Automated classification of multiphoton microscopy images of ovarian tissue using deep learning

作者：Huttunen Mikko J^*; Hassan Abdurahman; McCloskey Curtis W; Fasih Sijyl; Upham Jeremy; Vanderhyden Barbara C; Boyd Robert W; Murugkar Sangeeta

来源：Journal of Biomedical Optics, 2018, 23(6): 066002.

DOI：10.1117/1.JBO.23.6.066002

• medical and biological imaging
• ovarian cancer
• optical pathology
• tissue characterization
• nonlinear microscopy
• convolutional neural networks

Histopathological image analysis of stained tissue slides is routinely used in tumor detection and classification. However, diagnosis requires a highly trained pathologist and can thus be time-consuming, labor-intensive, and potentially risk bias. Here, we demonstrate a potential complementary approach for diagnosis. We show that multiphoton microscopy images from unstained, reproductive tissues can be robustly classified using deep learning techniques. We fine-train four pretrained convolutional neural networks using over 200 murine tissue images based on combined second-harmonic generation and two-photon excitation fluorescence contrast, to classify the tissues either as healthy or associated with high-grade serous carcinoma with over 95% sensitivity and 97% specificity. Our approach shows promise for applications involving automated disease diagnosis. It could also be readily applied to other tissues, diseases, and related classification problems.

• 出版日期2018-6