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Deep Neural Networks for Image-Based Dietary Assessment
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Fully automatic segmentation on prostate MR images based on cascaded fully convolution network.

Yi Zhu1, Rong Wei1, Ge Gao2

  • 1Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, P.R. China.

Journal of Magnetic Resonance Imaging : JMRI
|October 24, 2018
PubMed
Summary
This summary is machine-generated.

This study presents an automatic MRI segmentation method for prostate cancer detection, achieving high accuracy in segmenting the whole prostate and peripheral zone. The novel approach significantly outperforms traditional U-Net methods, aiding radiologists in cancer quantification.

Keywords:
cascaded fully convolutional networkfully automatic segmentationprostatic peripheral zonethe ROI of prostate

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computer-Aided Diagnosis

Background:

  • Prostate cancer predominantly occurs in the peripheral zone (PZ).
  • Accurate segmentation of prostate MRI is crucial for computer-aided diagnosis (CAD) and adheres to PI-RADS criteria.
  • Existing methods may lack efficiency in segmenting specific prostate regions.

Purpose of the Study:

  • To develop a fully automatic approach for segmenting the prostate outer contour and the peripheral zone (PZ) contour.
  • To achieve high efficacy in prostate MRI segmentation for improved cancer quantification.

Main Methods:

  • Utilized T2-weighted and diffusion-weighted imaging (DWI) datasets from 163 subjects (61 healthy, 102 cancer patients) at 3.0T.
  • Developed a novel method involving region cropping and cascaded networks to balance sample distribution.
  • Compared automated segmentation results against manual outlines from two expert radiologists using Dice Similarity Coefficient (DSC), False Positive Rate (FPR), and False Negative Rate (FNR).

Main Results:

  • Achieved a mean DSC of 92.7% for the whole prostate and 79.3% for the peripheral zone.
  • The proposed method demonstrated significantly higher segmentation precision compared to classical U-Net methods (P < 0.001).

Conclusions:

  • The developed fully automatic segmentation strategy provides satisfactory performance for prostate DWIs and T2-weighted images.
  • This approach enhances segmentation accuracy by balancing positive and negative samples, potentially improving CAD for prostate cancer.