mlsanityData to 14 Oct 2026

Text-Promptable Propagation for Referring Medical Image Sequence Segmentation

arXiv 2502.11093 ↗First posted 16 Feb 2025cs.CVcited by 4

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Abstract (arXiv)

Referring Medical Image Sequence Segmentation (Ref-MISS) is a novel and challenging task that aims to segment anatomical structures in medical image sequences (e.g. endoscopy, ultrasound, CT, and MRI) based on natural language descriptions. This task holds significant clinical potential and offers a user-friendly advancement in medical imaging interpretation. Existing 2D and 3D segmentation models struggle to explicitly track objects of interest across medical image sequences, and lack support for nteractive, text-driven guidance. To address these limitations, we propose Text-Promptable Propagation (TPP), a model designed for referring medical image sequence segmentation. TPP captures the intrinsic relationships among sequential images along with their associated textual descriptions. Specifically, it enables the recognition of referred objects through cross-modal referring interaction, and maintains continuous tracking across the sequence via Transformer-based triple propagation, using text embeddings as queries. To support this task, we curate a large-scale benchmark, Ref-MISS-Bench, which covers 4 imaging modalities and 20 different organs and lesions. Experimental results on this benchmark demonstrate that TPP consistently outperforms state-of-the-art methods in both medical segmentation and referring video object segmentation.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 29 of its 61 references that are in the map. First posted on arXiv 16 Feb 2025.

Builds on

29 of 61 references.

References · 29

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17 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Multimodal Referring Segmentation: A Surveycited by 22
  2. 2026B-GRTO: Bootstrapped Group Relative Tool Optimization for Referring Segmentationcited by 1
  3. 2026An Effective Router for Vision-Language Model Selectioncited by 1
  4. 2025Medical Referring Image Segmentation via Next-Token Mask Predictioncited by 0