Stroke Locus Net: Occluded Vessel Localization from MRI Modalities
This addresses the challenge of vessel localization in stroke diagnosis for medical imaging, but it appears incremental as it builds on existing methods like nnUNet and pGAN.
The paper tackled the problem of accurately localizing occluded vessels in ischemic stroke diagnosis using MRI scans, introducing Stroke Locus Net, which demonstrated promising results for faster and more informed diagnosis.
A key challenge in ischemic stroke diagnosis using medical imaging is the accurate localization of the occluded vessel. Current machine learning methods in focus primarily on lesion segmentation, with limited work on vessel localization. In this study, we introduce Stroke Locus Net, an end-to-end deep learning pipeline for detection, segmentation, and occluded vessel localization using only MRI scans. The proposed system combines a segmentation branch using nnUNet for lesion detection with an arterial atlas for vessel mapping and identification, and a generation branch using pGAN to synthesize MRA images from MRI. Our implementation demonstrates promising results in localizing occluded vessels on stroke-affected T1 MRI scans, with potential for faster and more informed stroke diagnosis.