标题： 乳腺分割网络：乳腺MRI的多标签分割 显示英文标题

标题： BreastSegNet: Multi-label Segmentation of Breast MRI

摘要： 乳腺MRI提供了高分辨率成像，对于乳腺癌筛查和术前分期至关重要。 然而，现有的乳腺MRI分割方法在范围上仍然有限，通常只关注少数解剖结构，如纤维腺体组织或肿瘤，并不涵盖扫描中看到的所有组织类型。 这限制了它们在定量分析中的实用性。 在本研究中，我们提出了BreastSegNet，这是一种用于乳腺MRI的多标签分割算法，涵盖了九个解剖标签：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。 我们手动标注了一组包含1123张MRI切片的数据集，并由专家放射科医生进行了详细的审查和修正。 此外，我们对九种分割模型进行了基准测试，包括U-Net、SwinUNet、UNet++、SAM、MedSAM以及使用多种ResNet编码器的nnU-Net。 其中，nnU-Net ResEncM在所有标签上的平均Dice分数达到最高，为0.694。 它在心脏、肝脏、肌肉、FGT和骨骼上的表现尤其出色，Dice分数超过0.73，心脏和肝脏的Dice分数接近0.90。 所有模型代码和权重均可公开获取，我们计划在以后发布数据。 显示英文摘要

摘要： 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.