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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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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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Automatic Segmentation of Organs-at-Risk in Thoracic Computed Tomography Images Using Ensembled U-Net InceptionV3

Malvika Ashok1, Abhishek Gupta1

  • 1School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|January 11, 2023
PubMed
Summary

This study introduces an advanced AI model for automatically segmenting organs at risk (OARs) in thoracic CT scans. The ensemble U-Net InceptionV3 model significantly improves the accuracy of identifying the esophagus, heart, trachea, and aorta for radiotherapy.

Keywords:
CT imagesU-Netcomputed tomographyorgans-at-risksegmentationthoracic radiology

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Accurate segmentation of organs at risk (OARs) is crucial for effective thoracic radiotherapy.
  • Manual segmentation is time-consuming and prone to inter-observer variability.
  • Automating OARs segmentation in computed tomography (CT) scans can enhance treatment planning.

Purpose of the Study:

  • To develop and evaluate an automated method for segmenting key OARs in thoracic CT images.
  • To improve the precision and efficiency of OARs segmentation for radiotherapy applications.
  • To compare the performance of the proposed model against existing state-of-the-art techniques.

Main Methods:

  • An ensemble model combining U-Net with InceptionV3 backbone was implemented.
  • The model was trained and validated on a dataset of 40 patient CT scans.
  • Hyperparameter tuning was performed to optimize segmentation performance.
  • The implementation utilized the Google Colab Pro+ framework.

Main Results:

  • The proposed ensemble U-Net InceptionV3 model achieved high segmentation accuracy.
  • Average performance metrics on the test dataset included a Dice coefficient of 0.9413, Hausdorff value of 0.1838, sensitivity of 0.9783, and specificity of 0.9895.
  • The model demonstrated superior performance compared to U-Net, ResNet, and Vgg16.

Conclusions:

  • The developed ensembled U-Net InceptionV3 model effectively automates the segmentation of esophagus, heart, trachea, and aorta in thoracic CT scans.
  • This automated approach offers improved accuracy and efficiency for radiotherapy planning.
  • The proposed method represents a significant advancement over current state-of-the-art segmentation techniques.