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Liver segmentation from computed tomography images using cascade deep learning.

José Denes Lima Araújo1, Luana Batista da Cruz1, João Otávio Bandeira Diniz2

  • 1Applied Computing Group (NCA - UFMA), Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65 085-580, São Luís, MA, Brazil.

Computers in Biology and Medicine
|December 13, 2021
PubMed
Summary

This study presents an automated liver segmentation method using deep convolutional neural networks for CT images, achieving high accuracy and efficiency in liver cancer treatment planning.

Keywords:
Computed tomographyConvolutional neural networksDeep learningLiver cancerLiver segmentation

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Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Artificial Intelligence in Medicine

Background:

  • Manual liver segmentation is time-consuming and prone to errors.
  • Accurate liver segmentation is crucial for liver cancer diagnosis and treatment planning.
  • Automatic methods are needed to overcome the challenges of manual segmentation.

Purpose of the Study:

  • To propose an automated method for liver segmentation.
  • To utilize computed tomography (CT) images for liver segmentation.
  • To improve time efficiency and accuracy in liver segmentation.

Main Methods:

  • A deep convolutional neural network (CNN) based approach.
  • Incorporation of image processing techniques.
  • A four-step process: image preprocessing, initial segmentation, reconstruction, and final segmentation.

Main Results:

  • Evaluation on 131 CT images from the LiTS dataset.
  • Achieved high performance metrics: 95.45% sensitivity, 99.86% specificity, 95.64% Dice coefficient.
  • Demonstrated low errors: 8.28% VOE, -0.41% RVD, 26.60 mm Hausdorff distance.

Conclusions:

  • The proposed method enables efficient liver segmentation from CT images.
  • A cascade approach with a reconstruction step using CNNs is effective.
  • Successful segmentation is achieved even in the presence of liver lesions.