Glomerulus Classification with Convolutional Neural Networks

  • Anibal Pedraza
  • Jaime Gallego
  • Samuel Lopez
  • Lucia Gonzalez
  • Arvydas Laurinavicius
  • Gloria Bueno
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 723)

Abstract

Glomerulus classification in kidney tissue segments is a key process in nephropathology to obtain correct diseases diagnosis. In this paper, we deal with the challenge to automate the Glomerulus classification from digitized kidney slide segments using a deep learning framework. The proposed method applies Convolutional Neural Networks (CNNs) classification 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, the labelling is performed applying the CNN classification to the image segments under analysis. The results obtained indicate that this technique is suitable for correct Glomerulus classification, showing robustness while reducing false positive and false negative detections.

Keywords

Glomerulus classification Digital pathology Nephropathology Convolutional neural networks 

Notes

Acknowledgments

This project has received funding from the European Union’s FP7 programme under grant agreement no: 612471. http://aidpath.eu/

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Anibal Pedraza
    • 1
  • Jaime Gallego
    • 1
  • Samuel Lopez
    • 1
  • Lucia Gonzalez
    • 2
  • Arvydas Laurinavicius
    • 3
  • Gloria Bueno
    • 1
  1. 1.University of Castilla La ManchaCiudad RealSpain
  2. 2.Hospital General UniversitarioCiudad RealSpain
  3. 3.Vilnius University Hospital Santariskes Clinics and Vilnius UniversityVilniusLithuania

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