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Previous works on segmentation of SEM (scanning electron microscope) bloodcell image ignore the semantic segmentation approach of whole-slide blood cellsegmentation. In the proposed work, we address the problem of whole-slide bloodcell segmentation using the semantic segmentation approach. We design a novelconvolutional encoder-decoder framework along with VGG-16 as the pixel-levelfeature extraction model. -e proposed framework comprises 3 main steps: First,all the original images along with manually generated ground truth masks ofeach blood cell type are passed through the preprocessing stage. In thepreprocessing stage, pixel-level labeling, RGB to grayscale conversion ofmasked image and pixel fusing, and unity mask generation are performed. Afterthat, VGG16 is loaded into the system, which acts as a pretrained pixel-levelfeature extraction model. In the third step, the training process is initiatedon the proposed model. We have evaluated our network performance on threeevaluation metrics. We obtained outstanding results with respect to classwise,as well as global and mean accuracies. Our system achieved classwise accuraciesof 97.45%, 93.34%, and 85.11% for RBCs, WBCs, and platelets, respectively,while global and mean accuracies remain 97.18% and 91.96%, respectively.

Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Image