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

## TL;DR

A deep learning model is developed to automatically segment cerebral cavernous malformations in brain MRIs, showing strong performance across different classifications.

## Contribution

A novel deep learning pipeline using Mask R-CNN and DeepMedic for automated CCM segmentation with a clinical GUI.

## Key findings

- The brain parenchyma extraction model achieved a Dice similarity coefficient of 0.956 ± 0.002.
- CCM segmentation using T2W images achieved an average Dice similarity coefficient of 0.741 ± 0.028.
- A user-friendly GUI was developed to support clinical use of the segmentation models.

## Abstract

This paper presents a deep learning model for the automated segmentation of cerebral cavernous malformations (CCMs).

The model was trained using treatment planning data from 199 Gamma Knife (GK) exams, comprising 171 cases with a single CCM and 28 cases with multiple CCMs. The training data included initial MRI images with target CCM regions manually annotated by neurosurgeons. For the extraction of data related to the brain parenchyma, we employed a mask region-based convolutional neural network (Mask R-CNN). Subsequently, this data was processed using a 3D convolutional neural network known as DeepMedic.

The efficacy of the brain parenchyma extraction model was demonstrated via five-fold cross-validation, resulting in an average Dice similarity coefficient of 0.956 ± 0.002. The segmentation models used for CCMs achieved average Dice similarity coefficients of 0.741 ± 0.028 based solely on T2W images. The Dice similarity coefficients for the segmentation of CCMs types were as follows: Zabramski Classification type I (0.743), type II (0.742), and type III (0.740). We also developed a user-friendly graphical user interface to facilitate the use of these models in clinical analysis.

This paper presents a deep learning model for the automated segmentation of CCMs, demonstrating sufficient performance across various Zabramski classifications.

not applicable.

The online version contains supplementary material available at 10.1186/s12880-025-01738-6.