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3D automatic anatomy segmentation based on iterative graph-cut-ASM.

Xinjian Chen1, Ulas Bagci

  • 1Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10 Room IC515, Bethesda, Maryland 20892-1182, USA. chenx6@mail.nih.gov

Medical Physics
|September 21, 2011
PubMed
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This summary is machine-generated.

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This study demonstrates a feasible automatic anatomy segmentation system for clinical radiology, achieving high accuracy in segmenting organs and foot bones rapidly. The novel iterative graph-cut-active shape model (IGCASM) method proves robust and efficient.

Area of Science:

  • Medical Imaging
  • Radiology
  • Computer-Aided Diagnosis

Background:

  • Accurate anatomical segmentation is crucial for clinical radiology and computer-aided diagnosis.
  • Existing segmentation methods often lack robustness or efficiency in 3D clinical datasets.

Purpose of the Study:

  • To develop and validate a novel automatic anatomy segmentation (AAS) system for clinical 3D radiological images.
  • To demonstrate the feasibility and efficacy of a hybrid segmentation approach combining active shape models (ASM) and graph-cut (GC) methods.

Main Methods:

  • A hierarchical 3D scale-based multiobject recognition method incorporating intensity-weighted ball-scale (b-scale) information into ASM.
  • An iterative graph-cut-ASM (IGCASM) algorithm for globally optimal 3D object delineation, generalizing a previous 2D method.

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  • Testing on clinical abdominal CT and foot MRI scans for segmentation of organs (liver, kidneys, spleen) and bones (calcaneus, tibia, cuboid, talus, navicular).

    • Main Results:

    • High recognition accuracies achieved: ~8 mm translation, ~10° rotation, 0.03 scale error for organs; ~3.57 mm translation, ~0.35° rotation, 0.025 scale error for foot bones.
    • Excellent delineation accuracy: 93.01% TPVF and 0.22% FPVF for organs; 93.75% TPVF and 0.28% FPVF for foot bones.
    • Rapid segmentation times: average 78 seconds for organs and 70 seconds for foot bones.

    Conclusions:

    • The proposed AAS system is feasible and effective for clinical 3D image analysis.
    • The hybrid IGCASM strategy demonstrates superior robustness and accuracy compared to individual ASM or GC methods.
    • The system enables rapid and accurate segmentation of clinically important anatomical structures within 1.5 minutes.