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Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing.

Grzegorz Chlebus1, Andrea Schenk2, Jan Hendrik Moltz2

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This study presents an automatic liver tumor segmentation method using a 2D convolutional neural network. The approach significantly reduces false positives while achieving comparable segmentation quality to human performance in liver tumor detection.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate liver tumor segmentation is crucial for treatment planning and monitoring.
  • Current methods can be time-consuming and prone to variability.

Purpose of the Study:

  • To develop a fully automatic method for liver tumor segmentation in CT images.
  • To improve the accuracy and efficiency of liver tumor analysis.

Main Methods:

  • A 2D fully convolutional neural network (CNN) was developed for segmentation.
  • An object-based postprocessing step was integrated to refine results.
  • Experiments were conducted on the Liver Tumor Segmentation (LiTS) challenge dataset.

Main Results:

  • The proposed method achieved an 85% reduction in false positives compared to raw CNN output.
  • Segmentation quality (mean Dice 0.69) was comparable to human performance (0.72).
  • Detection performance (recall 63%) was lower than human performance (92%).

Conclusions:

  • The developed automatic segmentation method shows promise for clinical applications.
  • Further improvements in detection are needed to match human capabilities.
  • The approach achieved state-of-the-art performance in the LiTS challenge.