Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation Journal Article
| Authors: | Ho, D. J.; Chui, M. H.; Vanderbilt, C. M.; Jung, J.; Robson, M. E.; Park, C. S.; Roh, J.; Fuchs, T. J. |
| Article Title: | Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation |
| Abstract: | Deep learning has been widely used to analyze digitized hematoxylin and eosin (H&E)-stained histopathology whole slide images. Automated cancer segmentation using deep learning can be used to diagnose malignancy and to find novel morphological patterns to predict molecular subtypes. To train pixel-wise cancer segmentation models, manual annotation from pathologists is generally a bottleneck due to its time-consuming nature. In this paper, we propose Deep Interactive Learning with a pretrained segmentation model from a different cancer type to reduce manual annotation time. Instead of annotating all pixels from cancer and non-cancer regions on giga-pixel whole slide images, an iterative process of annotating mislabeled regions from a segmentation model and training/finetuning the model with the additional annotation can reduce the time. Especially, employing a pretrained segmentation model can further reduce the time than starting annotation from scratch. We trained an accurate ovarian cancer segmentation model with a pretrained breast segmentation model by 3.5 hours of manual annotation which achieved intersection-over-union of 0.74, recall of 0.86, and precision of 0.84. With automatically extracted high-grade serous ovarian cancer patches, we attempted to train an additional classification deep learning model to predict BRCA mutation. The segmentation model and code have been released at https://github.com/MSKCC-Computational-Pathology/DMMN-ovary. © 2022 The Authors |
| Keywords: | ovarian cancer; segmentation; annotation; deep learning; computational pathology |
| Journal Title: | Journal of Pathology Informatics |
| Volume: | 14 |
| ISSN: | 2229-5089 |
| Publisher: | Wolters Kluwer - Medknow |
| Date Published: | 2023-01-01 |
| Start Page: | 100160 |
| Language: | English |
| DOI: | 10.1016/j.jpi.2022.100160 |
| PROVIDER: | scopus |
| PMCID: | PMC9758515 |
| PUBMED: | 36536772 |
| DOI/URL: | |
| Notes: | Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PDF -- Export Date: 1 February 2023 -- Source: Scopus |
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