ArticleComputational and mathematical methods in medicine2020
Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images.
Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- AneRBC dataset: a benchmark dataset for computer-aided anemia diagnosis using RBC images.Database : the journal of biological databases and curation · 2024Article
- Nucleus segmentation of white blood cells in blood smear images by modeling the pixels' intensities as a set of three Gaussian distributions.Medical & biological engineering & computing · 2024Article
- Blood cell image segmentation and classification: a systematic review.PeerJ. Computer science · 2024Article
- A lightweight network based on dual-stream feature fusion and dual-domain attention for white blood cells segmentation.Frontiers in oncology · 2023Article
- Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review.Medical & biological engineering & computing · 2022Review
- Using DeepLab v3 + -based semantic segmentation to evaluate platelet activation.Medical & biological engineering & computing · 2022Article
- WBC-based segmentation and classification on microscopic images: a minor improvement.F1000Research · 2021Article
- Systematic segmentation method based on PCA of image hue features for white blood cell counting.PloS one · 2021Article
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Authors and funding
6 authors.
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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. The 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.
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