Automatic labeling of molecular biomarkers on a cell-by-cell basis in immunohistochemistry images using convolutional neural networks

Fahime Sheikhzadeh; Anita Carraro; Jagoda Korbelik; Calum MacAulay; Martial Guillaud; Rabab K. Ward

doi:10.1117/12.2217046

23 March 2016 Automatic labeling of molecular biomarkers on a cell-by-cell basis in immunohistochemistry images using convolutional neural networks

Fahime Sheikhzadeh, Anita Carraro, Jagoda Korbelik, Calum MacAulay, Martial Guillaud, Rabab K. Ward

Author Affiliations +

Proceedings Volume 9791, Medical Imaging 2016: Digital Pathology; 97910R (2016) https://doi.org/10.1117/12.2217046
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

This paper addresses the problem of classifying cells expressing different biomarkers. A deep learning based method that can automatically localize and count the cells expressing each of the different biomarkers is proposed. To classify the cells, a Convolutional Neural Network (CNN) was employed. Images of Immunohistochemistry (IHC) stained slides that contain these cells were digitally scanned. The images were taken from digital scans of IHC stained cervical tissues, acquired for a clinical trial. More than 4,500 RGB images of cells were used to train the CNN. To evaluate our method, the cells were first manually labeled based on the expressing biomarkers. Then we performed the classification on 156 randomly selected images of cells that were not used in training the CNN. The accuracy of the classification was 92% in this preliminary data set. The results have shown that this method has a good potential in developing an automatic method for immunohistochemical analysis.

Citation Download Citation

Fahime Sheikhzadeh, Anita Carraro, Jagoda Korbelik, Calum MacAulay, Martial Guillaud, and Rabab K. Ward "Automatic labeling of molecular biomarkers on a cell-by-cell basis in immunohistochemistry images using convolutional neural networks", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910R (23 March 2016); https://doi.org/10.1117/12.2217046

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