A label-free and data-free training strategy for vasculature segmentation in serial sectioning OCT data
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arXiv
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| Format: | Preprint |
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2024
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| author | Chollet, Etienne Balbastre, Yael Magnain, Caroline Fischl, Bruce Wang, Hui |
| author_facet | Chollet, Etienne Balbastre, Yael Magnain, Caroline Fischl, Bruce Wang, Hui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13757 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A label-free and data-free training strategy for vasculature segmentation in serial sectioning OCT data Chollet, Etienne Balbastre, Yael Magnain, Caroline Fischl, Bruce Wang, Hui Image and Video Processing Computer Vision and Pattern Recognition Machine Learning I.4.6; I.2.10; I.2.1; I.2.6; I.6.5; J.3 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. |
| title | A label-free and data-free training strategy for vasculature segmentation in serial sectioning OCT data |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning I.4.6; I.2.10; I.2.1; I.2.6; I.6.5; J.3 |
| url | https://arxiv.org/abs/2405.13757 |