MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model

• URL: http://arxiv.org/abs/2410.05905v1
• Date: Tue, 8 Oct 2024 11:04:01 GMT
• Title: MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model
• Authors: Yiwen Ye, Ziyang Chen, Jianpeng Zhang, Yutong Xie, Yong Xia,
• Abstract summary: We introduce MedUniSeg, a prompt-driven universal segmentation model for 2D and 3D multi-task segmentation. MedUniSeg employs multiple modal-specific prompts alongside a universal task prompt to accurately characterize the modalities and tasks. We evaluate MedUniSeg on a comprehensive multi-modal upstream dataset consisting of 17 sub-datasets.
• Score: 27.58715707047272
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Universal segmentation models offer significant potential in addressing a wide range of tasks by effectively leveraging discrete annotations. As the scope of tasks and modalities expands, it becomes increasingly important to generate and strategically position task- and modal-specific priors within the universal model. However, existing universal models often overlook the correlations between different priors, and the optimal placement and frequency of these priors remain underexplored. In this paper, we introduce MedUniSeg, a prompt-driven universal segmentation model designed for 2D and 3D multi-task segmentation across diverse modalities and domains. MedUniSeg employs multiple modal-specific prompts alongside a universal task prompt to accurately characterize the modalities and tasks. To generate the related priors, we propose the modal map (MMap) and the fusion and selection (FUSE) modules, which transform modal and task prompts into corresponding priors. These modal and task priors are systematically introduced at the start and end of the encoding process. We evaluate MedUniSeg on a comprehensive multi-modal upstream dataset consisting of 17 sub-datasets. The results demonstrate that MedUniSeg achieves superior multi-task segmentation performance, attaining a 1.2% improvement in the mean Dice score across the 17 upstream tasks compared to nnUNet baselines, while using less than 1/10 of the parameters. For tasks that underperform during the initial multi-task joint training, we freeze MedUniSeg and introduce new modules to re-learn these tasks. This approach yields an enhanced version, MedUniSeg*, which consistently outperforms MedUniSeg across all tasks. Moreover, MedUniSeg surpasses advanced self-supervised and supervised pre-trained models on six downstream tasks, establishing itself as a high-quality, highly generalizable pre-trained segmentation model.

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