Chronic venous insufficiency (CVI) ranks among the most common health care issues worldwide. The current diagnosis of CVI is done by clinical examination and duplex ultrasound, which can only detect visible physical changes and deeper vascular structures whereas the superficial cutaneous vasculature cannot be resolved. There is indeed a lack of information that can potentially be extracted from the cutaneous microvasculature of patients affected by CVI. In this work, we designed and applied an optical coherence tomography angiography (OCTA) system, which is customized for lower extremity imaging of patients. Featuring fast imaging speed, large field of view, high spatial resolution, and most importantly non-invasiveness, this OCTA system was successfully applied in CVI and venous leg ulcer patient imaging. Using the OCTA results acquired from a cohort of 27 human subjects, we can clearly distinguish the vascular patterns uniquely associated with various stages of CVI. The findings of this study give an unexplored indicator to the disease of CVI and venous leg ulcer. With more patients to be recruited, we believe that OCTA imaging results for CVI can be used as a powerful tool in CVI screening and diagnosis.
Deep learning methods are the state-of-the-art for medical imaging segmentation tasks. Still, numerous segmentation algorithms based on heuristic-based methods have been proposed with exceptional results. To validate segmentation algorithms, manual annotations are typically considered as ground truth. However, manual annotations often suffer from inter/intra-operator variability and can also be occasionally inaccurate, especially when considering time-consuming and precise tasks. A sample case is the manual delineation of the lumen-intima (LI) and media-adventitia (MA) borders for intima-media thickness (IMT) measurement in B-mode ultrasound images. In this work, a novel hybrid learning paradigm which combines manual segmentations with the automatic segmentation of a dynamic programming technique for ground truth determination is presented. A profile consensus strategy is proposed to construct the hybrid ground truth. Two open-source datasets (n=2576) were employed for training four deep learning networks using the hybrid learning paradigm and three single source training targets as a comparison. The pipeline was fixed across the four tests and included a Faster R-CNN detection network to locate the carotid artery and then subsequent division into patches which were segmented using a UNet. The validation of the results was performed on an external test set comparing the predictions of the four different models to the annotations of three independent manual operators. The hybrid learning paradigm showed the best overall segmentation results (Dice=0.907±0.037, p<0.001) and demonstrated an exceptional correlation between the mean of three operators and the automatic measure (ICC(2,1)=0.958), demonstrating how the incorporation of heuristic-based segmentation methods within the learning paradigm of a deep neural network can enhance and improve final segmentation performance results.
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