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Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on

Davood Karimi1, Golnoosh Samei2, Claudia Kesch3

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Summary

This study introduces a new convolutional neural network (CNN) method for prostate segmentation in MRI, using statistical shape models to improve accuracy with limited data. The approach enhances segmentation performance, particularly at the prostate base and apex.

Keywords:
Convolutional neural networksDeep learningMedical image segmentationProstate segmentationStatistical shape models

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Existing convolutional neural network (CNN) methods for medical image segmentation often fail to account for domain-specific challenges.
  • Medical imaging presents unique difficulties, including limited training data and smaller variations in target anatomy compared to natural images.

Purpose of the Study:

  • To develop a novel CNN-based method for prostate segmentation in MRI.
  • To address limitations of existing methods by incorporating statistical shape models.
  • To improve segmentation accuracy in the context of limited medical imaging data.

Main Methods:

  • A CNN was designed to predict prostate center location and statistical shape model parameters.
  • A stage-wise training strategy was employed, starting with center prediction and progressing to shape and rotation parameters.
  • A data augmentation technique deforming training images based on shape model displacements was utilized.
  • Various regularization techniques, including elastic-net and spectral dropout, were applied.

Main Results:

  • The proposed method achieved a Dice score of 0.88.
  • Significantly improved segmentation performance was observed at the prostate base and apex compared to standard CNN methods.
  • Data augmentation utilizing the statistical shape model demonstrably enhanced segmentation outcomes.

Conclusions:

  • Integrating prior knowledge of organ shape, via statistical shape models, can significantly boost CNN-based segmentation performance.
  • Statistical shape models are effective for synthesizing additional training data, aiding the training of large CNNs in data-scarce medical applications.