A label-free and data-free training strategy for vasculature segmentation in serial sectioning OCT data
This addresses the challenge of accurate neurovascular analysis for researchers using OCT imaging, though it is incremental as it adapts existing synthetic data methods to a specific domain.
The paper tackled the problem of segmenting vasculature in serial sectioning OCT data, which suffers from low signal-to-noise and scarce labeled data, by using synthetic datasets to train a deep learning model, achieving similar Dice scores with different false positive and negative rates compared to realistic labels.
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.