Previously submitted to: JMIR Formative Research (no longer under consideration since Nov 21, 2025)
Date Submitted: Feb 14, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
ASPSeg: Adaptive Space-Pruning Segmentation for Coronary Angiograms Sequences
ABSTRACT
Background:
Image segmentation is essential in analyzing coronary angiography (CAG) X-ray images. However, recent deep learning-based segmentation models are often too complex to be efficiently executed on edge devices.
Objective:
This study proposes a novel method called ASPSeg (Adaptive Space-Pruning Segmentation) to ac-celerate the segmentation of CAG sequences.
Methods:
ASPSeg is a top-down adaptive algorithm that dynamically splits images into patches based on video content, ensuring that computational resources are focused on regions of interest. The performance and accuracy of ASPSeg are enhanced by utilizing spatial-temporal information from video sequences. The method's effectiveness is evaluated on edge nodes.
Results:
The experimental results show that ASPSeg can reduce a CPU’s computational load by nearly 50% and 60%, respectively, with negligible accuracy degradation.
Conclusions:
ASPSeg is a complementary technique to other model reduction methods, such as pruning and quan-tization, providing an orthogonal approach that improves the efficiency of model inference.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.