research-article

Fat Droplets Identification in Liver Biopsies using Supervised Learning Techniques

Authors:

Alexandros Arjmand,

Alexandros T. Tzallas,

Markos G. Tsipouras,

Roberta Forlano,

Pinelopi Manousou,

Nikolaos Katertsidis,

Nikolaos GiannakeasAuthors Info & Claims

PETRA '18: Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference

Pages 434 - 440

https://doi.org/10.1145/3197768.3201554

Published: 26 June 2018 Publication History

Abstract

Non-Alcoholic Fatty Liver Disease (NAFLD) is a frequent syndrome that exclusively refers to fat accumulation in liver and steatohepatitis1. It is considered as a massive disease ranging from 20% to 40% in adult populations of the Western World. Its prevalence is related to insulin resistance, which places individuals at high rates of mortality. An increased fat accumulation rate, can significantly increase the development of liver steatosis, which in later stages may progress into fibrosis and cirrhosis. In recent years, research groups focus on the automated fat detection based on histology and digital image processing. The current project, extends our previous work for the detection and quantification of fatty liver, by characterizing histological findings. It is an extensive study of supervised learning of fat droplet features, in order to exclude other findings from fat ratio computation. The method is evaluated on a set of 13 liver biopsy images, performing 92% accuracy.

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Cited By

Arjmand AAngelis CChristou VTzallas ATsipouras MGlavas EForlano RManousou PGiannakeas N(2019)Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image SamplesApplied Sciences10.3390/app1001004210:1(42)Online publication date: 19-Dec-2019
https://doi.org/10.3390/app10010042
Arjmand AGiannakeas N(2018)Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based SystemEngineering, Technology & Applied Science Research10.48084/etasr.22748:6(3550-3555)Online publication date: 22-Dec-2018
https://doi.org/10.48084/etasr.2274

Index Terms

Fat Droplets Identification in Liver Biopsies using Supervised Learning Techniques
1. Computing methodologies
  1. Computer graphics
  2. Machine learning

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cover image ACM Other conferences

PETRA '18: Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference

June 2018

591 pages

ISBN:9781450363907

DOI:10.1145/3197768

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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NSF: National Science Foundation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2018

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PETRA '18

PETRA '18: The 11th PErvasive Technologies Related to Assistive Environments Conference

June 26 - 29, 2018

Corfu, Greece

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Cited By

Arjmand AAngelis CChristou VTzallas ATsipouras MGlavas EForlano RManousou PGiannakeas N(2019)Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image SamplesApplied Sciences10.3390/app1001004210:1(42)Online publication date: 19-Dec-2019
https://doi.org/10.3390/app10010042
Arjmand AGiannakeas N(2018)Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based SystemEngineering, Technology & Applied Science Research10.48084/etasr.22748:6(3550-3555)Online publication date: 22-Dec-2018
https://doi.org/10.48084/etasr.2274

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