Segmentation of Lung Tomographic Images Using U-Net Deep Neural Networks

Eduardo Desteafni Stefanato; Vitor Souza Premoli Pinto de Oliveira; Christiano Jorge Gomes Pinheiro; Regina Cely Rodrigues Barroso; Anderson Alvarenga de Moura Meneses

Eduardo Desteafni Stefanato University of São Paulo https://orcid.org/0000-0002-2318-9884
Vitor Souza Premoli Pinto de Oliveira University of São Paulo https://orcid.org/0000-0001-7688-6854
Christiano Jorge Gomes Pinheiro Federal University of Espírito Santo https://orcid.org/0000-0003-2898-8738
Regina Cely Rodrigues Barroso Rio de Janeiro State University https://orcid.org/0000-0001-6348-694X
Anderson Alvarenga de Moura Meneses Federal University of Western Pará https://orcid.org/0000-0003-1461-2772

Keywords: U-Net, Semantic Segmentation, Deep Neural Networks, Biomedical Images

Abstract

Deep Neural Networks (DNNs) are among the best methods of Artificial Intelligence, especially in computer vision, where convolutional neural networks play an important role. There are numerous architectures of DNNs, but for image processing, U-Net offers great performance in digital processing tasks such as segmentation of organs, tumors, and cells for supporting medical diagnoses. In the present work, an assessment of U-Net models is proposed, for the segmentation of computed tomography of the lung, aiming at comparing networks with different parameters. In this study, the models scored 96% Dice Similarity Coefficient on average, corroborating the high accuracy of the U-Net for segmentation of tomographic images.

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