Image segmentation is one of the main resources in computer vision. Nowadays, this procedure can be made with high precision using Deep Learning, and this fact is important to applications of several research areas including medical image analysis. Image segmentation is currently applied to find tumors, bone defects and other elements that are crucial to achieve accurate diagnoses. The objective of the present work is to perform segmentation of lung computed tomography from a dataset of the website Kaggle (www.kaggle.com) using U-Net, a Deep Convolutional Neural Network with Deep Learning, used in biomedical image segmentation. The dataset contains 267 volumes, which are composed of the 2D images and the masks that corresponds to the manual segmented lungs, the reference image called ground truth. In the present work, the dataset was subdivided in such a way that 80% of the volumes were dedicated for training and 20% were used for testing. The results were evaluated using the Dice Similarity Coefficient as metric and the value 0.83 was the mean obtained segmenting images of test data.
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