The count and recognition of white blood cells in blood smear images play an important role in the diagnosis of blood diseases including leukemia. Traditional manual test results are easily disturbed by many factors. It is necessary to develop an automatic leukocyte analysis system to provide doctors with auxiliary diagnosis, and blood leukocyte segmentation is the basis of automatic analysis. In this paper, we improved the U-Net model and proposed a segmentation algorithm of leukocyte image based on dual path and atrous spatial pyramid pooling. Firstly, the dual path network was introduced into the feature encoder to extract multi-scale leukocyte features, and the atrous spatial pyramid pooling was used to enhance the feature extraction ability of the network. Then the feature decoder composed of convolution and deconvolution was used to restore the segmented target to the original image size to realize the pixel level segmentation of blood leukocytes. Finally, qualitative and quantitative experiments were carried out on three leukocyte data sets to verify the effectiveness of the algorithm. The results showed that compared with other representative algorithms, the proposed blood leukocyte segmentation algorithm had better segmentation results, and the mIoU value could reach more than 0.97. It is hoped that the method could be conducive to the automatic auxiliary diagnosis of blood diseases in the future.
血涂片图像中白细胞的计数和识别对诊断包括白血病在内的血液疾病起着至关重要的作用。传统的人工检测结果容易受到多种因素的干扰，有必要开发白细胞自动分析系统为医生提供辅助诊断，而血液白细胞分割则是自动分析的基础。本文改进U-Net模型，提出一种基于双路径和空洞空间金字塔池化的血液白细胞分割算法。首先在特征编码器中引入双路径网络提取图像中白细胞的多尺度特征，并使用空洞空间金字塔池化模块强化网络的特征提取能力，再用卷积和反卷积组成特征解码器将分割目标恢复到原始图像大小，实现血液白细胞的像素级分割。最后在三个白细胞数据集上进行定性定量实验，验证本文算法的有效性。研究结果表明，提出的血液白细胞分割算法相对于其他典型方法具有更为优秀的分割结果，mIoU值能达到0.97以上，今后或有助于血液疾病的自动辅助诊断。.
Keywords: Atrous spatial pyramid pooling; Convolutional neural network; Dual path network; Image segmentation; White blood cell segmentation.