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Iterative fully convolutional neural networks for automatic vertebra segmentation and identification.

Nikolas Lessmann1, Bram van Ginneken2, Pim A de Jong3

  • 1Image Sciences Institute, University Medical Center Utrecht, Room Q.02.4.45, 3508 GA Utrecht, P.O. Box 85500, The Netherlands.

Medical Image Analysis
|February 17, 2019
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Summary
This summary is machine-generated.

This study introduces an iterative deep learning method for segmenting and identifying vertebrae in CT and MR scans, regardless of image coverage. The approach accurately labels and classifies vertebrae, improving spine analysis for fracture detection.

Keywords:
Deep learningIterative instance segmentationVertebra identificationVertebra segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Spine Anatomy

Background:

  • Accurate vertebral segmentation and identification are crucial for automated spine analysis, including fracture detection.
  • Current methods are limited by the partial visibility of vertebrae in many CT and MR scans.
  • A robust method is needed that does not depend on the number or visibility of specific vertebrae.

Purpose of the Study:

  • To develop and evaluate an iterative instance segmentation approach for segmenting and anatomically labeling vertebrae in medical images.
  • To create a method that is independent of the number of visible vertebrae and can handle partial spine coverage.
  • To improve the accuracy and efficiency of vertebral analysis in diverse imaging modalities and scenarios.

Main Methods:

  • An iterative instance segmentation method using a fully convolutional neural network with a memory component.
  • The network analyzes image patches, leveraging memory of segmented vertebrae and image data to locate subsequent ones.
  • Incorporates prior knowledge of vertebral column contiguity for efficient image traversal and performs concurrent segmentation, labeling, and visibility prediction.

Main Results:

  • Achieved an average Dice score of 94.9% ± 2.1% for vertebra segmentation and 93% accuracy for anatomical identification.
  • Vertebrae visibility classification accuracy was 97%, enabling exclusion of incomplete data.
  • The iterative method demonstrated favorable comparison with state-of-the-art techniques in terms of speed, flexibility, and generalizability across diverse datasets.

Conclusions:

  • The proposed iterative instance segmentation method provides accurate and robust vertebral segmentation and anatomical identification.
  • The approach is effective across various modalities (CT, MR), fields of view, and challenging low-dose scans.
  • This flexible and generalizable method enhances automated spine analysis and abnormality detection.