CT Male Pelvic Organ Segmentation via Hybrid Loss Network With Incomplete Annotation.
CT Male Pelvic Organ Segmentation via Hybrid Loss Network With Incomplete Annotation.
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DOI：
10.1109/tmi.2020.2966389
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发表时间：
2020-06
影响因子：
10.6
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Wang S;Nie D;Qu L;Shao Y;Lian J;Wang Q;Shen D
足够的数据和完整的注释对于训练深度模型对 CT 男性盆腔器官进行自动、准确的分割至关重要，特别是当这些数据面临对比度低、形状变化大等巨大挑战时。然而，手动标注在财务和人力方面都非常昂贵，这通常会导致实际应用中标注的数据不够完整。为此，我们提出了一种新颖的深度框架来分割 CT 图像中的男性盆腔器官，并以非常用户友好的方式描绘不完整的注释。具体来说，我们设计了一个源自体素分类和边界回归的混合损失网络，以迭代的方式共同提高器官分割性能。此外，我们引入了标签完成策略来完成丰富的未注释体素的标签，然后将它们嵌入到训练数据中以增强模型能力。为了降低计算复杂度并提高分割性能，我们根据显着骨骼结构定位骨盆区域，以关注候选分割器官。在大型规划 CT 盆腔器官数据集上的实验结果表明，我们提出的不完整注释方法实现了与具有完整注释的最先进方法相当的分割性能。此外，我们提出的方法需要医疗专业人员手动绘制轮廓的工作量少得多，因此可以更容易地建立机构特定模型。
Sufficient data with complete annotation is essential for training deep models to perform automatic and accurate segmentation of CT male pelvic organs, especially when such data is with great challenges such as low contrast and large shape variation. However, manual annotation is expensive in terms of both finance and human effort, which usually results in insufficient completely annotated data in real applications. To this end, we propose a novel deep framework to segment male pelvic organs in CT images with incomplete annotation delineated in a very user-friendly manner. Specifically, we design a hybrid loss network derived from both voxel classification and boundary regression, to jointly improve the organ segmentation performance in an iterative way. Moreover, we introduce a label completion strategy to complete the labels of the rich unannotated voxels and then embed them into the training data to enhance the model capability. To reduce the computation complexity and improve segmentation performance, we locate the pelvic region based on salient bone structures to focus on the candidate segmentation organs. Experimental results on a large planning CT pelvic organ dataset show that our proposed method with incomplete annotation achieves comparable segmentation performance to the state-of-the-art methods with complete annotation. Moreover, our proposed method requires much less effort of manual contouring from medical professionals such that an institutional specific model can be more easily established.