White Blood Cell Detection and Segmentation Combined with the Channel Space Weighted Feature Pyramid Networks

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<p indent=0mm>The accurate detection and segmentation of leukocytes is a challenging task in medical image processing. The white blood cell image obtained under microscope is easily affected by impurities. There are many kinds of white cells, different shapes and small differences among them, and also exiting overlapping and adhesion phenomena, which leads to the inaccuracy of cell edge segmentation. The above problems are always the difficulties of detection and segmentation of white cell image. To solve the above problems, a leukocyte detection method based on Mask R-CNN and attention mechanism multi-scale feature fusion is proposed. Based on the Mask R-CNN structure, proposed thesis integrates the attention mechanism module in the FPN (feature pyramid networks) module, and proposes the CSFPN (channel spatial feature pyramid networks) structure. This structure can learn the weight of important channel features and the representation of important feature regions in the feature maps. At the same time, Skip-FPN module is added to the network structure, which fuses more low-level detailed information of leukocytes through short connection, so as to detect and segment leukocytes more accurately. Experimental results show that this method has good detection and segmentation performance. Under the Kaggle open source data set, the mAP value of this method for white blood cell detection reaches 98.25%, which has an increase of 1.25% compared with the previous improvement. The accuracy mIoU value reached 89.3%, which has an increase of 0.002% compared to the before improvement.