Scribble-Supervised Medical Image Segmentation with Dynamic Teacher Switching and Hierarchical Consistency

• URL: http://arxiv.org/abs/2601.14563v3
• Date: Fri, 23 Jan 2026 18:54:37 GMT
• Title: Scribble-Supervised Medical Image Segmentation with Dynamic Teacher Switching and Hierarchical Consistency
• Authors: Thanh-Huy Nguyen, Hoang-Loc Cao, Dat T. Chung, Mai-Anh Vu, Thanh-Minh Nguyen, Minh Le, Phat K. Huynh, Ulas Bagci,
• Abstract summary: We propose SDT-Net, a novel dual-teacher, single-student framework to maximize supervision quality from weak signals.<n>Our method features a Dynamic Teacher Switching (DTS) module to adaptively select the most reliable teacher.<n>This selected teacher then guides the student via two synergistic mechanisms: high-confidence pseudo-labels, refined by a Pick Reliable Pixels (PRP) mechanism, and multi-level feature alignment.
• Score: 7.48591408877799
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Scribble-supervised methods have emerged to mitigate the prohibitive annotation burden in medical image segmentation. However, the inherent sparsity of these annotations introduces significant ambiguity, which results in noisy pseudo-label propagation and hinders the learning of robust anatomical boundaries. To address this challenge, we propose SDT-Net, a novel dual-teacher, single-student framework designed to maximize supervision quality from these weak signals. Our method features a Dynamic Teacher Switching (DTS) module to adaptively select the most reliable teacher. This selected teacher then guides the student via two synergistic mechanisms: high-confidence pseudo-labels, refined by a Pick Reliable Pixels (PRP) mechanism, and multi-level feature alignment, enforced by a Hierarchical Consistency (HiCo) module. Extensive experiments on the ACDC and MSCMRseg datasets demonstrate that SDT-Net achieves state-of-the-art performance, producing more accurate and anatomically plausible segmentation.

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