An Encoder-Decoder Network with Residual Convolution for Leukocyte Image Segmentation

The appearance analysis and counting of peripheral blood leukocytes can assist the diagnosis of blood diseases such as leukemia. Therefore, it is necessary to automatically extract leukocytes from blood smear images. In this paper, we propose an end-to-end peripheral blood leukocyte image segmentation method based on fully convolutional neural network, which can not only encode multi-scale contextual information, but also capture clear boundary information through decoding. Specifically, the proposed method first utilizes the context-aware feature encoder with residual convolutions to extract a set of multi-scale features. Then, a refinement module of parallel dilated convolutions with multiple dilation rates for expanding the receptive field of the feature maps is introduced to aggregate contextual information. Finally, clear prediction results are reconstructed by the context-aware feature decoder and skip connections. We demonstrate the effectiveness of the proposed method on two publicly available datasets.