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

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

Human-AI Collaboration in Healthcare: A Scoping Review.

NPJ digital medicine·2026

Same author

Genome-Wide Identification of Pineapple AcINH Genes and Functional Characterization of <i>AcINH3</i> in Sucrose Metabolism and Drought Tolerance.

Plants (Basel, Switzerland)·2026

Same author

MiABI5-like7-<i>MiFT3</i> regulatory module controls floral transition induced by mepiquat chloride in evergreen perennial mango (<i>Mangifera indica</i> L.).

Horticulture research·2026

Same author

A visually grounded language model for fetal ultrasound understanding.

Nature biomedical engineering·2026

Same author

Alzheimer's Imaging Consortium.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Same author

Mango Fructokinases Inhibit Sugar Accumulation and Enhance Energy Metabolism in Transgenic Tomato.

Plants (Basel, Switzerland)·2025

Same journal

Co-assistant networks by pathology foundation model and convolutional neural network for gigapixel whole slide image analysis.

Medical image analysis·2026

Same journal

MBAS2024: A large-scale benchmark for multi-class bi-atrial segmentation in multi-center contrast-enhanced MRIs.

Medical image analysis·2026

Same journal

Respiratory motion augmentation for personalized super-resolution (RMApSR) of 3D cine MR images in MRI-guided radiotherapy.

Medical image analysis·2026

Same journal

Biom3d, a modular framework to host and develop 3D segmentation methods.

Medical image analysis·2026

Same journal

Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision.

Medical image analysis·2026

Same journal

Real-time patient-specific microwave ablation zone prediction via a unified bioheat solver and MRI-informed perturbation learning.

Medical image analysis·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 17, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

IterMask3D: Unsupervised anomaly detection and segmentation with test-time iterative mask refinement in 3D brain MRI.

Ziyun Liang¹, Xiaoqing Guo², Wentian Xu¹

¹Department of Engineering Science, University of Oxford, United Kingdom.

Medical Image Analysis

|September 13, 2025

Summary

This summary is machine-generated.

IterMask3D improves unsupervised anomaly detection in 3D brain MRIs by iteratively refining spatial masks. This novel approach reduces false positives and enhances the accuracy of anomaly segmentation and artifact detection.

Keywords:

3D brain MRI Anomaly detection Unsupervised anomaly segmentation

More Related Videos

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

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

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

Published on: January 7, 2019

Related Experiment Videos

Last Updated: Jan 17, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

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

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

Published on: January 7, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Unsupervised anomaly detection methods learn normal patterns to identify deviations as anomalies.
Current methods corrupt images for training, risking information loss and false positives.
3D brain MRI analysis requires robust methods for anomaly detection and segmentation.

Purpose of the Study:

To introduce IterMask3D, an iterative spatial mask-refining strategy for unsupervised anomaly detection and segmentation in 3D brain MRI.
To reduce false positives in anomaly detection by minimizing information loss during the learning process.
To enhance reconstruction performance by incorporating high-frequency image content.

Main Methods:

Iterative spatial masking and reconstruction of 3D brain MRI data.
Mask shrinking based on reconstruction error to progressively unmask normal regions.
Utilizing high-frequency image content to guide reconstruction of masked areas.

Main Results:

IterMask3D effectively detects synthetic and real-world imaging artifacts in 3D brain MRIs.
The method demonstrates strong performance in segmenting various pathological lesions across multiple MRI sequences.
Consistent effectiveness shown in extensive experiments, reducing false positives compared to prevailing methods.

Conclusions:

IterMask3D offers a significant advancement in unsupervised anomaly detection and segmentation for 3D brain MRI.
The iterative mask-refining strategy enhances accuracy and reduces false positives.
The method shows broad applicability for artifact detection and lesion segmentation in neuroimaging.