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Applying Deep Learning to Quantify Empty Lacunae in Histologic Sections of Osteonecrosis of the Femoral Head. Journal of orthopaedic research : official publication of the Orthopaedic Research Society Lui, E., Maruyama, M., Guzman, R. A., Moeinzadeh, S., Pan, C., Pius, A. K., Quig, M. S., Wong, L. E., Goodman, S. B., Yang, Y. P. 2021

Abstract

Osteonecrosis of the femoral head (ONFH) is a disease in which inadequate blood supply to the subchondral bone causes death of cells in the bone marrow. Decalcified histology and assessment of the percentage of empty lacunae are used to quantify the severity of ONFH. However, the current clinical practice of manually counting cells is a tedious and inefficient process. We utilized the power of artificial intelligence by training an established deep convolutional neural network framework, Faster-RCNN, to automatically classify and quantify osteocytes (healthy and pyknotic) and empty lacunae in 135 histology images. The adjusted correlation coefficient between the trained cell classifier and the ground truth was R = 0.98. The methods detailed in this work significantly reduced the manual effort of cell counting in ONFH histological samples and can be translated to other fields of image quantification. This article is protected by copyright. All rights reserved.

View details for DOI 10.1002/jor.25201

View details for PubMedID 34676596