Multitasking segmentation of lung and COVID-19 findings in CT scans using modified EfficientDet, UNet and MobileNetV3 models

The Coronavirus Disease 2019 (COVID-19) pandemic that affects the world since 2020 generated a great amount of research interest in how to provide aid to medical staff on triage, diagnosis, and prognosis. This work proposes an automated segmentation model over Computed Tomography (CT) scans, segmenting the lung and COVID-19 related lung findings at the same time. Manual segmentation is a time-consuming and complex task, especially when applied to high-resolution CT scans, resulting in a lack of gold standards annotation. Thanks to data provided by the RadVid19 Brazilian initiative, providing over a hundred annotated High Resolution CT (HRCT), we analyze the performance of three convolutional neural networks for the segmentation of lung and COVID findings: a 3D UNet architecture; a modified EfficientDet (2D) architecture; and 3D and 2D variations of the MobileNetV3 architecture. Our method achieved first place in the RadVid19 challenge, among 13 other competitors' submissions. Additionally, we evaluate the model with the best result on the challenge in four public CT datasets, comparing our results against other related works, and studying the effects of using different annotations in training and testing. Our best method achieved on testing upwards of 0.98 Lung and 0.73 Findings 3D Dice and reached state-of-the-art performance on public data.