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Structured crowdsourcing enables convolutional segmentation of histology images
Ist Teil von
Bioinformatics, 2019-09, Vol.35 (18), p.3461-3467
Ort / Verlag
England: Oxford University Press
Erscheinungsjahr
2019
Link zum Volltext
Quelle
Free E-Journal (出版社公開部分のみ)
Beschreibungen/Notizen
Abstract
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. Inter-participant 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.
Availability and Implementation
Dataset is freely available at: https://goo.gl/cNM4EL.
Supplementary information
Supplementary data are available at Bioinformatics online.