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Interactive Cascaded Network for Prostate Cancer Segmentation from Multimodality MRI with Automated Quality

Weixuan Kou1, Cristian Rey2, Harry Marshall3

  • 1Department of Electrical Engineering, City University of Hong Kong, Hong Kong.

Bioengineering (Basel, Switzerland)
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for prostate cancer (PCa) segmentation using AI. It significantly reduces manual annotation, achieving high accuracy comparable to full manual segmentation with only half the effort.

Keywords:
automatic quality assessmentinteractive segmentationprostate lesion segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate prostate cancer (PCa) segmentation from multiparametric MRI is vital for clinical decisions.
  • Current automated methods lack accuracy, while interactive methods are time-consuming.
  • Existing segmentation workflows face challenges in cost-effectiveness and user burden.

Purpose of the Study:

  • To develop an innovative framework for accurate and efficient PCa segmentation.
  • To reduce the manual annotation burden in medical image segmentation.
  • To improve the cost-effectiveness of segmentation workflows while maintaining high accuracy.

Main Methods:

  • A novel framework combining a coarse segmentation network, a rejection network, and the Segment Anything Model (SAM).
  • Automated initial segmentation followed by quality assessment via a rejection network.
  • Selective user interaction for low-quality segmentations and automated ROI cropping for high-quality ones.

Main Results:

  • The framework significantly reduces annotation effort by flagging ~20% of images for manual review.
  • Achieved final segmentation accuracy statistically indistinguishable from full manual annotation with only 50% manual input.
  • Demonstrated substantial improvements in segmentation efficiency and accuracy.

Conclusions:

  • The proposed framework effectively balances segmentation accuracy and efficiency.
  • It offers a cost-effective solution for PCa segmentation, reducing user intervention.
  • The adaptable framework holds potential for various medical image segmentation applications.