Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

PANTHER Challenge Report: Cross-Domain Pancreatic Tumor Segmentation in Magnetic Resonance Imaging.

Medical image analysis·2026
Same author

Rethinking the detail-preserved completion of complex tubular structures based on point cloud: A dataset and a benchmark.

Medical image analysis·2026
Same author

Global burden of bipolar disorder in 204 countries and territories, 1990-2021: a systematic analysis of temporal trends, demographic disparities, and SDI associations for the Global Burden of Disease Study 2021.

Psychiatry research·2026
Same author

Advancement of deep learning models with whole slide image in diagnosis, subtyping and prognosis for glioma.

Progress in biomedical engineering (Bristol, England)·2026
Same author

Interlayer-aware postoperative facial appearance prediction in orthognathic surgery with bio-geometric guidance.

Physics in medicine and biology·2026
Same author

Re-innervation of neuromuscular junctions by a conductive polypyrrole/silk fibroin/GelMA hydrogel facilitated functional skeletal muscle regeneration following volumetric muscle loss.

Journal of orthopaedic translation·2026
Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026
Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026
Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026
Same journal

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026
Same journal

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026
Same journal

Annotation-efficient medical image segmentation via cross-latent graphs and vector-quantized memory.

Medical image analysis·2026
See all related articles

Related Experiment Video

Updated: Nov 8, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

620

Meta grayscale adaptive network for 3D integrated renal structures segmentation.

Yuting He1, Guanyu Yang2, Jian Yang3

  • 1LIST, Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China.

Medical Image Analysis
|April 18, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces MGANet, a novel deep learning model for segmenting 3D renal structures (kidneys, tumors, arteries, veins) in one step. It achieves high accuracy, aiding surgeons in planning kidney cancer treatments.

Keywords:
Automatic hyper-parameter searchEnsemble learningGrayscale interest searchIntegrated renal structures segmentationMeta grayscale adaptive networkMeta grayscale ensembleMeta learning

More Related Videos

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

3.1K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.1K

Related Experiment Videos

Last Updated: Nov 8, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

620
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

3.1K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.1K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Nephrology

Background:

  • Accurate segmentation of 3D integrated renal structures (IRS) is crucial for planning laparoscopic partial nephrectomy (LPN).
  • Previous 3D IRS segmentation attempts faced challenges due to low contrast and inter-image grayscale variations in CTA images.
  • Existing methods struggle with the narrow distribution range of regions of interest (ROIs) and varying network preferences.

Purpose of the Study:

  • To develop the first deep learning framework for simultaneous 3D IRS segmentation (kidney, tumors, arteries, veins) in a single inference.
  • To address the limitations of grayscale distribution and inter-image variations in medical image segmentation.
  • To improve preoperative planning and intraoperative guidance for LPN.

Main Methods:

  • Proposed the Meta Greyscale Adaptive Network (MGANet), a novel deep learning framework.
  • Introduced Grayscale Interest Search (GIS) to focus on task-dependent grayscale distributions by adaptively scaling window width and center.
  • Implemented Meta Grayscale Adaptive (MGA) learning, an image-level meta-learning strategy for dynamic feature fusion based on image distribution.

Main Results:

  • Achieved an average Dice coefficient of 87.9% for renal structures segmentation.
  • Demonstrated the effectiveness of MGANet in overcoming grayscale distribution challenges.
  • Showcased powerful segmentation accuracy and significant clinical potential for renal cancer treatment.

Conclusions:

  • MGANet is the first framework to achieve simultaneous 3D IRS segmentation in one inference.
  • Adaptive grayscale distribution selection and personalized feature fusion enhance segmentation quality.
  • The approach holds significant clinical value for improving renal cancer treatment outcomes.