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Updated: May 25, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Medical image segmentation by combining graph cuts and oriented active appearance models.

Xinjian Chen1, Jayaram K Udupa, Ulas Bagci

  • 1Department of Radiology and Imaging Sciences, Clinical Center, National Institute of Health, Bethesda, MD 20814, USA. myfuturejian@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 8, 2012
PubMed
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This study introduces a novel method combining active appearance models (AAM), live wire (LW), and graph cuts (GCs) for accurate 3-D abdominal organ segmentation, achieving high accuracy and improved speed.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Computational Anatomy

Background:

  • Accurate segmentation of abdominal organs is crucial for medical diagnosis and treatment planning.
  • Existing methods often face challenges in speed and accuracy, particularly in 3D applications.

Purpose of the Study:

  • To develop and evaluate a novel, robust, and efficient method for 3D abdominal organ segmentation.
  • To improve upon conventional active appearance model (AAM) methods by integrating live wire (LW) and graph cuts (GCs).

Main Methods:

  • A hybrid approach combining Active Appearance Models (AAM), Live Wire (LW), and Graph Cuts (GCs) for 3D organ segmentation.
  • Development of an oriented AAM (OAAM) by integrating AAM and LW for improved object recognition.
  • Implementation of a 3D shape-constrained Graph Cuts (GC) method for accurate object delineation, utilizing a pseudo-3D initialization strategy.

Related Experiment Videos

Last Updated: May 25, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Main Results:

  • Achieved high segmentation accuracy with a true positive volume fraction (TPVF) > 94.3%.
  • Demonstrated significant improvements in initialization performance through the combination of AAM and LW, and a multiobject strategy.
  • The pseudo-3D OAAM method showed comparable performance to traditional 3D AAM but was 12 times faster.
  • The proposed method's performance was comparable to state-of-the-art liver segmentation algorithms.

Conclusions:

  • The proposed AAM, LW, and GCs integrated method offers a significant advancement in 3D abdominal organ segmentation.
  • The novel OAAM and 3D shape-constrained GC approach provide a fast, accurate, and robust solution for clinical applications.
  • The method facilitates improved initialization and delineation, outperforming traditional 3D AAM in speed while maintaining accuracy.