  Previous works on segmentation of SEM (scanning electron microscope) blood
cell image ignore the semantic segmentation approach of whole-slide blood cell
segmentation. In the proposed work, we address the problem of whole-slide blood
cell segmentation using the semantic segmentation approach. We design a novel
convolutional encoder-decoder framework along with VGG-16 as the pixel-level
feature extraction model. -e proposed framework comprises 3 main steps: First,
all the original images along with manually generated ground truth masks of
each blood cell type are passed through the preprocessing stage. In the
preprocessing stage, pixel-level labeling, RGB to grayscale conversion of
masked image and pixel fusing, and unity mask generation are performed. After
that, VGG16 is loaded into the system, which acts as a pretrained pixel-level
feature extraction model. In the third step, the training process is initiated
on the proposed model. We have evaluated our network performance on three
evaluation metrics. We obtained outstanding results with respect to classwise,
as well as global and mean accuracies. Our system achieved classwise accuracies
of 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.
