The ALTIS method, developed by the research group, provides a fast and accurate automatic segmentation tool of the lungs and trachea in a chest computed tomography scan. Moreover, the method is robust, since its methodology was developed considering anomalies that deform the lungs. However, the method fails in severe cases of pulmonary anatomy impairment or in the presence of structures attached to the pleura, such as tumors or consolidations. The present work aims at exploring deep learning of radiomic features from the image to improve the ALTIS method and provide a tool based on graph search algorithms to perform a semi-automatic correction of the segmentation, exploring the features learned by deep learning.
News published in Agência FAPESP Newsletter about the scholarship: