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

Abstract

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.

Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features

“…On ALL-IDB1-2 datasets, the O-NB classifier achieves maximum accuracy compared to the O-DA classifier. The experimental evaluation shows that the suggested technique outperforms as compared with existing methods [63,64] for the WBCs classification. The classification results of different types of WBCs as presented in Figure 18.…”

“…The classification results are depicted in Table 9. Table 9 demonstrates the comparison of the results with the existing approaches such as [63,64]. The existing methods achieve 97.1 ACC on ALL-IDB1 and 98.6 ACC on ALL-IDB2 datasets, whereas the proposed method achieves 97.2 ACC, 100 ACC on ALL-IDB1-2 datasets, respectively.…”

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues

“…A variety of neural net and other architectures have been explored to improve detection and classification accuracy and expand generalizability to new types of images. Several deep learning architectures developed for natural images have been adapted for marker detection in images of cells including Fully Convolutional Networks (FCNs) (Lux and Matula, 2020), Visual Geometry Group (VGG16) (Wang et al, 2019;Shahzad M et al, 2020), Residual Networks (ResNets) (Lee and Jeong, 2020), UNet (Al-Kofahi et al, 2018;McQuin et al, 2018;Schmidt et al, 2018;Wen et al, 2018;Vu et al, 2019;Horwath et al, 2020;Lugagne, Lin and Dunlop, 2020), and Mask R-CNN (Kromp et al, 2019;Vuola, Akram andKannala, 2019, 2019;Korfhage et al, 2020;Liu et al, 2020;Masubuchi et al, 2020). In classical image analysis, advances in methodology commonly involve the development of new algorithms; any changes in parameter settings needed to accommodate new data are regarded as project-specific details.…”

“…Nuclei can be identified in the absence of staining (Chalfoun et al, 2014;Stylianidou et al, 2016;Lux and Matula, 2020) but for fluorescence imaging, labeling with DAPI (4′,6-diamidino-2-phenylindole) or Hoechst 33342 is common; in transfected cells or transgenic animals, fluorescently tagged proteins such as histones can also be used (Al-Kofahi et al, 2018;Schmidt et al, 2018;Vuola, Akram and Kannala, 2019;Wang et al, 2019;. In classical histology, nuclei are labelled along with other structures using chromogenic dyes such as H&E (Al-Kofahi et al, 2010;Qi et al, 2012;Xu, Lu and Mandal, 2014;Chen et al, 2016;Xu et al, 2017;Lee and Jeong, 2020;Liu et al, 2020;Shahzad M et al, 2020).…”

White Blood Cells Detection using YOLOv3 with CNN Feature Extraction Models

“…A different comparison had been made between the VGG16 and Resnet50 for WBCs classification [16] and the Resnet50 achieved 88.29% of accuracy. One of the project that utilized VGG16 is by M. Shahzad [17]. The framework starts with feeding the original images and ground truth images to the preprocessing stage.…”