https://github.com/Project-MONAI/VISTA. |
| title | VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.05285 |