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Deep Learning-Based Semantic Segmentation of Blood Cells from Microscopic Images

Asha, S. B. ; Gopakumar, G.

Big Data, Machine Learning, and Applications, 2023, Vol.1053, p.381-394

Singapore: Springer Singapore Pte. Limited

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  • Título:
    Deep Learning-Based Semantic Segmentation of Blood Cells from Microscopic Images
  • Autor: Asha, S. B. ; Gopakumar, G.
  • Assuntos: Biomedical image processing ; Deep learning ; Microscopic image analysis ; Semantic segmentation
  • É parte de: Big Data, Machine Learning, and Applications, 2023, Vol.1053, p.381-394
  • Descrição: Morphological analysis and differential cell counting are important in characterizing many diseases including malaria and leukaemia. The basic building block involved is cell segmentation and is a challenging but beneficial task in cytopathology. Microscopy being the gold standard for cell analysis, approaches have been discovered from traditional image processing operations to deep learning techniques for cell segmentation from microscopy images. In the last few years, classification networks were extended for image segmentation using the pixel-based classification method, known as semantic segmentation. Convolutional neural networks exhibited good performance in image segmentation. However, the networks suffered some limitations due to fully connected layers and pooling layers that restricted the size of images to be given as input and resulted in the loss of spatial context. In this research, experiments were carried out with two popular CNN architectures UNet and SegNet, traditionally used for semantic segmentation of natural images. By identifying the capacity of these networks for cell segmentation on natural images, we have experimented on a custom-built RBC, WBC and platelet cell segmentation dataset based on ALL-IDB. We critically evaluate the performance of both architectures with an intuitive explanation of their difference in performance. The UNet outperformed SegNet that too with limited labelled training data giving a promising Dice score of 0.97. With the experiments and analysis conducted in this work, we propose that the UNet is a very good choice for cell segmentation in cytopathology applications.
  • Títulos relacionados: Lecture Notes in Electrical Engineering
  • Editor: Singapore: Springer Singapore Pte. Limited
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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