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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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

Published on: November 30, 2022

Tumor segmentation from computed tomography image data using a probabilistic pixel selection approach.

Jung Leng Foo1, Go Miyano, Thom Lobe

  • 1Virtual Reality Applications Center, 1620 Howe Hall, Iowa State University, Ames, IA 50011, USA. foo@iastate.edu

Computers in Biology and Medicine
|December 15, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel probabilistic segmentation method for improved tumor detection in CT scans. The automated approach enhances diagnostic accuracy by minimizing segmentation errors, aiding radiologists in clinical decision-making.

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Radiology

Background:

  • Automatic tumor segmentation in CT data is challenging due to unclear boundaries between tumors and healthy tissues.
  • Accurate segmentation is crucial for effective diagnosis and treatment planning using digital medical data.

Purpose of the Study:

  • To develop an efficient probabilistic segmentation method for tumors in CT images.
  • To enhance the utility of digital medical data in radiological diagnosis through improved segmentation.
  • To introduce an automatic seed selection algorithm for segmenting tumors across multiple CT slices.

Main Methods:

  • Image enhancement including windowing, sharpening, and noise removal.
  • Probabilistic segmentation based on pixel spatial and intensity properties after user-initialized seed placement.
  • An automatic seed selection algorithm employing a greedy search for intensity-matched pixels across slices.

Main Results:

  • The developed method was tested on ten diverse CT datasets.
  • Five datasets achieved mean false positive error rates below 10% compared to manual segmentation.
  • Four datasets achieved mean false negative error rates below 10% compared to manual segmentation.

Conclusions:

  • The probabilistic segmentation method shows promise for accurate and efficient tumor detection in CT scans.
  • The automatic seed selection algorithm effectively handles changes in tumor shape and location across slices.
  • This approach can improve the reliability of computer-aided diagnosis in radiology.