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

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

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

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

U-NET aplicada a segmentação de ossos em microtomografias computadorizadas obtidas por radiação síncrotron para análises histomorfométricas

Actually, artificial intelligence (AI) participates increasingly in the elaboration of biomedical diagnoses. Clinical applications have used deep learning (DP) methods in the segmentation process, helping in the early treatment of diseases. Based on this principle, this work proposes, via Deep Neural Network (DNN), U-Net, to segment images of rat tibia, the main idea was to use AI architectures added to the image quantification technique, bone histomorphometry. To obtain the images, it was used the non-destructive technique of Computerized Microtomography obtained by X-rays from Synchrotron Radiation (µTC-RS). The initial objective was to enable models to eliminate marrow and other artifacts, leaving only bone; the final objective was to contribute to the state of the art in the use of PA-based methods in contrast to traditional segmentation methods, seeking to apply them to biomedical images. In this study, the developed models resulted in an average of approximately 90% for the Sørensen-Dice coefficient metric, demonstrating a high replicability rate.Atualmente, a inteligência artificial (IA) participa cada vez mais na elaboração de diagnósticos biomédicos. Aplicações clínicas têm utilizado de métodos de aprendizagem profunda (AP) no processo de segmentação, auxiliando no tratamento antecipado de doenças. Partindo desse pressuposto, este trabalho propõe, via Rede Neural Profunda (RNP), U-Net, segmentar imagens de tíbia de rato, tendo como ideia central utilizar arquiteturas de IA somada a técnica de quantificação de imagem, histomorfometria óssea. Para obtenção das imagens foi utilizado a técnica não destrutiva de Microtomografia Computadorizada obtida por raio-x oriundos de Radiação Síncrotron (µTC-RS). O objetivo inicial foi capacitar modelos para eliminar medula e outros artefatos, permanecendo somente osso; tendo como objetivo final buscar contribuir com o estado da arte no que dita o uso de métodos baseados em AP em contrapartida com métodos tradicionais de segmentação, na busca de aplicá-las em imagens biomédicas. Nesse estudo, os modelos desenvolvidos resultaram em uma média aproximada de 90% para a métrica do coeficiente do Sørensen-Dice, demonstrando uma alta taxa de replicabilidade