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This study examines the potential of Self-Supervised Learning (SSL) for segmenting the aorta and coronary arteries in Coronary Computed Tomography Angiography (CCTA) volumes to facilitate automated Pericoronary Adipose Tissue (PCAT) analysis. Utilizing 83 CCTA volumes, we explored the efficacy of SSL in a limited dataset environment. Forty-nine CCTA volumes were designated for SSL and supervised learning, while the remaining 34 formed a held-out test set. The Deep Learning (DL) model’s encoder was pretrained on unlabeled CCTA volumes during SSL and subsequently fine-tuned on labeled volumes in the supervised learning phase. This process enabled the DL model to learn feature representations without extensive annotations. The segmentation performance was assessed by varying the percentage of the 49 CCTA volumes used in supervised learning. With SSL, the model demonstrated a consistently higher segmentation performance than that of non-pretrained (random) weights, achieving a Dice of 0.866 with only 15 labeled volumes (30%) compared to a Dice of 0.864 with 44 labeled volumes (90%) required by random weights. Additionally, we segmented Pericoronary Adipose Tissue (PCAT), finding no significant differences in mean Hounsfield Unit (HU) attenuation between ground truth and predictions. The mean attenuation of PCAT-LAD and PCAT-RCA for ground truth were -79.97 HU (SD = 9.54) and -85.47 HU (SD = 8.41) respectively, indicating no statistically significant differences when compared to the predicted values of -80.11 HU (SD = 9.35) for PCAT-LAD and -86.19 HU (SD = 8.36) for PCAT-RCA. The findings suggest that in-domain SSL pretraining required about 66% less labeled volumes for comparable segmentation performance, thus offering a more efficient approach to leveraging limited dataset for DL applications in medical imaging.
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