利用医生注视轨迹提升医学图像弱监督分割精度
Enjoying Information Dividend: Gaze Track-based Medical Weakly Supervised Segmentation
- 融合注视点、时长和顺序构建注视轨迹图，实现多层级监督
- 在Kvasir-SEG和NCI-ISBI上分别提升3.21%和2.61%的Dice分数
- 适合关注医学图像标注效率与模型性能平衡的研究者
医学图像弱监督语义分割面临稀疏标注难以有效利用的挑战。一种有前景的方向是利用眼动仪记录诊断过程中关注区域的注视数据。然而，现有基于注视的方法（如GazeMedSeg）未能充分挖掘注视数据中的丰富信息。本文提出GradTrack框架，通过整合医生注视轨迹中的固定点、持续时间和时间顺序，增强弱监督分割性能。GradTrack包含两个核心组件：注视轨迹图生成与轨迹注意力机制，在解码过程中协同实现多层级特征渐进式优化。在Kvasir-SEG和NCI-ISBI数据集上的实验表明，GradTrack持续优于现有注视基方法，分别取得3.21%和2.61%的Dice分数提升。此外，其性能显著缩小了与全监督模型nnUNet之间的差距。
原文摘要 · Abstract (English)
Weakly supervised semantic segmentation (WSSS) in medical imaging struggles with effectively using sparse annotations. One promising direction for WSSS leverages gaze annotations, captured via eye trackers that record regions of interest during diagnostic procedures. However, existing gaze-based methods, such as GazeMedSeg, do not fully exploit the rich information embedded in gaze data. In this paper, we propose GradTrack, a framework that utilizes physicians' gaze track, including fixation points, durations, and temporal order, to enhance WSSS performance. GradTrack comprises two key components: Gaze Track Map Generation and Track Attention, which collaboratively enable progressive feature refinement through multi-level gaze supervision during the decoding process. Experiments on the Kvasir-SEG and NCI-ISBI datasets demonstrate that GradTrack consistently outperforms existing gaze-based methods, achieving Dice score improvements of 3.21\% and 2.61\%, respectively. Moreover, GradTrack significantly narrows the performance gap with fully supervised models such as nnUNet.
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