用解剖先验信息提升淋巴结分割精度，减少性别偏差。
Investigating Gender Bias in Lymph-node Segmentation with Anatomical Priors
- 用器官结构作为解剖先验指导淋巴结分割
- 女性患者腹部区域分割质量显著提升
- 适合关注医学影像公平性的研究者
放疗需要精确分割器官危及组织（OARs）和临床靶区（CTV），以最大化疗效并最小化毒性。尽管深度学习在自动勾画方面取得显著进展，但复杂目标如CTV仍具挑战性。本研究探索利用已良好分割的简单结构（如OARs）作为解剖先验（AP）信息，以改进CTV分割。我们研究了分割模型中的性别偏差及其在引入先验信息后的缓解效果。结果表明，采用所述策略结合先验知识可提升女性患者的分割质量，并显著降低性别偏差，尤其在腹部区域表现突出。本研究对比分析了新的编码策略，凸显了使用解剖先验实现更公平分割结果的潜力。
原文摘要 · Abstract (English)
Radiotherapy requires precise segmentation of organs at risk (OARs) and of the Clinical Target Volume (CTV) to maximize treatment efficacy and minimize toxicity. While deep learning (DL) has significantly advanced automatic contouring, complex targets like CTVs remain challenging. This study explores the use of simpler, well-segmented structures (e.g., OARs) as Anatomical Prior (AP) information to improve CTV segmentation. We investigate gender bias in segmentation models and the mitigation effect of the prior information. Findings indicate that incorporating prior knowledge with the discussed strategies enhances segmentation quality in female patients and reduces gender bias, particularly in the abdomen region. This research provides a comparative analysis of new encoding strategies and highlights the potential of using AP to achieve fairer segmentation outcomes.
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