Epithelium segmentation and automated Gleason grading of prostate cancer via deep learning in label‐free multiphoton microscopic images. Issue 2 (4th December 2019)

• Record Type: Journal Article
• Authors: Yang, Qinqin; Xu, Zhexin; Liao, Chenxi; Cai, Jianyong; Huang, Ying; Chen, Hong; Tao, Xuan; Huang, Zheng; Chen, Jianxin; Dong, Jiyang; Zhu, Xiaoqin
• Is Part Of: Journal of biophotonics. Volume 13:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-04
• Subjects: deep learning -- epithelium segmentation -- Gleason Grading -- multiphoton microscopy -- prostate cancer; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.201900203
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract: In the current clinical care practice, Gleason grading system is one of the most powerful prognostic predictors for prostate cancer (PCa). The grading system is based on the architectural pattern of cancerous epithelium in histological images. However, the standard procedure of histological examination often involves complicated tissue fixation and staining, which are time‐consuming and may delay the diagnosis and surgery. In this study, label‐free multiphoton microscopy (MPM) was used to acquire subcellular‐resolution images of unstained prostate tissues. Then, a deep learning architecture (U‐net) was introduced for epithelium segmentation of prostate tissues in MPM images. The obtained segmentation results were then merged with the original MPM images to train a classification network (AlexNet) for automated Gleason grading. The developed method achieved an overall pixel accuracy of 92.3% with a mean F1 score of 0.839 for epithelium segmentation. By merging the segmentation results with the MPM images, the accuracy of Gleason grading was improved from 72.42% to 81.13% in hold‐out test set. Our results suggest that MPM in combination with deep learning holds the potential to be used as a fast and powerful clinical tool for PCa diagnosis. Abstract : Label‐freely multiphoton microscopy (MPM) is employed to produce subcellular‐resolution images of the unstained prostate tissues. Then, a cascade deep learning architecture is introduced for epithelium segmentation on the MPM images, and automated Gleason grading based on the architectural pattern of cancerous epithelium with segmented result as a priori. This work suggests that MPM, combined with deep learning method, holds the potential to be a powerful tool in clinical diagnosis for prostate cancers.