Segmentation of Chronic Subdural Hematomas Using 3D Convolutional Neural Networks.

Area of Science:

• Neurosurgery
• Radiology
• Artificial Intelligence

Background:

• Chronic subdural hematomas (cSDHs) are a growing neurological concern requiring surgical intervention.
• Computed tomography (CT) is crucial for managing cSDHs, with serial imaging guiding treatment.
• Hematoma volume is key for therapy decisions and evaluating new management strategies.

Purpose of the Study:

• To develop an automated program for calculating cSDH volume from CT scans.
• To enable precise pre- and postoperative hematoma volume measurements.
• To create a tool for efficient evaluation of cSDH treatment efficacy.

Main Methods:

• A convolutional neural network (CNN) was trained using 21,710 images from 128 CT scans.
• The dataset included pre- and postoperative coronal head CTs of patients with cSDHs.
• Manual segmentation was performed to train the automated cSDH segmentation model.

Main Results:

• The best CNN model achieved a DICE score of 0.8351 on the test dataset.
• An average DICE score of 0.806 ± 0.06 was obtained on the validation set.
• Model performance was optimized with specific network depth and residual block configurations.

Conclusions:

• A CNN was successfully trained for automated cSDH segmentation on CT scans.
• This automated tool can provide accurate measurements for assessing treatment effectiveness.
• The developed AI assists in managing this prevalent neurological disease.