Automatic liver segmentation with CT images based on 3D U-net deep learning approach

Ting Yu Su, Wei Tse Yang, Tsu Chi Cheng, Yi Fei He, Yu Hua Fang

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

Abstract

The detection and the evaluation of the shape of liver from abdominal computed tomography (CT) images are fundamental tasks in the computer-assisted liver surgery planning such as radiation therapy. However, the segmentation of the liver still remains many challenges to be solved, such as ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we developed an automatic liver segmentation model based on 3D U-net network. Some preprocessing steps were done to elevate the performance of our protocol first. Also, an approximate liver map was generated by calculating the gradient of CT images. The area which had high possibility to be liver was select as the training set to make sure the balance of data. Then, a deep learning U-net structure was applied for the processed training data. Finally, some post-processing methods, which include k-means clustering and morphology algorithms, was applied in our protocol. Our protocol showed the results with high structure similarity index (SSIM), dice score coefficient and peak signal-to noise ratio (PSNR) of liver segmentation model, demonstrating the potential clinical applicability of the proposed approach.

Original languageEnglish
Title of host publicationInternational Forum on Medical Imaging in Asia 2019
EditorsJong Hyo Kim, Hiroshi Fujita, Feng Lin
PublisherSPIE
ISBN (Electronic)9781510627758
DOIs
Publication statusPublished - 2019
EventInternational Forum on Medical Imaging in Asia 2019 - Singapore, Singapore
Duration: 2019 Jan 72019 Jan 9

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11050
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceInternational Forum on Medical Imaging in Asia 2019
CountrySingapore
CitySingapore
Period19-01-0719-01-09

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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