Faculty Collaboration Database

Generative Adversarial Domain Adaptation for Nucleus Quantification in Images of Tissue Immunohistochemically Stained for Ki-67. JCO Clin Cancer Inform 2020 Jul;4:666-679

Date

07/31/2020

Pubmed ID

32730116

Pubmed Central ID

PMC7397778

DOI

10.1200/CCI.19.00108

Scopus ID

2-s2.0-85088885411 (requires institutional sign-in at Scopus site)   10 Citations

Abstract

PURPOSE: We focus on the problem of scarcity of annotated training data for nucleus recognition in Ki-67 immunohistochemistry (IHC)-stained pancreatic neuroendocrine tumor (NET) images. We hypothesize that deep learning-based domain adaptation is helpful for nucleus recognition when image annotations are unavailable in target data sets.

METHODS: We considered 2 different institutional pancreatic NET data sets: one (ie, source) containing 38 cases with 114 annotated images and the other (ie, target) containing 72 cases with 20 annotated images. The gold standards were manually annotated by 1 pathologist. We developed a novel deep learning-based domain adaptation framework to count different types of nuclei (ie, immunopositive tumor, immunonegative tumor, nontumor nuclei). We compared the proposed method with several recent fully supervised deep learning models, such as fully convolutional network-8s (FCN-8s), U-Net, fully convolutional regression network (FCRN) A, FCRNB, and fully residual convolutional network (FRCN). We also evaluated the proposed method by learning with a mixture of converted source images and real target annotations.

RESULTS: Our method achieved an F₁ score of 81.3% and 62.3% for nucleus detection and classification in the target data set, respectively. Our method outperformed FCN-8s (53.6% and 43.6% for nucleus detection and classification, respectively), U-Net (61.1% and 47.6%), FCRNA (63.4% and 55.8%), and FCRNB (68.2% and 60.6%) in terms of F₁ score and was competitive with FRCN (81.7% and 70.7%). In addition, learning with a mixture of converted source images and only a small set of real target labels could further boost the performance.

CONCLUSION: This study demonstrates that deep learning-based domain adaptation is helpful for nucleus recognition in Ki-67 IHC stained images when target data annotations are not available. It would improve the applicability of deep learning models designed for downstream supervised learning tasks on different data sets.

Author List

Zhang X, Cornish TC, Yang L, Bennett TD, Ghosh D, Xing F

Author

Toby Charles Cornish MD, PhD Professor in the Pathology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Humans
Immunohistochemistry
Ki-67 Antigen