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Semi-automatic segmentation of thoracic structures in computed tomography scans

Grant number: 21/05704-2
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): July 01, 2021
Effective date (End): June 30, 2022
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal researcher:Alexandre Xavier Falcão
Grantee:Ilan Francisco da Silva
Home Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?, AP.TEM

Abstract

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.

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