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Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation.

Wenjian Qin1,2,3, Jia Wu2, Fei Han2,4

  • 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.

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Summary

This study introduces a new superpixel-based and boundary sensitive convolutional neural network (SBBS-CNN) for automated liver segmentation in CT scans. The SBBS-CNN accurately segments livers, improving radiation therapy planning for liver cancer.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Accurate liver segmentation in abdominal CT scans is crucial for hepatocellular carcinoma radiation therapy planning.
  • Automated liver segmentation faces challenges due to low soft tissue contrast and liver deformability.

Purpose of the Study:

  • To develop a novel superpixel-based and boundary sensitive convolutional neural network (SBBS-CNN) for automated liver segmentation.
  • To enhance the accuracy and efficiency of liver segmentation in CT imaging.

Main Methods:

  • Images were partitioned into superpixels, and segmentation was converted to a multinomial classification of superpixels (interior liver, boundary, background).
  • An entropy-based saliency map guided patch sampling, prioritizing informative regions like the liver boundary.
  • A deep convolutional neural network (CNN) pipeline was trained to predict liver boundary probability maps.

Main Results:

  • The SBBS-CNN achieved a mean Dice similarity coefficient of 97.31% ± 0.36% and an average symmetric surface distance of 1.77 mm ± 0.49 mm.
  • The algorithm demonstrated superior performance compared to existing methods like U-Net, pixel-based CNN, active contour, level-sets, and graph-cut.
  • Tested on 100 patients with 10-fold cross-validation, showing high accuracy and reliability.

Conclusions:

  • The SBBS-CNN provides an accurate and effective tool for automated liver segmentation in CT images.
  • The proposed framework shows potential for direct application in other medical image segmentation tasks.
  • This method can significantly aid in radiation therapy planning for liver cancer patients.