Text-promptable Propagation for Referring Medical Image Sequence Segmentation

• URL: http://arxiv.org/abs/2502.11093v1
• Date: Sun, 16 Feb 2025 12:13:11 GMT
• Title: Text-promptable Propagation for Referring Medical Image Sequence Segmentation
• Authors: Runtian Yuan, Jilan Xu, Mohan Chen, Qingqiu Li, Yuejie Zhang, Rui Feng, Tao Zhang, Shang Gao,
• Abstract summary: Referring Medical Image Sequence aims to segment the referred anatomical entities corresponding to medical text prompts. TPP supports the segmentation of arbitrary objects of interest based on cross-modal prompt fusion. We curate a large and comprehensive benchmark covering 4 modalities and 20 different organs and lesions.
• Score: 18.633874947279168
• License:
• Abstract: Medical image sequences, generated by both 2D video-based examinations and 3D imaging techniques, consist of sequential frames or slices that capture the same anatomical entities (e.g., organs or lesions) from multiple perspectives. Existing segmentation studies typically process medical images using either 2D or 3D methods in isolation, often overlooking the inherent consistencies among these images. Additionally, interactive segmentation, while highly beneficial in clinical scenarios, faces the challenge of integrating text prompts effectively across multi-modalities. To address these issues, we introduce an innovative task, Referring Medical Image Sequence Segmentation for the first time, which aims to segment the referred anatomical entities corresponding to medical text prompts. We develop a strong baseline model, Text-Promptable Propagation (TPP), designed to exploit the intrinsic relationships among sequential images and their associated textual descriptions. TPP supports the segmentation of arbitrary objects of interest based on cross-modal prompt fusion. Carefully designed medical prompts are fused and employed as queries to guide image sequence segmentation through triple-propagation. We curate a large and comprehensive benchmark covering 4 modalities and 20 different organs and lesions. Experimental results consistently demonstrate the superior performance of our approach compared to previous methods across these datasets.

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