Background As of June 1, 2020, coronavirus disease 2019 (COVID-19) has caused more than 6,000,000 infected persons and 360,000 deaths globally. Previous studies revealed pregnant women with COVID-19 had similar clinical manifestations to nonpregnant women. However, little is known about the outcome of neonates born to infected women. Methods and findings In this retrospective study, we studied 29 pregnant women with COVID-19 infection delivered in 2 designated general hospitals in Wuhan, China between January 30 and March 10, 2020, and 30 neonates (1 set of twins). Maternal demographic characteristics, delivery course, symptoms, and laboratory tests from hospital records were extracted. Neonates were hospitalized if they had symptoms (5 cases) or their guardians agreed to a hospitalized quarantine (13 cases), whereas symptom-free neonates also could be discharged after birth and followed up through telephone (12 cases). For hospitalized neonates, laboratory test results and chest X-ray or computed tomography (CT) were extracted from hospital records. The presence of antibody of SARS-CoV-2 was assessed in the serum of 4 neonates. Among 29 pregnant COVID-19-infected women (13 confirmed and 16 clinical diagnosed), the majority had higher education (56.6%), half were employed (51.7%), and their mean age was 29 years. Fourteen women experienced mild symptoms including fever (8), cough (9), shortness of breath (3), diarrhea (2), vomiting (1), and 15 were symptom-free.
Minimal path techniques can efficiently extract geometrically curve-like structures by finding the path with minimal accumulated cost between two given endpoints. Though having found wide practical applications (e.g., line identification, crack detection, and vascular centerline extraction), minimal path techniques suffer from some notable problems. The first one is that they require setting two endpoints for each line to be extracted (endpoint problem). The second one is that the connection might fail when the geodesic distance between the two points is much shorter than the desirable minimal path (shortcut problem). In addition, when connecting two distant points, the minimal path connection might become inefficient as the accumulated cost increases over the propagation and results in leakage into some non-feature regions near the starting point (accumulation problem). To address these problems, this paper proposes an approach termed minimal path propagation with backtracking. We found that the information in the process of backtracking from reached points can be well utilized to overcome the above problems and improve the extraction performance. The whole algorithm is robust to parameter setting and allows a coarse setting of the starting point. Extensive experiments with both simulated and realistic data are performed to validate the performance of the proposed method.
Automatic and accurate esophageal lesion classification and segmentation is of great significance to clinically estimate the lesion status of esophageal disease and make suitable diagnostic schemes. Due to individual variations and visual similarities of lesions in shapes, colors and textures, current clinical methods remain subject to potential high-risk and time-consumption issues. In this paper, we propose an Esophageal Lesion Network (ELNet) for automatic esophageal lesion classification and segmentation using deep convolutional neural networks (DCNNs). The underlying method automatically integrates dual-view contextual lesion information to extract global features and local features for esophageal lesion classification of four esophageal image types (Normal, Inflammation, Barrett, and Cancer) and proposes lesion-specific segmentation network for automatic esophageal lesion annotation of three esophageal lesion types at pixel level. For established clinical large-scale database of 1051 white-light endoscopic images, ten-fold cross-validation is used in method validation. Experiment results show that the proposed framework achieves classification with sensitivity of 0.9034, specificity of 0.9718 and accuracy of 0.9628, and the segmentation with sensitivity of 0.8018, specificity of 0.9655 and accuracy of 0.9462. All of these indicate that our method enables an efficient, accurate and reliable esophageal lesion diagnosis in clinical.The main contributions of our work can be generalized as follows: 1 For the first time, proposed ELNet enables an automatically and reliably comprehensive esophageal lesions classification of four esophageal lesion types (Normal, Inflammation, Barrett, and Cancer) and lesion-specific segmentation from clinically white-light esophageal images to make suitable and repaid diagnostic schemes for clinicians. 2 A novel Dual-Stream network (DSN) is proposed for esophageal lesion classification. DSN automatically integrates dual-view contextual lesion information using two CNN streams to complementarily extract the global features from the holistic esophageal images and the local features from the lesion patches. 3 Lesion-specific esophageal lesion annotation with Segmentation Network with Classification (SNC) strategy is proposed to automatically annotate three lesion types (Inflammation, Barrett, Cancer) at pixel level to reduce the intra-class differences of esophageal lesions. 4 A clinically large-scale database esophageal database is established for esophageal lesions classification and segmentation. This database includes 1051 white-light esophageal images, which consists of endoscopic images in four different lesion types. Each image in this database has a classification label and its corresponding segmentation annotation.
Diabetic retinopathy (DR) is the most common eye complication of diabetes and one of the leading causes of blindness and vision impairment. Automated and accurate DR grading is of great significance for the timely and effective treatment of fundus diseases. Current clinical methods remain subject to potential time-consumption and high-risk. In this paper, a hierarchically Coarse-to-fine network (CF-DRNet) is proposed as an automatic clinical tool to classify five stages of DR severity grades using convolutional neural networks (CNNs). The CF-DRNet conforms to the hierarchical characteristic of DR grading and effectively improves the classification performance of five-class DR grading, which consists of the following: (1) The Coarse Network performs two-class classification including No DR and DR, where the attention gate module highlights the salient lesion features and suppresses irrelevant background information. (2) The Fine Network is proposed to classify four stages of DR severity grades of the grade DR from the Coarse Network including mild, moderate, severe non-proliferative DR (NPDR) and proliferative DR (PDR). Experimental results show that proposed CF-DRNet outperforms some stateof-art methods in the publicly available IDRiD and Kaggle fundus image datasets. These results indicate our method enables an efficient and reliable DR grading diagnosis in clinic.
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