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Updated: Dec 24, 2025

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

Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation.

Théo Estienne^1,2,3,4, Marvin Lerousseau^1,2,3,5, Maria Vakalopoulou^1,4,5

¹Gustave Roussy-CentraleSupélec-TheraPanacea Center of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France.

Frontiers in Computational Neuroscience

|April 9, 2020

Summary

This summary is machine-generated.

This study introduces a novel deep learning method for joint brain tumor segmentation and medical image registration. The approach improves registration accuracy within tumor regions, offering a versatile solution for medical image analysis.

Keywords:

brain tumor segmentation convolutional neural networks deep learning deformable registration multi-task networks

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Area of Science:

Medical Image Analysis
Deep Learning
Neuroimaging

Background:

Image registration and segmentation are critical in medical imaging.
Deep learning has shown state-of-the-art performance in various image analysis tasks.
Existing methods often address registration and segmentation separately.

Purpose of the Study:

To propose a novel, efficient, multi-task algorithm for joint image registration and brain tumor segmentation.
To exploit interdependencies between registration and segmentation for improved performance.
To develop a generic method applicable to abnormal brain volumes.

Main Methods:

A multi-task deep learning framework integrating registration and segmentation.
Relaxation of similarity constraints within tumor regions during inference.
Joint optimization exploiting task interdependencies.

Main Results:

Competitive quantitative and qualitative results on BraTS 2018 and OASIS 3 datasets.
Significant improvement (p < 0.005) in registration performance within tumor areas.
Demonstrated robustness without predefined conditions on brain volumes.

Conclusions:

The proposed joint framework effectively addresses registration and segmentation simultaneously.
The method enhances registration accuracy in challenging tumor regions.
This approach offers a generalized solution for medical image analysis of brain abnormalities.