Automatic hemorrhage segmentation on head CT scan for traumatic brain injury using 3D deep learning model.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neurosurgery

Background:

• Traumatic brain injury (TBI) is a leading cause of death and disability in young adults.
• Surgical intervention decisions for TBI, such as craniotomy, depend on injury type and neurological examination.
• Intracranial hemorrhage subtypes (subdural, epidural, intraparenchymal) often require surgical management.

Purpose of the Study:

• To develop and evaluate a novel automatic method for segmenting traumatic brain injury hemorrhage subtypes on CT scans.
• To integrate CT scans with bone window settings as input for a deep learning model.
• To improve the accuracy and efficiency of hemorrhage segmentation for surgical planning.

Main Methods:

• A deep learning model, a 3D convolutional neural network with four parallel pathways, was utilized for segmentation.
• Brain CT scans from adult patients were preprocessed using subdural and bone window settings.
• The model was trained on a dataset and segmentation results were refined using post-processing techniques, including a region-growing algorithm.

Main Results:

• The proposed method achieved median Dice similarity coefficients greater than 0.37 for all hemorrhage subtypes.
• The deep learning model demonstrated improved segmentation performance compared to existing literature.
• The integration of bone window CT scans enhanced the segmentation capabilities.

Conclusions:

• The novel automatic deep learning method effectively segments traumatic brain injury hemorrhage subtypes.
• The approach shows promise for improving diagnostic accuracy and aiding surgical decision-making in TBI cases.
• This method offers a significant advancement over previous techniques for CT-based hemorrhage segmentation.