School of Biomedical Engineering, Western University, London, ON, Canada.
Digital Image Group (DIG), London, ON, Canada.
Med Image Anal. 2021 May;70:102005. doi: 10.1016/j.media.2021.102005. Epub 2021 Feb 18.
Accurate liver tumor segmentation without contrast agents (non-enhanced images) avoids the contrast-agent-associated time-consuming and high risk, which offers radiologists quick and safe assistance to diagnose and treat the liver tumor. However, without contrast agents enhancing, the tumor in liver images presents low contrast and even invisible to naked eyes. Thus the liver tumor segmentation from non-enhanced images is quite challenging. We propose a Weakly-Supervised Teacher-Student network (WSTS) to address the liver tumor segmentation in non-enhanced images by leveraging additional box-level-labeled data (labeled with a tumor bounding-box). WSTS deploys a weakly-supervised teacher-student framework (TCH-ST), namely, a Teacher Module learns to detect and segment the tumor in enhanced images during training, which facilitates a Student Module to detect and segment the tumor in non-enhanced images independently during testing. To detect the tumor accurately, the WSTS proposes a Dual-strategy DRL (DDRL), which develops two tumor detection strategies by creatively introducing a relative-entropy bias in the DRL. To accurately predict a tumor mask for the box-level-labeled enhanced image and thus improve tumor segmentation in non-enhanced images, the WSTS proposes an Uncertainty-Sifting Self-Ensembling (USSE). The USSE exploits the weakly-labeled data with self-ensembling and evaluates the prediction reliability with a newly-designed Multi-scale Uncertainty-estimation. WSTS is validated with a 2D MRI dataset, where the experiment achieves 83.11% of Dice and 85.12% of Recall in 50 patient testing data after training by 200 patient data (half amount data is box-level-labeled). Such a great result illustrates the competence of WSTS to segment the liver tumor from non-enhanced images. Thus, WSTS has excellent potential to assist radiologists by liver tumor segmentation without contrast-agents.
无对比剂（非增强图像）的肝脏肿瘤精准分割避免了与对比剂相关的耗时和高风险，为放射科医生提供了快速、安全的诊断和治疗肝脏肿瘤的辅助手段。然而，由于缺乏对比剂增强，肝脏图像中的肿瘤对比度较低，甚至肉眼无法观察到。因此，从非增强图像中进行肝脏肿瘤分割极具挑战性。我们提出了一种弱监督的师生网络（WSTS），通过利用额外的盒级标记数据（标记有肿瘤边界框）来解决非增强图像中的肝脏肿瘤分割问题。WSTS 采用了一种弱监督的师生框架（TCH-ST），即教师模块在训练过程中学习检测和分割增强图像中的肿瘤，从而使学生模块在测试过程中能够独立检测和分割非增强图像中的肿瘤。为了准确地检测肿瘤，WSTS 提出了一种双策略 DRL（DDRL），通过在 DRL 中创造性地引入相对熵偏差，开发了两种肿瘤检测策略。为了准确地预测盒级标记增强图像的肿瘤掩模，从而提高非增强图像中的肿瘤分割，WSTS 提出了一种不确定性筛选自集成（USSE）。USSE 利用自集成方法处理弱标记数据，并使用新设计的多尺度不确定性估计来评估预测可靠性。在一个 2D MRI 数据集上进行了验证，在对 200 个患者数据（一半为盒级标记）进行训练后，在 50 个患者测试数据中，WSTS 的 Dice 得分为 83.11%，召回率为 85.12%。这样的优异结果表明，WSTS 有能力从非增强图像中分割肝脏肿瘤。因此，WSTS 有潜力通过无对比剂的肝脏肿瘤分割来协助放射科医生。
