Dynamic OCT angiography (OCTA) is an attractive approach for monitoring stimulus-evoked hemodynamics; however, a large dataset poses a great challenge to data processing. This study proposed a GPU-based real-time data processing pipeline for dynamic inverse SNR-decorrelation OCTA (ID-OCTA) with line-process rate of 133 kHz. Real-time processing enabled automatic optimization of angiogram quality, which improved the vessel SNR, contrast-to-noise ratio, and connectivity by 14.37, 14.08, and 9.76%, respectively. Furthermore, dynamic angiographic imaging of stimulus-evoked hemodynamics was achieved within a single trail in the mouse retina. Therefore, GPU ID-OCTA enables real-time and high-quality angiographic imaging and is particularly suitable for hemodynamic studies.
We investigated the correlation of the blood optical attenuation coefficient (OAC) and the blood glucose concentration (BGC). The blood OAC was measured in mouse retina in vivo through OCT angiography (OCTA). The arteries and veins presented a blood OAC change of ~0.05-0.07 mm-1 per 10 mg/dl and a significant elevation of blood OAC in diabetic mice was observed. Besides, the veins had a higher correlation coefficient between the measured blood OAC and BGC than that of the arteries. The blood OAC-BGC correlation suggests a concept of non-invasive OCTA-based glucometry, allowing a fast assessment of the blood glucose of specific vessels.
Malignant melanoma (MM) of the eyelid is of high malignancy, high mortality, and easy to metastasize. Currently, the gold standard for MM treatment and prognosis is histopathology, but the diagnosis of different experts is often divergent. The computer-aided diagnosis based on deep learning helps to improve efficiency and accuracy. In this paper, a complete set of methods for MM diagnosis is proposed using the convolutional neural network (CNN) to classify the patch level pathological images. Hematoxylin and Eosin (H and E)-stained pathological images of the eyelids are classified as malignant melanoma and non-malignant melanoma (NMM). The prediction results are filled by location in the probabilistic map of the whole slide image level. Random forest classifier based on CNN inference results extract 31- dimensional features to achieve whole slide image-level classification. The color constancy method and the edge extraction mapping method based on the Sobel operator (EMBS) can significantly improve the performance of the model. The patch level classification results show that the balance accuracy is 93% on the Second Affiliated Hospital, Zhejiang University School of Medicine (ZJU-2) test set, and the balance accuracy is 89.4% on the Shanghai Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine (SJTU) test set. The corresponding area under curve (AUC) is 0.990 and 0.970. For whole slide image level classification results, the AUC for SJTU test set is 0.999, the sensitivity is 100%, and the specificity is 97.4%. As a result, our model can effectively tackle the challenge of clinicopathological diagnosis and relieve the pressure of pathologists.
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