Embolia pulmonar é uma das principais causas de morte relacionadas a doenças cardiovasculares no mundo, uma vez que é feito o diagnostico é necessária uma resposta rápida pela equipe médica para salvar o paciente. A principal forma de diagnóstico é pelo exame de tomografia computadorizada e, devido a grande quantidade de dados que o exame gera, algoritmos de deep learning têm mostrado bons resultados em encontrar embolia pulmonar de maneira autônoma. O objetivo deste trabalho é desenvolver uma rede de deep learning capaz de encontrar embolia pulmonar em exames de tomografia computadorizada. Até então, utilizando uma rede inspirada na U-net, o método segmentou trombos atingindo um Dice Score de 0.81 e um IoU de 0.79.
Pulmonary embolism has a high incidence and mortality, especially if undiagnosed. The examination of choice for diagnosing the disease is computed tomography pulmonary angiography. As many factors can lead to misinterpretations and diagnostic errors, different groups are utilizing deep learning methods to help improve this process. The diagnostic accuracy of these methods tends to increase by augmenting the training dataset. Deep learning methods can potentially benefit from the use of images acquired with devices from different vendors. To the best of our knowledge, we have developed the first public dataset annotated at the pixel and image levels and the first pixel-level annotated dataset to contain examinations performed with equipment from Toshiba and GE. This dataset includes 40 examinations, half performed with each piece of equipment, representing samples from two medical services. We also included measurements related to the cardiac and circulatory consequences of pulmonary embolism. We encourage the use of this dataset to develop, evaluate and compare the performance of new AI algorithms designed to diagnose PE.
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