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Related Experiment Video

Updated: Mar 30, 2026

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

Within-brain classification for brain tumor segmentation.

Mohammad Havaei¹, Hugo Larochelle², Philippe Poulin²

¹Université de Sherbrooke, 2500 Boul. de l'Université, Sherbrooke, QC, J1K 2R1, Canada. mohammad.havaei@gmail.com.

International Journal of Computer Assisted Radiology and Surgery

|November 5, 2015

Summary

This summary is machine-generated.

This study introduces a novel machine learning framework for interactive brain tumor segmentation. By training within each individual brain, the method achieves high accuracy with reduced computational resources.

Keywords:

Brain tumor segmentation Computer-aided detection Interactive Machine learning Segmentation Within-brain generalization

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Area of Science:

Medical imaging
Machine learning
Computational neuroscience

Background:

Brain tumor segmentation is crucial for diagnosis and treatment planning.
Traditional machine learning methods struggle with inter-brain variability and MRI noise.
Existing methods often require extensive manual correction and computational power.

Purpose of the Study:

To develop an interactive framework for brain tumor segmentation using machine learning.
To address limitations of cross-brain generalization in current segmentation techniques.
To improve segmentation accuracy and efficiency through within-brain generalization.

Main Methods:

Proposed a semi-automatic, interactive segmentation method.
Implemented within-brain generalization, training and applying models to each brain individually.
Incorporated spatial feature coordinates (i, j, k) alongside intensity features.
Investigated optimized kernels and adapted hyperparameters for each specific brain.

Main Results:

Achieved the second-highest accuracy on the MICCAI-BRATS 2013 dataset among published methods.
Demonstrated significant reductions in memory and processing power compared to state-of-the-art approaches.
Showcased the effectiveness of spatial features and within-brain adaptation.

Conclusions:

Spatial features significantly enhance classification performance in interactive segmentation.
Within-brain generalization is a viable strategy to overcome MRI-specific noise and bias.
The developed interactive framework offers a computationally efficient and accurate solution for brain tumor segmentation.