Computer-Aided Diagnosis and Quantification of Cirrhotic Livers Based on Morphological Analysis and Machine Learning

Yen Wei Chen, Jie Luo, Chunhua Dong, Xianhua Han, Tomoko Tateyama, Akira Furukawa, Shuzo Kanasaki

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

It is widely known that morphological changes of the liver and the spleen occur during the clinical course of chronic liver diseases. In this paper, we proposed a morphological analysis method based on statistical shape models (SSMs) of the liver and spleen for computer-aided diagnosis and quantification of the chronic liver. We constructed not only the liver SSM but also the spleen SSM and a joint SSM of the liver and the spleen for a morphologic analysis of the cirrhotic liver in CT images. The effective modes are selected based on both its accumulation contribution rate and its correlation with doctor's opinions (stage labels). We then learn a mapping function between the selected mode and the stage of chronic liver. The mapping function was used for diagnosis and staging of chronic liver diseases.

Original languageEnglish
Article number264809
JournalComputational and Mathematical Methods in Medicine
Volume2013
DOIs
Publication statusPublished - 2013
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Applied Mathematics

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