Leukocyte segmentation based on DenseREU-Net

The detection of white blood cells provides important information in cellular research regarding infections, inflammation, immune function, and blood pathologies. Effective segmentation of WBCs in blood microscopic images not only aids pathologists in making more accurate diagnoses and early detections but is also crucial for identifying the types of lesions. Due to significant differences among various types of pathological WBCs and the complexities associated with cellular imaging and staining techniques, accurately recognizing and segmenting these different types of WBCs remains challenging. To address these challenges, this paper proposes a WBC segmentation technique based on DenseREU-Net, which enhances feature exchange and reuse by employing Dense Blocks and residual units. Additionally, it introduces mixed pooling and skip multi-scale fusion modules to improve the recognition and segmentation accuracy of different types of pathological WBCs. This study was validated on two datasets provided by DML-LZWH (Liuzhou Workers’ Hospital Medical Laboratory). Experimental results indicate that on the multi-class dataset, DenseREU-Net achieves an average IoU of 73.05% and a Dice coefficient of 80.25%. For the binary classification blind sample dataset, the average IoU and Dice coefficient are 83.98% and 90.41%, respectively. In both datasets, the proposed model significantly outperforms other advanced medical image segmentation algorithms. Overall, DenseREU-Net effectively analyzes blood microscopic images and accurately recognizes and segments different types of WBCs, providing robust support for the diagnosis of blood-related diseases. •The DenseREU-Net uses Densely Connected Convolutional Block in the encoder for better feature reuse and representation.•This paper replaces the convolution block with residual unit and uses Convolutional Block Attention Module for adaptive feature optimization.•This paper adopts Hybrid Pooling in the input layer to fuse max and average pooling.•This paper replaces UNet’s middle layer with Jump Multi-scale Fusion Module, using skip connections for better detail.