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 增加對慢性淋巴細胞白血病合并非霍奇金淋巴瘤臨床病例的認識。 方法 通過報道2011年11月和2012年7月入住的2例確診為慢性淋巴細胞白血病合并非霍奇金淋巴瘤患者的診治過程，復習文獻，討論其發病機制、治療及預后。 結果 該2例患者均予以化療，其中1例淺表淋巴結明顯縮小，骨髓涂片基本恢復正常，病情控制較好；另1例合并癥多、病情惡化快、腫瘤化療效果欠佳，最后因呼吸衰竭死亡。 結論 慢性淋巴細胞白血病合并非霍奇金淋巴瘤，治療上應綜合考慮患者年齡、ECOG評分、臨床分期、預后指數等因素，原則上以治療惡性程度更高的非霍奇金淋巴瘤為主，可根據慢性淋巴細胞白血病分期進行觀察、隨訪或積極治療。