An Automated Segmentation Algorithm for CT Volumes of Livers with Atypical Shapes and Large Pathological Lesions

• UMETSU Shun

  Tokyo University of Agriculture and Technology

• SHIMIZU Akinobu

  Tokyo University of Agriculture and Technology

• WATANABE Hidefumi

  Tokyo University of Agriculture and Technology

• KOBATAKE Hidefumi

  Institute of National Colleges of Technology

• NAWANO Shigeru

  International University of Health and Welfare

Abstract

This paper presents a novel liver segmentation algorithm that achieves higher performance than conventional algorithms in the segmentation of cases with unusual liver shapes and/or large liver lesions. An L1 norm was introduced to the mean squared difference to find the most relevant cases with an input case from a training dataset. A patient-specific probabilistic atlas was generated from the retrieved cases to compensate for livers with unusual shapes, which accounts for liver shape more specifically than a conventional probabilistic atlas that is averaged over a number of training cases. To make the above process robust against large pathological lesions, we incorporated a novel term based on a set of “lesion bases” proposed in this study that account for the differences from normal liver parenchyma. Subsequently, the patient-specific probabilistic atlas was forwarded to a graph-cuts-based fine segmentation step, in which a penalty function was computed from the probabilistic atlas. A leave-one-out test using clinical abdominal CT volumes was conducted to validate the performance, and proved that the proposed segmentation algorithm with the proposed patient-specific atlas reinforced by the lesion bases outperformed the conventional algorithm with a statistically significant difference.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E97.D (4), 951-963, 2014

  The Institute of Electronics, Information and Communication Engineers

Keywords

• CT volume
• liver segmentation
• lasso
• lesion
• probabilistic atlas

Details

• CRID: 1390001204378031488
• NII Article ID: 130003394921
• DOI: 10.1587/transinf.e97.d.951
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E97.D/4/E97.D_951/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed