Liver fibrosis and nas scoring from CT images using self-supervised learning and texture encoding

Ananya Jana, Hui Qu, Carlos D. Minacapelli, Carolyn Catalano, Vinod Rustgi, Dimitris Metaxas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver diseases (CLD) which can progress to liver cancer. The severity and treatment of NAFLD is determined by NAFLD Activity Scores (NAS) and liver fibrosis stage, which are usually obtained from liver biopsy. However, biopsy is invasive in nature and involves risk of procedural complications. Current methods to predict the fibrosis and NAS scores from noninvasive CT images rely heavily on either a large annotated dataset or transfer learning using pretrained networks. However, the availability of a large annotated dataset cannot be always ensured and there can be domain shifts when using transfer learning. In this work, we propose a self-supervised learning method to address both problems. As the NAFLD causes changes in the liver texture, we also propose to use texture encoded inputs to improve the performance of the model. Given a relatively small dataset with 30 patients, we employ a self-supervised network which achieves better performance than a network trained via transfer learning. The code is publicly available 1.

Original languageEnglish (US)
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PublisherIEEE Computer Society
Pages1553-1557
Number of pages5
ISBN (Electronic)9781665412469
DOIs
StatePublished - Apr 13 2021
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: Apr 13 2021Apr 16 2021

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2021-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Country/TerritoryFrance
CityNice
Period4/13/214/16/21

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Keywords

  • Deep learning
  • Liver fibrosis
  • Local binary pattern
  • NAS score
  • Self-supervised learning

Fingerprint

Dive into the research topics of 'Liver fibrosis and nas scoring from CT images using self-supervised learning and texture encoding'. Together they form a unique fingerprint.

Cite this