无需联合标注数据，就能同时精准分割脑部正常组织与病变
Learning to segment anatomy and lesions from disparately labeled sources in brain MRI
- 分路处理健康组织与病变，用注意力机制融合多序列信息
- 在胶质母细胞瘤数据集上，对多个解剖结构和病灶分割更准确
- 适合缺乏联合标注的医学图像分割任务，尤其适用于脑部病变分析
在脑部磁共振成像（MRI）中同时分割健康组织与病灶仍面临挑战，主要因病灶会破坏解剖结构，且缺乏同时标注健康组织与病灶的联合训练数据。本文提出一种新方法，可抵御病灶干扰，并在仅使用非联合标注数据（即不同图像分别标注健康组织或病灶）的情况下进行训练，实现自动分割两者。与以往工作不同，该方法将健康组织与病灶分割任务解耦为两条路径，利用多序列采集信息，并通过注意力机制融合；推理时采用图像自适应机制，降低病灶区域对健康组织预测的负面影响；训练阶段结合元学习与协同训练，充分挖掘分散标注数据。在公开的脑胶质母细胞瘤数据集上，本模型在多个解剖结构及病灶分割上均优于现有最先进方法。
原文摘要 · Abstract (English)
Segmenting healthy tissue structures alongside lesions in brain Magnetic Resonance Images (MRI) remains a challenge for today's algorithms due to lesion-caused disruption of the anatomy and lack of jointly labeled training datasets, where both healthy tissues and lesions are labeled on the same images. In this paper, we propose a method that is robust to lesion-caused disruptions and can be trained from disparately labeled training sets, i.e., without requiring jointly labeled samples, to automatically segment both. In contrast to prior work, we decouple healthy tissue and lesion segmentation in two paths to leverage multi-sequence acquisitions and merge information with an attention mechanism. During inference, an image-specific adaptation reduces adverse influences of lesion regions on healthy tissue predictions. During training, the adaptation is taken into account through meta-learning and co-training is used to learn from disparately labeled training images. Our model shows an improved performance on several anatomical structures and lesions on a publicly available brain glioblastoma dataset compared to the state-of-the-art segmentation methods.
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