This paper presents a kind of automatic segmentation method for white blood cell based on HSI corrected space information fusion. Firstly, the original cell image is transformed to HSI colour space conversion. Because the transformation formulas of H component piecewise function was discontinuous, the uniformity of uniform visual cytoplasm area in the original image was lead to become lower in this channel. We then modified formulas, and then fetched information of nucleus, cytoplasm, red blood cells and background region according to distribution characteristics of the H, S and I-channel, using the theory and method of information fusion to build fusion imageⅠand fusion imageⅡ, which only contained cytoplasm and a small amount of interference, and fetched nucleus and cytoplasm respectively. Finally, we marked the nucleus and cytoplasm region and obtained the final result of segmentation. The simulation results showed that the new algorithm of image segmentation for white blood cell had high accuracy, robustness and universality.
The number of white blood cells in the leucorrhea microscopic image can indicate the severity of vaginal inflammation. At present, the detection of white blood cells in leucorrhea mainly relies on manual microscopy by medical experts, which is time-consuming, expensive and error-prone. In recent years, some studies have proposed to implement intelligent detection of leucorrhea white blood cells based on deep learning technology. However, such methods usually require manual labeling of a large number of samples as training sets, and the labeling cost is high. Therefore, this study proposes the use of deep active learning algorithms to achieve intelligent detection of white blood cells in leucorrhea microscopic images. In the active learning framework, a small number of labeled samples were firstly used as the basic training set, and a faster region convolutional neural network (Faster R-CNN) training detection model was performed. Then the most valuable samples were automatically selected for manual annotation, and the training set and the corresponding detection model were iteratively updated, which made the performance of the model continue to increase. The experimental results show that the deep active learning technology can obtain higher detection accuracy under less manual labeling samples, and the average precision of white blood cell detection could reach 90.6%, which meets the requirements of clinical routine examination.
Objective To investigate the risk factors for early in-hospital death in patients with acute Stanford type A aortic dissection and emergency surgical treatment. MethodsWe retrospectively analyzed the clinical data of 189 patients with acute Stanford type A aortic dissection who underwent surgery in the First Affiliated Hospital of Xinjiang Medical University between January 2017 and January 2020. There were 160 males and 29 females with an average age of 46.35±9.17 years. All patients underwent surgical treatment within 24 hours. The patients were divided into a survival group (n=160) and a death group (n=29) according to their outcome (survival or death) during hospitalization in our hospital. Perioperative clinical data were analyzed and compared between the two groups. Results The overall in-hospital mortality was 15.34% (29/189). There was a statistical difference between the two groups in white blood cell count, blood glucose, aspartate aminotransferase (AST), bilirubin, creatinine, operative method, operation time, aortic occlusion time, or cardiopulmonary bypass time (P<0.05). Multivariate regression identified white blood cell count [OR=1.142, 95%CI (1.008, 1.293)], bilirubin [OR=0.906, 95%CI (0.833, 0.985)], creatinine [OR=1.009, 95%CI (1.000, 1.017)], cardiopulmonary bypass time [OR=1.013, 95%CI (1.003, 1.024)] as postoperative risk factors for early in-hospital death in the patients undergoing acute Stanford type A aortic dissection surgery (P<0.05). Conclusion Our study demonstrated that white blood cell, bilirubin, creatinine and cardiopulmonary bypass time are independent risk factors for in-hospital death after acute Stanford type A aortic dissection surgery.