Diseases such as diabetes and hypertension can lead to change the shape of the retinal blood vessels. Segmentation of fundus images is a key step in the process of quantitative analysis of the disease, which is instructive in the analysis and diagnosis of clinical diseases. In this paper, a method for the segmentation of retinal image vessels based on fully convolutional network (FCN) with depthwise separable convolution and channel weighting is presented. Firstly, CLAHE and Gamma correction of the green channel of the fundus image are used to enhance the contrast. Then, in order to adapt to network training, the enhanced image is divided into patches to expand the data. Finally, the depthwise separable convolution instead of the standard convolution method is used to increase the network width. Meanwhile, the channel weighting module is introduced to explicitly model the relationship between the characteristic channels in order to improve the distinguishability of the features. The combination of them is applied to the FCN and the results of expert manual identification are used to supervise the experiment on the DRIVE database. The results show that the segmentation accuracy of the proposed method in DRIVE database reached 0.963 0 and AUC reached 0.983 1. The segmentation accuracy in STARE database reached 0.962 0 and AUC achieved 0.983 0. To some extent, the proposed method has better feature resolution and better segmentation performance.
目的 增加对慢性淋巴细胞白血病合并非霍奇金淋巴瘤临床病例的认识。 方法 通过报道2011年11月和2012年7月入住的2例确诊为慢性淋巴细胞白血病合并非霍奇金淋巴瘤患者的诊治过程，复习文献，讨论其发病机制、治疗及预后。 结果 该2例患者均予以化疗，其中1例浅表淋巴结明显缩小，骨髓涂片基本恢复正常，病情控制较好；另1例合并症多、病情恶化快、肿瘤化疗效果欠佳，最后因呼吸衰竭死亡。 结论 慢性淋巴细胞白血病合并非霍奇金淋巴瘤，治疗上应综合考虑患者年龄、ECOG评分、临床分期、预后指数等因素，原则上以治疗恶性程度更高的非霍奇金淋巴瘤为主，可根据慢性淋巴细胞白血病分期进行观察、随访或积极治疗。