提出新方法精准分割脊髓肿瘤，提升治疗规划准确性。
BATseg: Boundary-aware Multiclass Spinal Cord Tumor Segmentation on 3D MRI Scans
- 基于边界感知损失函数学习肿瘤表面距离场。
- 在653例患者数据上实现四类脊髓肿瘤的高精度分割。
- 首个大规模脊髓肿瘤数据集，适合医学影像研究者使用。
脊髓肿瘤显著增加神经功能障碍和死亡风险。精确的形态学量化（包括大小、位置和类型）有助于优化治疗方案。尽管近期医学图像分割方法表现优异，但主要针对脑肿瘤等较大病灶，忽视了脊髓肿瘤因体积小、位置和形状多样带来的挑战。为此，本文提出BATseg方法，通过新型多类别边界感知损失函数学习肿瘤表面距离场。为验证效果，我们构建了首个大规模脊髓肿瘤数据集，包含653名患者的钆增强T1加权3D MRI扫描，涵盖星形细胞瘤、室管膜瘤、血管母细胞瘤和脊髓型脑膜瘤四种最常见类型。在该数据集及另一公开肾肿瘤数据集上的实验表明，所提方法在多类别肿瘤分割任务中表现更优。
原文摘要 · Abstract (English)
Spinal cord tumors significantly contribute to neurological morbidity and mortality. Precise morphometric quantification, encompassing the size, location, and type of such tumors, holds promise for optimizing treatment planning strategies. Although recent methods have demonstrated excellent performance in medical image segmentation, they primarily focus on discerning shapes with relatively large morphology such as brain tumors, ignoring the challenging problem of identifying spinal cord tumors which tend to have tiny sizes, diverse locations, and shapes. To tackle this hard problem of multiclass spinal cord tumor segmentation, we propose a new method, called BATseg, to learn a tumor surface distance field by applying our new multiclass boundary-aware loss function. To verify the effectiveness of our approach, we also introduce the first and large-scale spinal cord tumor dataset. It comprises gadolinium-enhanced T1-weighted 3D MRI scans from 653 patients and contains the four most common spinal cord tumor types: astrocytomas, ependymomas, hemangioblastomas, and spinal meningiomas. Extensive experiments on our dataset and another public kidney tumor segmentation dataset show that our proposed method achieves superior performance for multiclass tumor segmentation.
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