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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Efficient Liver Segmentation from Computed Tomography Images Using Deep Learning.

Mubashir Ahmad1,2, Syed Furqan Qadri1, M Usman Ashraf3

  • 1College of Computer Science and Software Engineering, Computer Vision Institute, Shenzhen University, Shenzhen, Guangdong Province 518060, China.

Computational Intelligence and Neuroscience
|May 31, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning method using stacked autoencoders (SAE) for accurate liver segmentation in CT scans. The patch-based approach enhances feature learning, achieving a 96.47% Dice Similarity Coefficient (DSC) for improved medical diagnosis.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • Accurate liver segmentation in computed tomography (CT) images is crucial for quantitative biomarkers and medical diagnosis.
  • Fuzzy boundaries in CT images present significant challenges for precise liver segmentation.

Purpose of the Study:

  • To propose a novel patch-based deep learning method for liver segmentation in CT images.
  • To leverage stacked autoencoder (SAE) for unsupervised feature learning to overcome segmentation difficulties.

Main Methods:

  • A patch-based deep learning approach using SAE for unsupervised feature learning from CT image patches.
  • Preprocessing of CT images and conversion into overlapping patches for input into SAE.
  • Supervised fine-tuning of learned features and classification to generate a probability map for liver segmentation.

Main Results:

  • The proposed algorithm demonstrated satisfactory performance on test CT images.
  • Achieved a Dice Similarity Coefficient (DSC) of 96.47%, outperforming existing methods in liver segmentation.
  • The patch-based SAE method effectively learns discriminative features for liver identification.

Conclusions:

  • The patch-based deep learning method using SAE is effective for liver segmentation in CT images.
  • This approach offers improved accuracy and robustness compared to traditional methods.
  • The method holds potential for enhancing computer-aided decision support systems in radiology.