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A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans.

Accurate knowledge of the liver structure, including liver surface and lesion localization, is usually required in treatments such as liver tumor ablations and/or radiotherapy. This paper presents a new method and corresponding algorithm for fast segmentation of the liver and its internal lesions fr...

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Published in:European Radiology Vol. 18; no. 8; pp. 1658 - 1666
Main Authors: Massoptier L, Casciaro S, Massoptier, Laurent, Casciaro, Sergio
Format: research Journal Article
Published: Springer Nature Aug2008
Online Access:View this record in EBSCOhost
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      pub: Springer Nature
      place: New York, New York
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        atl: A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans.
      aug:
        au:
          Massoptier L
          Casciaro S
          Massoptier, Laurent
          Casciaro, Sergio
        affil: Division of Biomedical Engineering Science and Technology, Institute of Clinical Physiology of National Research Council, Campus Ecotekne, via per Monteroni, 73100, Lecce, Italy
      sug:
        subj:
          Algorithms
          Artificial Intelligence
          Information Science Methods
          Liver Neoplasms Radiography
          Liver Radiography
          Radiographic Image Enhancement Methods
          Radiographic Image Interpretation, Computer-Assisted Methods
          Tomography, X-Ray Computed Methods
          Reproducibility of Results
          Sensitivity and Specificity
          Human
      ab: Accurate knowledge of the liver structure, including liver surface and lesion localization, is usually required in treatments such as liver tumor ablations and/or radiotherapy. This paper presents a new method and corresponding algorithm for fast segmentation of the liver and its internal lesions from CT scans. No interaction between the user and analysis system is required for initialization since the algorithm is fully automatic. A statistical model-based approach was created to distinguish hepatic tissue from other abdominal organs. It was combined to an active contour technique using gradient vector flow in order to obtain a smoother and more natural liver surface segmentation. Thereafter, automatic classification was performed to isolate hepatic lesions from liver parenchyma. Twenty-one datasets, presenting different anatomical and pathological situations, have been processed and analyzed. Special focus has been driven to the resulting processing time together with quality assessment. Our method allowed robust and efficient liver and lesion segmentations very close to the ground truth, in a relatively short processing time (average of 11.4 s for a 512 x 512-pixel slice). A volume overlap of 94.2% and an accuracy of 3.7 mm were achieved for liver surface segmentation. Sensitivity and specificity for tumor lesion detection were 82.6% and 87.5%, respectively.
      pubtype: Academic Journal
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        research
        Journal Article
      ougenre: Article
    language: English
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