One-Shot Medical Video Object Segmentation via Temporal Contrastive Memory Networks

• URL: http://arxiv.org/abs/2503.14979v1
• Date: Wed, 19 Mar 2025 08:17:48 GMT
• Title: One-Shot Medical Video Object Segmentation via Temporal Contrastive Memory Networks
• Authors: Yaxiong Chen, Junjian Hu, Chunlei Li, Zixuan Zheng, Jingliang Hu, Yilei Shi, Shengwu Xiong, Xiao Xiang Zhu, Lichao Mou,
• Abstract summary: One-shot medical video object segmentation requires separating foreground and background pixels throughout a video given only the mask annotation of the first frame.<n>To address this problem, we propose a temporal contrastive memory network that aligns embeddings from adjacent frames while pushing apart distant ones to explicitly model inter-frame relationships.<n>We also collect a diverse, multi-source medical video dataset spanning various modalities and anatomies to benchmark this task.
• Score: 30.811519197333993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video object segmentation is crucial for the efficient analysis of complex medical video data, yet it faces significant challenges in data availability and annotation. We introduce the task of one-shot medical video object segmentation, which requires separating foreground and background pixels throughout a video given only the mask annotation of the first frame. To address this problem, we propose a temporal contrastive memory network comprising image and mask encoders to learn feature representations, a temporal contrastive memory bank that aligns embeddings from adjacent frames while pushing apart distant ones to explicitly model inter-frame relationships and stores these features, and a decoder that fuses encoded image features and memory readouts for segmentation. We also collect a diverse, multi-source medical video dataset spanning various modalities and anatomies to benchmark this task. Extensive experiments demonstrate state-of-the-art performance in segmenting both seen and unseen structures from a single exemplar, showing ability to generalize from scarce labels. This highlights the potential to alleviate annotation burdens for medical video analysis. Code is available at https://github.com/MedAITech/TCMN.

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