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Automatic bone segmentation in whole-body CT images.

André Klein1,2, Jan Warszawski3, Jens Hillengaß4

  • 1Division of Medical Image Computing, Deutsches Krebsforschungszentrum (DKFZ), Im Neuenheimer Feld 581, Heidelberg, Germany. andre.klein@dkfz.de.

International Journal of Computer Assisted Radiology and Surgery
|November 15, 2018
PubMed
Summary

Fully automatic bone segmentation in whole-body CT scans for multiple myeloma patients is now possible using U-Net inspired convolutional neural networks. This method achieves high accuracy, aiding in disease staging and treatment planning.

Keywords:
Bone segmentationComputed tomographyDeep learningMultiple myelomaU-Net

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • Accurate localization of bony structures in CT images is crucial for diagnosis and treatment planning.
  • Manual or semi-automatic bone segmentation is time-consuming and impractical for clinical use.
  • Multiple myeloma patients require precise bone evaluation.

Purpose of the Study:

  • To develop a reliable and fully automatic bone segmentation method for whole-body CT scans.
  • To address the limitations of manual segmentation in clinical settings.
  • To improve diagnostic and treatment planning for multiple myeloma.

Main Methods:

  • Utilized convolutional neural networks with a U-Net inspired architecture.
  • Compared three training procedures: 2D axial slices, pseudo-3D (axial, sagittal, coronal), and unsupervised pre-training.
  • Evaluated performance on an in-house dataset and a publicly available dataset.

Main Results:

  • Achieved a Dice score of 0.95 and IOU of 0.91 on the in-house dataset (18 scans, 6800 slices).
  • Outperformed other methods on a public dataset, achieving a Dice score of 0.92 and IOU of 0.85.
  • Publicly released data and ground truth for reproducibility.

Conclusions:

  • The proposed automatic bone segmentation method is reliable and accurate.
  • This technology can facilitate bone density evaluation and focal lesion localization.
  • Potential impact on disease staging and treatment planning for multiple myeloma.