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J. Imaging
Volume 4
Issue 1
10.3390/jimaging4010020
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Article

Glomerulus Classification and Detection Based on Convolutional Neural Networks †

by
Jaime Gallego
1,
Anibal Pedraza
1,
Samuel Lopez
1,
Georg Steiner
2,
Lucia Gonzalez
3,
Arvydas Laurinavicius
4 and
Gloria Bueno
1,*
1
Electrical Engineering Department, University of Castilla La Mancha, Ciudad Real 13071, Spain
2
TissueGnostics GmbH, Vienna 1020, Austria
3
Hospital General Universitario, Ciudad Real 13005, Spain
4
Vilnius University Hospital Santariskes Clinics and Vilnius University, Vilnius 08406, Lithuania
*
Author to whom correspondence should be addressed.
†
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.
J. Imaging 2018, 4(1), 20; https://doi.org/10.3390/jimaging4010020
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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Abstract

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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MDPI and ACS Style

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. https://doi.org/10.3390/jimaging4010020

AMA Style

Gallego J, Pedraza A, Lopez S, Steiner G, Gonzalez L, Laurinavicius A, Bueno G. Glomerulus Classification and Detection Based on Convolutional Neural Networks. Journal of Imaging. 2018; 4(1):20. https://doi.org/10.3390/jimaging4010020

Chicago/Turabian Style

Gallego, Jaime, Anibal Pedraza, Samuel Lopez, Georg Steiner, Lucia Gonzalez, Arvydas Laurinavicius, and Gloria Bueno. 2018. "Glomerulus Classification and Detection Based on Convolutional Neural Networks" Journal of Imaging 4, no. 1: 20. https://doi.org/10.3390/jimaging4010020

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