Cerebral Artery and Vein Segmentation in Four-dimensional CT Angiography Using Convolutional Neural Networks.

PURPOSE: To implement and test a deep learning approach for the segmentation of the arterial and venous cerebral vasculature with four-dimensional (4D) CT angiography. MATERIALS AND METHODS: Patients who had undergone 4D CT angiography for the suspicion of acute ischemic stroke were retrospectively identified. A total of 390 patients evaluated in 2014 (n = 113) or 2018 (n = 277) were included in this study, with each patient having undergone one 4D CT angiographic scan. One hundred patients from 2014 were randomly selected, and the arteries and veins on their CT scans were manually annotated by five experienced observers. The weighted temporal average and weighted temporal variance from 4D CT angiography were used as input for a three-dimensional Dense-U-Net. The network was trained with the fully annotated cerebral vessel artery-vein maps from 60 patients. Forty patients were used for quantitative evaluation. The relative absolute volume difference and the Dice similarity coefficient are reported. The neural network segmentations from 277 patients who underwent scanning in 2018 were qualitatively evaluated by an experienced neuroradiologist using a five-point scale. RESULTS: The average time for processing arterial and venous cerebral vasculature with the network was less than 90 seconds. The mean Dice similarity coefficient in the test set was 0.80 ± 0.04 (standard deviation) for the arteries and 0.88 ± 0.03 for the veins. The mean relative absolute volume difference was 7.3% ± 5.7 for the arteries and 8.5% ± 4.8 for the veins. Most of the segmentations (n = 273, 99.3%) were rated as very good to perfect. CONCLUSION: The proposed convolutional neural network enables accurate artery and vein segmentation with 4D CT angiography with a processing time of less than 90 seconds.© RSNA, 2020.

The value of (video)dermoscopy in the diagnosis and monitoring of common inflammatory skin diseases: a systematic review.

Clinical diagnosis of inflammatory skin disorders (ISD), including hair and nail disorders, is not always straightforward. Not uncommonly, a punch biopsy may be required. Dermoscopy and videodermoscopy (VD) are non-invasive techniques that are used for in vivo examination of the skin, hair, and nails. Both techniques can contribute to determining the accurate diagnosis of common ISD and can be useful for assessing treatment effects. However, the value of VD over conventional dermoscopy for ISD is undetermined. We systematically searched and reviewed the current published literature on ISD evaluated by VD and dermoscopy in the electronic databases, PubMed, Embase, the Cochrane Library, and Web of Science. All studies were assessed for quality using the Strengthening the Reporting of Observational studies in Epidemiology and Cochrane checklist. Finally, 82 studies were eligible for inclusion. An overview is presented of the (video)dermoscopic features for common ISD diagnoses, with details regarding the level of accuracy and features that should be monitored during treatment. Although both techniques are promising, studies of high methodological quality are necessary to determine the value of VD over conventional dermoscopy for common ISD.