We present an Expectation-Maximization (EM) Regularized Deep Learning (EMReDL) model for the weakly supervised tumor segmentation. The proposed framework was tailored to glioblastoma, a type of malignant tumor characterized by its diffuse infiltration into the surrounding brain tissue, which poses significant challenge to treatment target and tumor burden estimation based on conventional structural MRI. Although physiological MRI can provide more specific information regarding tumor infiltration, the relatively low resolution hinders a precise full annotation. This has motivated us to develop a weakly supervised deep learning solution that exploits the partial labelled tumor regions. EMReDL contains two components: a physiological prior prediction model and EM-regularized segmentation model. The physiological prior prediction model exploits the physiological MRI by training a classifier to generate a physiological prior map. This map was passed to the segmentation model for regularization using the EM algorithm. We evaluated the model on a glioblastoma dataset with the available pre-operative multiparametric MRI and recurrence MRI. EMReDL was shown to effectively segment the infiltrated tumor from the partially labelled region of potential infiltration. The segmented core and infiltrated tumor showed high consistency with the tumor burden labelled by experts. The performance comparison showed that EMReDL achieved higher accuracy than published state-of-the-art models. On MR spectroscopy, the segmented region showed more aggressive features than other partial labelled region. The proposed model can be generalized to other segmentation tasks with partial labels, with the CNN architecture flexible in the framework.

中文翻译：

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期望最大化的正规化深度学习用于胶质母细胞瘤的弱监督肿瘤分割

我们为弱监督的肿瘤分割提出了期望最大化（EM）正规化深度学习（EMReDL）模型。拟议的框架是针对胶质母细胞瘤量身定制的，它是一种恶性肿瘤，其特征是其弥散性浸润到周围的脑组织中，这对基于常规结构MRI的治疗目标和肿瘤负荷估算提出了重大挑战。尽管生理MRI可以提供有关肿瘤浸润的更多特定信息，但是相对较低的分辨率会妨碍精确的完整注释。这激励了我们开发一种弱监督的深度学习解决方案，该解决方案利用了部分标记的肿瘤区域。EMReDL包含两个组件：生理先验预测模型和EM规范化的分割模型。生理先验预测模型通过训练分类器以生成生理先验图来利用生理MRI。该图被传递到分割模型以使用EM算法进行正则化。我们在胶质母细胞瘤数据集上通过可用的术前多参数MRI和复发MRI对模型进行了评估。EMReDL已显示可从潜在浸润的部分标记区域有效分割浸润的肿瘤。分割的核心和浸润性肿瘤与专家标记的肿瘤负担显示出高度一致性。性能比较表明，EMReDL比已发布的最新模型具有更高的准确性。在MR光谱学上，分割区域显示出比其他部分标记区域更具侵略性的特征。