Scientific Posters

PARTICIPANTS
Presenter
Bilal Ahmed MD  
Abstract Co-Author
Rene Korn  
Markus Kietzmann  
Johann Kim  
Guenter Schmidt  
Masoom Haider MD  
et al  
SUBSPECIALTY CONTENT
Informatics
 
  CODE: LL-IN2114-B13
  SESSION: Informatics

  Automated CT-based Liver and Metastases Volume Assessment
 
 
  DATE: Sunday, November 29 2009
  START TIME: 12:30 PM
  END TIME: 01:30 PM
  LOCATION: Lakeside Learning Center



  DISCLOSURES
  B.A. - Nothing to disclose.  
  R.K. - Employee, Definiens AG, Munich, Germany  
  M.K. - Employee, Definiens AG, Munich, Germany  
  J.K. - Employee, Definiens AG, Munich, Germany  
  G.S. - Employee, Definiens AG, Munich, Germany  
  M.H. - Nothing to disclose.  
  0.e.  

 PURPOSE
 

Liver volume is routinely used in liver surgery and volumetric measurement of metastatic disease is useful in assessing tumor response to therapy. Manual volumetry of liver is a tedious, time-consuming and subjective process. This has led to a growing interest in the development of fast and accurate liver segmentation methods.

The purpose of this study is to retrospectively evaluate the accuracy of liver and liver metastases volume measured by a fully-automated software platform tailored to segmentation of medical images developed by the Definiens Cognition Network Technology®. The segmentation algorithm quantifies the liver and its lesions in a fully automatic manner by identifying context information from the lung, spine, ribs and gall bladder.
 

  
 METHOD AND MATERIALS
 

Institutional Review Board approval was obtained. A total of 30 consecutive portal phase CTs from distinct patients who had liver metastases were obtained. Scans were performed at 1mm collimations on a 320 slice CT scanner. All CTs were manually segmented to define reference standard boundaries for liver metastases and liver by author BA. Five representative cases were selected as a training set for algorithm refinement. The remaining 25 cases were used to test the resultant segmentation algorithm. Automated segmentations generated were compared to the expert generated references and deviations were measured.
 

  
 RESULTS
 

The correlation coefficient between the measured and estimated volumes obtained from Definiens segmentation algorithm was 0.864 ± 0.04 (range, 0.777 – 0.935). The mean volumetric overlap was 77% ± 6 (range, 64 – 88%). Mean liver volume measured by the automatic segmentation algorithm and manual segmentation was 1820 cm³ ± 788 (range, 710 – 4565 cm³) and 1806 cm³ ± 685 (range, 934 – 3460 cm³), respectively. The mean total time for the automatic segmentation was 4.8 minutes ± 1.2 on E6750 @2.66GHz Intel Core™ 2 Duo CPU with 3.25 GB RAM.
 

  
 CONCLUSION
 

An evaluation of the automated segmentation algorithm developed by Definiens shows promising results for segmentation of liver and liver metastases.
 

  
 CLINICAL RELEVANCE/APPLICATION
 

Automatic, accurate and fast liver and liver tumor volumetry has important applications in clinical medicine, including liver surgery and assessment of tumour response to therapy.
 

  
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