Effective contrast-enhanced preprocessing for intracranial artery segmentation in digital subtraction angiography

Area of Science:

• Medical Imaging
• Computer Vision
• Neuroscience

Background:

• Digital Subtraction Angiography (DSA) is vital for visualizing intracranial arteries.
• Accurate segmentation of DSA images is challenging, especially for thin and branching vessels.
• Existing preprocessing methods may not adequately preserve vascular connectivity.

Purpose of the Study:

• To develop and evaluate a novel contrast-enhanced preprocessing method for DSA.
• To improve intracranial artery segmentation, focusing on vascular connectivity preservation.
• To assess the method's effectiveness across different deep learning segmentation models.

Main Methods:

• A haze-inspired contrast enhancement technique was developed, including transmission-map estimation and wavelet-based suppression.
• The method was evaluated against no preprocessing and CLAHE (Contrast-Limited Adaptive Histogram Equalization).
• Segmentation was performed using U-Net, U-Net++, and nnU-Net models, with evaluation metrics including overlap and vascular connectivity (VC).

Main Results:

• The proposed method significantly improved vascular connectivity (VC) error, reducing it by up to 55% in the nnU-Net model.
• VC error decreased from 39.05 (no preprocessing) to 17.58 with the proposed method.
• While modest improvements were seen in overlap metrics, the method excelled at reducing vessel fragmentation and preserving thin branches.

Conclusions:

• The proposed preprocessing framework offers a model-agnostic approach to enhance topology-sensitive DSA vessel segmentation.
• The method prioritizes vascular continuity, which is clinically significant for cerebrovascular interpretation and surgical planning.
• This technique is effective in reducing fragmentation and maintaining the integrity of thin vascular structures in DSA.