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Automatic segmentation of leukocytes images using deep learning

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Abstract

In this paper, we address the problem of detecting and segmenting leukocytes in images. These cells are an important part of the immune system and some of their attributes (such as the amount and aspect of the cells) are used to detect several diseases (e.g., leukemia). To accomplish this task, we used the deep learning architecture named U-Net, a commonly used segmentation network originally developed aiming at biomedical image segmentation. Since the background in leukocytes images is more constant than in other segmentation tasks, i.e., there is little variety of undesired objects, we opted to use a personalized version of the network and we evaluated the impact of using different combinations of convolutional blocks and filters to build the network model. We compared our network model with different approaches found in the literature using an image dataset containing images of leukocytes and other blood structures. Results demonstrated the superiority of our approach in terms of Jaccard index and, for this given problem, the number of blocks is more important than the total number of trainable parameters of the U-Net.

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Funding

André R. Backes gratefully acknowledges the financial support of CNPq (Grant #307100/2021-9). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001.

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  1. Department of Computing, Federal University of São Carlos, 13565-905, São Carlos, SP, Brazil

    André Ricardo Backes

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  1. André Ricardo Backes
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ARB performed the experiments, wrote the main manuscript text and prepared the figures.

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Correspondence to André Ricardo Backes.

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Backes, A.R. Automatic segmentation of leukocytes images using deep learning. SIViP (2024). https://doi.org/10.1007/s11760-024-03069-5

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