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Automatic Skeleton Segmentation in CT Images Based on U-Net.

Eva Milara1, Adolfo Gómez-Grande2,3, Pilar Sarandeses2,3

  • 1Biomedical Engineering and Telemedicine Centre, Center for Biomedical Technology, ETSI Telecomunicación, Universidad Politécnica de Madrid, 28040, Madrid, Spain.

Journal of Imaging Informatics in Medicine
|April 30, 2024
PubMed
Summary

This study introduces a deep learning model for automatic skeleton segmentation from CT scans. The U-Net based model accurately segments bone structures, aiding in the evaluation of conditions like osteoporosis and bone metastasis.

Keywords:
Anatomical imagesBone automatic segmentationNeural networksU-Net architecture

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Skeletal Biology

Background:

  • Morphological bone alterations occur in various clinical contexts, including bone metastasis, oncological therapies, and osteoporosis.
  • Visual assessment of bone changes from anatomical images is suboptimal.
  • Precise skeletal segmentation is crucial for accurate bone evaluation.

Purpose of the Study:

  • To propose a neural network model for automatic skeleton segmentation from 2D computerized tomography (CT) slices.
  • To evaluate the model's performance in segmenting bone structures accurately.

Main Methods:

  • Utilized 77 CT images with semimanual segmentation for training and testing.
  • Implemented a U-Net based neural network architecture.
  • Applied preprocessing steps including stretcher removal, thresholding, clipping, and normalization.

Main Results:

  • Achieved a Jaccard index (IoU) of 0.959 and a Dice index of 0.979 with the best performing model.
  • Demonstrated high accuracy in automatic bone segmentation.

Conclusions:

  • Deep learning models, specifically U-Net architecture, show significant potential for medical image analysis.
  • The developed model proves effective for precise bone segmentation, aiding clinical evaluation.