Liver workbench: A tool suite for liver and liver tumor segmentation and modeling

Jiayin Zhou, Wei Xiong, Feng Ding, Weimin Huang, Tian Qi, Zhimin Wang, Thiha Oo, Sudhakar Kundapur Venkatesh

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

3 Scopus citations

Abstract

Robust and efficient liver and tumor segmentation segmentation tools from CT images are important for clinical decision-making in liver treatment planning and response evaluation. In this work, we report recent advances in an ongoing project Liver Workbench which aims to provide a suite of tools for the segmentation segmentation, quantification and modeling of various objects in CT images such as the liver, its vessels and tumors. Firstly, a liver segmentation segmentation approach is described. It registers a liver mesh model model to actual image features by adopting noise-insensitive flipping-free mesh deformations. Next, a propagation learning approach is incorporated into a semi-automatic classification method for robust segmentation segmentation of liver tumors based on liver ROI obtained. Finally, an unbiased probabilistic liver atlas construction technique is adopted to embody the shape and intensity variation to constrain liver segmentation segmentation. We also report preliminary experimental results.

Original languageEnglish (US)
Title of host publicationAdvances in Bio-Imaging
Subtitle of host publicationFrom Physics to Signal Understanding Issues: State-of-the-Art and Challenges
EditorsNicolas Lomenie, Daniel Racoceanu, Alexandre Gouaillard
Pages193-208
Number of pages16
DOIs
StatePublished - Feb 1 2012

Publication series

NameAdvances in Intelligent and Soft Computing
Volume120
ISSN (Print)1867-5662

ASJC Scopus subject areas

  • Computer Science(all)

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    Zhou, J., Xiong, W., Ding, F., Huang, W., Qi, T., Wang, Z., Oo, T., & Venkatesh, S. K. (2012). Liver workbench: A tool suite for liver and liver tumor segmentation and modeling. In N. Lomenie, D. Racoceanu, & A. Gouaillard (Eds.), Advances in Bio-Imaging: From Physics to Signal Understanding Issues: State-of-the-Art and Challenges (pp. 193-208). (Advances in Intelligent and Soft Computing; Vol. 120). https://doi.org/10.1007/978-3-642-25547-2_12