Automated Computer Vision Methods for Image Segmentation, Stereotactic Localization, and Functional Outcome...

Area of Science:

• Neurosurgery
• Medical Imaging
• Artificial Intelligence

Background:

• Basal ganglia intracranial hemorrhage (bgICH) morphology impacts postoperative functional outcomes.
• Minimally invasive surgical (MIS) evacuation is a key treatment for bgICH.
• Accurate spatial representation of bgICH is crucial for predicting outcomes.

Purpose of the Study:

• To automate bgICH spatial representation modeling for functional outcome prediction after MIS evacuation.
• To develop and validate computer vision models for bgICH segmentation and localization.
• To assess the performance of automated models in predicting functional outcomes compared to manual methods.

Main Methods:

• Convolutional neural network (CNN) models were trained for key-point detection and instance segmentation using CT and CT angiography images.
• Anatomic landmarks were identified to establish a universal stereotactic reference frame.
• Models were tested on patient data undergoing MIS bgICH evacuation, correlating predictions with modified Rankin Scale scores.

Main Results:

• Automated segmentation closely correlated with manual segmentation (R²=0.95), with a median volume difference of 2 mL.
• Median localization accuracy was 4 mm, with highly correlated landmark coordinates across all axes.
• Automated models predicted functional outcomes (mRS 4-6) with performance comparable to manual models (AUC 0.81 vs. 0.84).

Conclusions:

• Computer vision models can accurately replicate manual segmentation and stereotactic localization of bgICH.
• Automated models effectively prognosticate functional outcomes after MIS bgICH evacuation.
• This technology holds promise for enhancing surgical planning and patient management in neurosurgery.