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

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Published on: January 7, 2019

Auto-segmentation of cerebral cavernous malformations using a convolutional neural network.

Chi-Jen Chou¹, Huai-Che Yang^2,3, Cheng-Chia Lee^2,3

¹Division of Neurosurgery, Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.

BMC Medical Imaging

|May 26, 2025

Summary

A new deep learning model automates cerebral cavernous malformations (CCMs) segmentation using MRI data. This AI tool shows promising results for clinical analysis and aids in classifying CCM types.

Keywords:

Cerebral cavernous malformations Deep learning DeepMedic Gamma knife (GK) treatment planning Mask region-based convolutional neural network

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Area of Science:

Medical Imaging
Artificial Intelligence
Neurosurgery

Background:

Cerebral cavernous malformations (CCMs) are vascular abnormalities requiring accurate segmentation for treatment planning.
Automated segmentation of CCMs can improve efficiency and consistency in clinical workflows.

Purpose of the Study:

To develop and evaluate a deep learning model for automated segmentation of cerebral cavernous malformations (CCMs).
To assess the model's performance across different CCM classifications and facilitate clinical application.

Main Methods:

A Mask R-CNN model was used for brain parenchyma extraction, followed by a 3D CNN (DeepMedic) for CCM segmentation.
The models were trained on 199 Gamma Knife (GK) exams, with manual annotations from neurosurgeons.
A user-friendly graphical user interface (GUI) was developed for clinical integration.

Main Results:

The brain parenchyma extraction model achieved a Dice similarity coefficient of 0.956 ± 0.002.
CCM segmentation using T2W images yielded an average Dice similarity coefficient of 0.741 ± 0.028.
The model demonstrated consistent performance across Zabramski Classification types I (0.743), II (0.742), and III (0.740).

Conclusions:

The developed deep learning model provides effective automated segmentation of CCMs.
The model's performance is sufficient across various CCM types, supporting its clinical utility.
The integrated GUI enhances the practical application of this AI tool in neurosurgical analysis.