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Related Experiment Video

Updated: Feb 24, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.6K

Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks.

Ruida Cheng1, Holger R Roth2, Nathan Lay2

  • 1Imaging Sciences Laboratory, Center of Information Technology, NIH, Bethesda, Maryland, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|August 26, 2017
PubMed
Summary

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This study introduces a novel deep learning method for precise prostate segmentation in MRI scans. The enhanced Holistically Nested Network (HNN) model significantly improves accuracy, outperforming traditional methods for better prostate cancer diagnosis.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Prostate segmentation in MRI is crucial but challenging due to anatomical variability and image artifacts.
  • Traditional methods struggle with accuracy because of noise and similar tissue intensities near the prostate boundary.
  • Deep learning offers potential for improved segmentation accuracy.

Purpose of the Study:

  • To develop and evaluate advanced deep learning methods for accurate automatic prostate segmentation in MRI.
  • To compare a novel holistic deep learning approach with a patch-based method.
  • To establish a new state-of-the-art in MRI prostate segmentation.

Main Methods:

  • Investigated patch-based convolutional networks for prostate contour refinement.
Keywords:
deep learningholistically nested edge detectionholistically nested networksmagnetic resonance imagesprostatesegmentation

Related Experiment Videos

Last Updated: Feb 24, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.6K
  • Proposed an end-to-end segmentation method integrating Holistically Nested Edge Detection (HNED) with Fully Convolutional Networks (FCNs).
  • Evaluated the enhanced Holistically Nested Network (HNN) model on 250 patient MRI scans using fivefold cross-validation.

    • Main Results:

    • The enhanced HNN model achieved a Dice Similarity Coefficient (DSC) of [Formula: see text] and a Jaccard Index (IoU) of [Formula: see text].
    • The holistic HNN model significantly outperformed a patch-based AlexNet model, showing a 9% improvement in DSC and 13% in IoU.
    • The method demonstrated state-of-the-art performance compared to existing MRI prostate segmentation techniques.

    Conclusions:

    • The proposed enhanced HNN model provides highly accurate automatic prostate segmentation in MRI.
    • Holistic deep learning methods, particularly the enhanced HNN, offer significant advantages over patch-based approaches.
    • This research advances the field of medical image analysis for prostate cancer diagnosis and treatment planning.