Structured crowdsourcing enables convolutional segmentation of histology images
Structured crowdsourcing enables convolutional segmentation of histology images
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DOI：
10.1093/bioinformatics/btz083
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发表时间：
2019-09-15
期刊：
影响因子：
5.8
通讯作者：
Cooper, Lee A. D.
中科院分区：
文献类型：
--
作者：
Amgad, Mohamed;Elfandy, Habiba;Cooper, Lee A. D.
动机：虽然深度学习算法在语义图像分割任务中表现出色，但需要大量注释数据集来创建准确的模型。组织学图像的注释具有挑战性，因为需要仔细描绘组织结构的努力和经验，以及与整个幻灯片图像的共享和标记相关的困难。结果：我们招募了25名参与者，从资深病理学家到医学院学生，使用数字幻灯片档案来描绘151张乳腺癌幻灯片的组织区域。系统地评估了参与者之间的不一致性，揭示了肿瘤和间质的低不一致性，以及更主观定义或罕见的组织类别的高不一致性。由高级参与者提供的反馈使20,000 +注释组织区域的生成和管理成为可能。使用这些标注训练的全卷积网络准确率很高（平均AUC=0.945），标注数据的规模显著提高了图像分类的准确率。
Motivation: While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images.Results: We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Interparticipant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy.