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Related Experiment Video

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A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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MRI-based prostate and dominant lesion segmentation using cascaded scoring convolutional neural network.

Zachary A Eidex1,2, Tonghe Wang1,3, Yang Lei1

  • 1Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA.

Medical Physics
|May 9, 2022
PubMed
Summary
This summary is machine-generated.

A new AI method accurately segments prostate and dominant intraprostatic lesions (DILs) on MRI. This automated segmentation aids in planning precise radiation therapy for improved prostate cancer treatment outcomes.

Keywords:
MRIdeep learningprostate and dominant lesion segmentation

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Area of Science:

  • Medical Imaging and Radiation Oncology
  • Artificial Intelligence in Healthcare
  • Computational Anatomy

Background:

  • Dose escalation to dominant intraprostatic lesions (DILs) is a promising strategy for enhancing prostate radiation therapy outcomes.
  • Accurate and rapid delineation of the prostate and DILs is crucial for effective treatment planning in radiation therapy.
  • Current segmentation methods may face challenges with the irregular shapes of DILs.

Purpose of the Study:

  • To propose and evaluate a novel 3D cascaded scoring convolutional neural network for automatic segmentation of the prostate and DILs from MRI.
  • To enable faster and more accurate delineation for improved treatment planning in focal boost prostate radiation therapy.

Main Methods:

  • A 3D cascaded scoring convolutional neural network was developed for end-to-end segmentation, involving region-of-interest localization, object identification, and target definition.
  • A learned scoring strategy was integrated to address the challenge of segmenting irregularly shaped DILs, improving segmentation quality.
  • The method was evaluated on 77 patients using MRI and PET/CT data, with ground truth contours delineated by expert radiologists, employing fivefold cross-validation and holdout testing.

Main Results:

  • The proposed method achieved high accuracy in segmenting both the prostate and DILs, with average Dice similarity coefficients (DSC) of 89.6% ± 8.9% for the prostate and 84.3% ± 11.9% for DILs.
  • Quantitative metrics including average centroid distance and volume difference demonstrated the precision of the automated segmentation.
  • The method's performance was comparable to or superior to existing segmentation approaches in holdout testing.

Conclusions:

  • The developed automatic segmentation method accurately and simultaneously delineates both the prostate and DILs from MRI data.
  • This AI-driven approach shows significant potential for facilitating focal boost prostate radiation therapy planning.
  • The algorithm offers a reliable tool for improving the precision and efficiency of radiation treatment for prostate cancer.