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Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural

Trong-Ngoc Le1, Pham The Bao2, Hieu Trung Huynh3

  • 1Faculty of Information Technology, Industrial University of Ho Chi Minh City, 12 Nguyen Van Bao, Go Vap District, Ho Chi Minh City, Vietnam; Faculty of Information Technology, University of Science, 227 Nguyen Van Cu, District 5, Ho Chi Minh City, Vietnam.

Biomed Research International
|September 7, 2016
PubMed
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This study presents a new computerized method for segmenting liver tumors in MRI scans, achieving accurate boundary detection and volume estimation. The developed scheme offers a promising tool for improving liver tumor analysis in clinical settings.

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Oncology

Background:

  • Accurate liver tumor segmentation is crucial for diagnosis and treatment planning.
  • Manual segmentation is time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To develop and evaluate an automated scheme for liver tumor segmentation in T1-weighted MR images.
  • To compare the automated segmentation results with manual segmentation by a radiologist.

Main Methods:

  • A four-stage computerized scheme involving region of interest extraction, noise reduction, boundary enhancement, and a single hidden layer feedforward neural network (SLFN) for voxel classification.
  • Utilized a 3D fast marching algorithm for initial region generation and postprocessing for boundary refinement.

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Main Results:

  • The scheme demonstrated a mean volumetric overlap error of 27.43% and a mean percentage volume error of 15.73%.
  • Surface distance metrics included mean average surface distance of 0.58 mm, root mean square surface distance of 1.20 mm, and maximal surface distance of 6.29 mm.

Conclusions:

  • The proposed computerized scheme provides accurate liver tumor segmentation in MR images.
  • The method shows potential for assisting radiologists in liver tumor analysis and reducing segmentation variability.