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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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An automatic method for colon segmentation in CT colonography.

Alberto Bert1, Ivan Dmitriev, Silvano Agliozzo

  • 1im3D S.p.A. Medical Imaging Lab, Via Lessolo 3, 10153 Torino, Italy. alberto.bert@i-m3d.com

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|March 24, 2009
PubMed
Summary
This summary is machine-generated.

An automatic method accurately segments the colonic wall in abdominal computed tomography (CT) scans. This technique shows high precision, identifying 97% of segments and 99.8% of the colon surface for polyp detection.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computer-Aided Diagnosis

Background:

  • Accurate segmentation of the colonic wall is crucial for detecting abnormalities in CT colonography.
  • Existing methods may lack robustness or require manual intervention.

Purpose of the Study:

  • To develop and evaluate an automatic method for colonic wall segmentation in abdominal CT scans.
  • To assess the method's accuracy and independence from scanner variations.

Main Methods:

  • A multistage approach utilizing an adaptive 3D region-growing algorithm.
  • Self-adjusting growing conditions based on local intensity variations at the air-tissue boundary.
  • Evaluation using expert radiologist visual segmentation of retrospectively collected CT scans.

Main Results:

  • The method successfully identified 97% of colon segments and 99.8% of the colon surface.
  • The segmentation accurately replicated the anatomical profile of the colonic wall.
  • Performance was largely independent of scanner and acquisition conditions.

Conclusions:

  • The proposed automatic segmentation method is accurate and robust for cleansed, air-inflated colons in CT.
  • This technique holds promise for improving computer-aided detection of polyps in CT colonography.