Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 13, 2026

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

Bridging Annotation Gaps: Hierarchical Self-Support Learning for Brain Tumor Segmentation.

Saqib Qamar^1,2, Mohd Fazil³, Zubair Ashraf⁴

¹Department of Intelligent Systems, KTH Royal Institute of Technology, 10044 Stockholm, Sweden.

Diagnostics (Basel, Switzerland)

|June 12, 2026

Summary

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

Multifunctional Phase Change Composites with Thermal-Shock Resistance and Electromagnetic Wave Absorption for High-Power Electronics.

ACS applied materials & interfaces·2026

Same author

MultiFAR: Multidimensional information fusion with attention-driven representation learning for student performance prediction.

PloS one·2025

Same author

LWLCM: A novel lightweight stream cipher using logistic chaos function and multiplexer for IoT communications.

PloS one·2025

Same author

Phase Engineered Composite Phase Change Materials for Thermal Energy Manipulation.

Small (Weinheim an der Bergstrasse, Germany)·2024

Same author

A Novel Thermal Interface Material Composed of Vertically Aligned Boron Nitride and Graphite Films for Ultrahigh Through-Plane Thermal Conductivity.

Small methods·2024

Same author

High-Efficiency Thermal-Shock Resistance Enabled by Radiative Cooling and Latent Heat Storage.

Advanced materials (Deerfield Beach, Fla.)·2024

Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026

Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026

Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026

Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026

Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026

Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026

See all related articles

This summary is machine-generated.

Hierarchical Adaptive Group Self-Support Learning (HAGSS) improves brain tumor segmentation from incomplete MRI data. This plug-in framework enhances accuracy by adaptively forming groups and focusing on tumor boundaries, without increasing computational cost.

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Accurate brain tumor segmentation using Magnetic Resonance Imaging (MRI) relies on fusing multiple data modalities.
Clinical MRI often presents incomplete datasets due to acquisition issues, patient limitations, or varying protocols.
Existing segmentation methods struggle with incomplete data, either isolating modality processing or using costly teacher networks.

Purpose of the Study:

Introduce Hierarchical Adaptive Group Self-Support Learning (HAGSS) to address limitations in existing group self-support methods for incomplete multi-modal brain tumor segmentation.
Overcome static group formation, uniform voxel treatment, and distribution mismatch issues present in current approaches.
Enhance the robustness and accuracy of brain tumor segmentation when dealing with missing MRI modalities.

Keywords:

boundary-aware calibration brain tumor cross-modal consistency learning incomplete multi-modal MRI segmentation self-support learning

More Related Videos

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Related Experiment Videos

Last Updated: Jun 13, 2026

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Main Methods:

Implement a hierarchical adaptive group formation mechanism using voxel-level prediction confidence scores for dynamic group reassignment.
Introduce a boundary-aware calibration module for spatially varied distillation weights, prioritizing tumor boundary regions.
Incorporate a cross-scale consistency regularization term to ensure agreement between multi-resolution predictions and stabilize self-support targets.

Main Results:

HAGSS demonstrated consistent performance improvements over state-of-the-art methods on BraTS2020, BraTS2018, and BraTS2021 datasets.
Achieved average Dice score gains of 1.30% (BraTS2020) and 1.61% (BraTS2021) across whole tumor, tumor core, and enhancing tumor regions.
Observed statistically significant improvements (p<0.05) across all tested datasets and segmentation regions.

Conclusions:

HAGSS functions solely during the training phase, introducing no additional parameters or inference costs.
The framework is designed as a plug-in module, compatible with various multi-encoder incomplete multi-modal segmentation architectures.
Publicly available code facilitates the adoption and further development of this approach for brain tumor segmentation research.