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J. Imaging 2018, 4(1), 20; doi:10.3390/jimaging4010020

Glomerulus Classification and Detection Based on Convolutional Neural Networks

Electrical Engineering Department, University of Castilla La Mancha, Ciudad Real 13071, Spain
TissueGnostics GmbH, Vienna 1020, Austria
Hospital General Universitario, Ciudad Real 13005, Spain
Vilnius University Hospital Santariskes Clinics and Vilnius University, Vilnius 08406, Lithuania
This paper is an extended version of our paper published in: Pedraza A., Gallego J., Lopez S., Gonzalez L., Laurinavicius A., Bueno G. (2017) Glomerulus Classification with Convolutional Neural Networks. In: Valdés Hernández M., González-Castro V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol. 723. Springer, Cham.
Author to whom correspondence should be addressed.
Received: 6 November 2017 / Revised: 2 January 2018 / Accepted: 8 January 2018 / Published: 16 January 2018
(This article belongs to the Special Issue Selected Papers from “MIUA 2017”)
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Glomerulus classification and detection in kidney tissue segments are key processes in nephropathology used for the correct diagnosis of the diseases. In this paper, we deal with the challenge of automating Glomerulus classification and detection from digitized kidney slide segments using a deep learning framework. The proposed method applies Convolutional Neural Networks (CNNs) between two classes: Glomerulus and Non-Glomerulus, to detect the image segments belonging to Glomerulus regions. We configure the CNN with the public pre-trained AlexNet model and adapt it to our system by learning from Glomerulus and Non-Glomerulus regions extracted from training slides. Once the model is trained, labeling is performed by applying the CNN classification to the image blocks under analysis. The results of the method indicate that this technique is suitable for correct Glomerulus detection in Whole Slide Images (WSI), showing robustness while reducing false positive and false negative detections. View Full-Text
Keywords: Glomerulus classification; Glomerulus detection; digital pathology; Convolutional Neural Networks Glomerulus classification; Glomerulus detection; digital pathology; Convolutional Neural Networks

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Gallego, J.; Pedraza, A.; Lopez, S.; Steiner, G.; Gonzalez, L.; Laurinavicius, A.; Bueno, G. Glomerulus Classification and Detection Based on Convolutional Neural Networks. J. Imaging 2018, 4, 20.

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