标题： 从解剖学中学习：用于全身MRI转移性骨病分割的监督解剖预训练（SAP） 显示英文标题

标题： Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI

摘要： 在全身磁共振成像（WB-MRI）中对转移性骨病（MBD）进行分割是一个具有挑战性的问题。 由于病灶的外观和解剖位置各不相同，边界模糊以及类别不平衡严重，获得可靠的分割需要包含病灶变异性的大型、标注良好的数据集。 生成这样的数据集需要大量时间和专业知识，并且容易出错。 虽然自监督学习（SSL）可以利用大量的未标记数据集，但学习到的通用表示通常无法捕捉准确检测病灶所需的细微特征。 在本工作中，我们提出了一种监督解剖预训练（SAP）方法，该方法从有限的解剖标签数据集中进行学习。 首先，开发并训练了一个基于MRI的骨骼分割模型，用于健康个体的WB-MRI扫描，以实现高质量的骨骼分割。 然后，我们将它在44名转移性前列腺癌患者的队列中对MBD分割的下游效果，与基线随机初始化和最先进的SSL方法进行了比较。 SAP显著优于基线和SSL预训练模型，达到了归一化表面Dice值0.76和Dice系数0.64。 该方法在病灶检测中的F2得分为0.44，优于基线的0.24和SSL的0.31。 当仅考虑大于1~ml的临床相关病灶时，SAP在32名患者中的28名中实现了100%的检测灵敏度。 从解剖学中学习骨形态可以产生一种有效且与领域相关的归纳偏差，可用于骨病灶的下游分割任务。 所有代码和模型均已公开可用。 显示英文摘要

摘要： The segmentation of metastatic bone disease (MBD) in whole-body MRI (WB-MRI) is a challenging problem. Due to varying appearances and anatomical locations of lesions, ambiguous boundaries, and severe class imbalance, obtaining reliable segmentations requires large, well-annotated datasets capturing lesion variability. Generating such datasets requires substantial time and expertise, and is prone to error. While self-supervised learning (SSL) can leverage large unlabeled datasets, learned generic representations often fail to capture the nuanced features needed for accurate lesion detection. In this work, we propose a Supervised Anatomical Pretraining (SAP) method that learns from a limited dataset of anatomical labels. First, an MRI-based skeletal segmentation model is developed and trained on WB-MRI scans from healthy individuals for high-quality skeletal delineation. Then, we compare its downstream efficacy in segmenting MBD on a cohort of 44 patients with metastatic prostate cancer, against both a baseline random initialization and a state-of-the-art SSL method. SAP significantly outperforms both the baseline and SSL-pretrained models, achieving a normalized surface Dice of 0.76 and a Dice coefficient of 0.64. The method achieved a lesion detection F2 score of 0.44, improving on 0.24 (baseline) and 0.31 (SSL). When considering only clinically relevant lesions larger than 1~ml, SAP achieves a detection sensitivity of 100% in 28 out of 32 patients. Learning bone morphology from anatomy yields an effective and domain-relevant inductive bias that can be leveraged for the downstream segmentation task of bone lesions. All code and models are made publicly available.