2024/05/22 by Etienne Chollet, Yaël Balbastre, Chollet, Etienne +7
Engineering · Medicine · #Cerebrovascular and Carotid Artery Diseases #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #I.2.1 #I.2.10 #I.2.6 #I.4.6 #I.6.5 #Image and Video Processing (eess.IV) #J.3 #Machine Learning (cs.LG) #Optical Coherence Tomography Applications #Retinal Imaging and Analysis #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2405.13757
openalex publication_date 2024/05/22 · openalex created_date 2024/05/25 · openalex updated_date 2026/07/28
Serial sectioning Optical Coherence Tomography (sOCT) is a high-throughput, label free microscopic imaging technique that is becoming increasingly popular to study post-mortem neurovasculature. Quantitative analysis of the vasculature requires highly accurate segmentation; however, sOCT has low signal-to-noise-ratio and displays a wide range of contrasts and artifacts that depend on acquisition parameters. Furthermore, labeled data is scarce and extremely time consuming to generate. Here, we leverage synthetic datasets of vessels to train a deep learning segmentation model. We construct the vessels with semi-realistic splines that simulate the vascular geometry and compare our model with realistic vascular labels generated by constrained constructive optimization. Both approaches yield similar Dice scores, although with very different false positive and false negative rates. This method addresses the complexity inherent in OCT images and paves the way for more accurate and efficient analysis of neurovascular structures.