提出乳腺MRI多标签分割模型，精准识别九类组织结构。
BreastSegNet: Multi-label Segmentation of Breast MRI
- 基于专家标注的1123张图像，构建覆盖九类组织的乳腺MRI数据集。
- nnU-Net ResEncM在全部标签上平均Dice达0.694，心肝等关键组织接近0.90。
- 首次实现乳腺全组织多标签分割，适合临床定量分析与医学影像研究者。
乳腺MRI提供高分辨率成像，对乳腺癌筛查和术前分期至关重要。然而现有分割方法范围有限，通常仅关注纤维腺体组织或肿瘤等少数解剖结构，无法覆盖扫描中所有可见组织，限制了定量分析的应用。本研究提出BreastSegNet，一种针对乳腺MRI的多标签分割算法，涵盖九个解剖标签：纤维腺体组织（FGT）、血管、肌肉、骨骼、病灶、淋巴结、心脏、肝脏和植入物。我们手动标注了1123张MRI切片，经专家放射科医生详细审阅与修正。同时，对比了九种分割模型，包括U-Net、SwinUNet、UNet++、SAM、MedSAM以及采用多种ResNet编码器的nnU-Net。其中，nnU-Net ResEncM在所有标签上取得最高平均Dice分数0.694；在心脏、肝脏、肌肉、FGT和骨骼上表现优异，Dice超过0.73，心脏和肝脏接近0.90。所有模型代码与权重均已公开，数据集计划后续发布。
原文摘要 · Abstract (English)
Breast MRI provides high-resolution imaging critical for breast cancer screening and preoperative staging. However, existing segmentation methods for breast MRI remain limited in scope, often focusing on only a few anatomical structures, such as fibroglandular tissue or tumors, and do not cover the full range of tissues seen in scans. This narrows their utility for quantitative analysis. In this study, we present BreastSegNet, a multi-label segmentation algorithm for breast MRI that covers nine anatomical labels: fibroglandular tissue (FGT), vessel, muscle, bone, lesion, lymph node, heart, liver, and implant. We manually annotated a large set of 1123 MRI slices capturing these structures with detailed review and correction from an expert radiologist. Additionally, we benchmark nine segmentation models, including U-Net, SwinUNet, UNet++, SAM, MedSAM, and nnU-Net with multiple ResNet-based encoders. Among them, nnU-Net ResEncM achieves the highest average Dice scores of 0.694 across all labels. It performs especially well on heart, liver, muscle, FGT, and bone, with Dice scores exceeding 0.73, and approaching 0.90 for heart and liver. All model code and weights are publicly available, and we plan to release the data at a later date.
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