FocusSDF：基于符号距离监督的边界感知医学图像分割学习方法
FocusSDF: Boundary-Aware Learning for Medical Image Segmentation via Signed Distance Supervision
AI总结：
针对医学图像分割中边界保持的难题，提出基于符号距离函数的边界感知损失函数FocusSDF，多数据集多任务实验验证其性能优于现有距离变换类损失函数。
摘要：
医学图像分割是医学图像分析的核心组成部分，为临床实践中的精准诊断和高效治疗干预提供基础。尽管现有研究已取得显著进展，但多数分割模型未显式编码边界信息，因此边界保持始终是医学图像分割领域的长期难题。为解决该问题，我们提出FocusSDF，这是一种基于符号距离函数（SDFs）的新型损失函数，通过为更靠近病灶或器官边界的像素自适应分配更高权重，引导网络聚焦于边界区域，有效实现边界感知能力。为严格验证FocusSDF的效果，我们在涵盖脑动脉瘤、中风、肝脏、乳腺肿瘤分割任务、跨多种成像模态的多类数据集上，搭配四种基于距离的损失函数，将其与包括基础模型MedSAM在内的五种当前最优医学图像分割模型开展了大量对比评估。实验结果一致表明，FocusSDF的性能优于现有基于距离变换的损失函数。
英文摘要：
Segmentation of medical images constitutes an essential component of medical image analysis, providing the foundation for precise diagnosis and efficient therapeutic interventions in clinical practices. Despite substantial progress, most segmentation models do not explicitly encode boundary information; as a result, making boundary preservation a persistent challenge in medical image segmentation. To address this challenge, we introduce FocusSDF, a novel loss function based on the signed distance functions (SDFs), which redirects the network to concentrate on boundary regions by adaptively assigning higher weights to pixels closer to the lesion or organ boundary, effectively making it boundary aware. To rigorously validate FocusSDF, we perform extensive evaluations against five state-of-the-art medical image segmentation models, including the foundation model MedSAM, using four distance-based loss functions across diverse datasets covering cerebral aneurysm, stroke, liver, and breast tumor segmentation tasks spanning multiple imaging modalities. The experimental results consistently demonstrate the superior performance of FocusSDF over existing distance transform based loss functions.
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