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

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Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor

Jose Dolz^1,2, Anne Laprie³, Soléakhéna Ken³

¹AQUILAB, Biocentre A. Fleming, 250 rue Salvador Allende, 59120, Loos les Lille, France. jose.dolz.upv@gmail.com.

International Journal of Computer Assisted Radiology and Surgery

|July 25, 2015

Summary

This study introduces a machine learning approach using support vector machines (SVM) for precise brainstem segmentation on MRI scans. The method significantly reduces segmentation time and improves accuracy, showing promise for clinical applications in radiotherapy.

Keywords:

Brain cancer MRI segmentation Machine learning Radiotherapy Supervised learning Support vector machines

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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

Area of Science:

Medical Imaging
Machine Learning in Medicine
Radiotherapy Planning

Background:

Precise organ delineation is crucial for minimizing radiotherapy toxicity.
Manual segmentation of organs at risk is time-consuming and prone to inter-observer variability.
Machine learning, specifically Support Vector Machines (SVM), offers an alternative to traditional atlas-based segmentation.

Purpose of the Study:

To develop and evaluate an SVM-based method for segmenting the brainstem on MRI scans.
To assess the performance of SVM using various image intensity value configurations.
To compare the proposed method against existing techniques for brainstem segmentation.

Main Methods:

Support Vector Machines (SVM) classifier was employed for brainstem segmentation on 14 adult brain MRI scans.
Evaluated five different image intensity value (IIV) configurations as features for SVM training.
Segmentation accuracy was quantified using Dice Similarity Coefficient (DSC) and Absolute Volume Difference (AVD).

Main Results:

Mean DSC values ranged from 0.89 to 0.90 across all IIV configurations.
Mean AVD was below 1.5 cm³, with the best configuration achieving 0.85 cm³ (3.99% difference).
The proposed SVM approach demonstrated superior performance in both accuracy and segmentation time compared to existing methods.

Conclusions:

The SVM-based segmentation provides consistent volume estimation and high spatial similarity to expert delineations.
The method shows potential for clinical adoption due to its efficiency and accuracy.
This approach offers a promising alternative for automated brainstem segmentation in neuro-oncology.