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.
Diabetic foot ulcer (DFU) is a diabetic complication due to peripheral vasculopathy and neuropathy. A promising technology for wound healing in DFU is low-level light therapy (LLLT). Despite several studies showing positive effects of LLLT on DFU, LLLT’s physiological effects have not yet been studied. The objective of this study was to investigate vascular and nervous systems modification in DFU after LLLT. Two samples of 45 DFU patients and 11 healthy controls (HCs) were recruited. The total hemoglobin (totHb) concentration change was monitored before and after LLLT by near-infrared spectroscopy and analyzed in time and frequency domains. The spectral power of the totHb changes in the very-low frequency (VLF, 20 to 60 mHz) and low frequency (LF, 60 to 140 mHz) bandwidths was calculated. Data analysis revealed a mean increase of totHb concentration after LLLT in DFU patients, but not in HC. VLF/LF ratio decreased significantly after the LLLT period in DFU patients (indicating an increased activity of the autonomic nervous system), but not in HC. Eventually, different treatment intensities in LLLT therapy showed a different response in DFU. Overall, our results demonstrate that LLLT improves blood flow and autonomic nervous system regulation in DFU and the importance of light intensity in therapeutic protocols.
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