Benchmarking Wilms' tumor in multisequence MRI data: why does current clinical practice fail? Which popular segmentation algorithms perform well?
Benchmarking Wilms' tumor in multisequence MRI data: why does current clinical practice fail? Which popular segmentation algorithms perform well?
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DOI：
10.1117/1.jmi.6.3.034001
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发表时间：
2019-07-01
影响因子：
2.4
通讯作者：
Graf, Norbert
中科院分区：
文献类型：
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作者：
Mueller, Sabine;Farag, Iva;Graf, Norbert
肾母细胞瘤是儿童最常见的恶性实体瘤之一。肿瘤组织的准确分割是治疗和治疗计划中的关键步骤。由于很难获得一套全面的儿童肿瘤数据，到目前为止还没有基准可以评估人类或基于计算机的分割的质量。我们在论文中的贡献有三个：(I)我们提出了第一个非均匀Wilms‘s肿瘤基准数据集。它包含化疗前和化疗后的多序列MRI数据集，以及基于五位人类专家的共识近似的基本事实注释。(Ii)我们分析了人类专家的注释和评价者之间的可变性，发现目前确定肿瘤体积的临床实践是不准确的，并且化疗后的手动注释可能有很大的不同。(Iii)我们评估了六种基于计算机的分割方法，从经典的方法到最近的深度学习技术。我们表明，最好的注释提供的质量可与人类专家注释相媲美。(C)2019年光学仪器工程师学会(SPIE)
Wilms' tumor is one of the most frequent malignant solid tumors in childhood. Accurate segmentation of tumor tissue is a key step during therapy and treatment planning. Since it is difficult to obtain a comprehensive set of tumor data of children, there is no benchmark so far allowing evaluation of the quality of human or computer-based segmentations. The contributions in our paper are threefold: (i) we present the first heterogeneous Wilms' tumor benchmark data set. It contains multisequence MRI data sets before and after chemotherapy, along with ground truth annotation, approximated based on the consensus of five human experts. (ii) We analyze human expert annotations and interrater variability, finding that the current clinical practice of determining tumor volume is inaccurate and that manual annotations after chemotherapy may differ substantially. (iii) We evaluate six computer-based segmentation methods, ranging from classical approaches to recent deep-learning techniques. We show that the best ones offer a quality comparable to human expert annotations. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)