A computational method to aid the detection and annotation of pleural lesions in CT images of the thorax

Azael de Melo e Sousa; Alexandre Xavier Falcão; Ericson Bagatin; Gustavo Souza Meirelles

doi:10.1117/12.2513049

15 March 2019 A computational method to aid the detection and annotation of pleural lesions in CT images of the thorax

Azael de Melo e Sousa, Alexandre Xavier Falcão, Ericson Bagatin, Gustavo Souza Meirelles

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 109490Z (2019) https://doi.org/10.1117/12.2513049
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Several thoracic diseases can affect the pleural space. Pleural-based lesions usually require a careful and timeconsuming visual inspection of the computed tomography (CT) slices to be detected. In order to facilitate this task, we propose a computational method that automatically detects pleural-based lesion candidates in the lung’s surface. The first step of this method is the segmentation of both lungs. For that purpose, any segmentation method can be applied but in this work we used ALTIS, a fast sequence of image processing operators that automatically segments each lung (i.e., air volume) and the trachea. The proposed approach helps the specialist during the annotation process, allowing the creation of properly annotated datasets, and the development of machine learning methods for computer-aided diagnosis. The evaluation of the proposed method was performed in a set of 40 CT scans of patients with pleural plaques and tumor (lung nodules). Two thoracic radiologists and one pulmonologist assessed the images and provided clinical data. Experiments indicate that the proposed method managed to detect most anomalies in a matter of seconds.

Conference Presentation

Citation Download Citation

Azael de Melo e Sousa, Alexandre Xavier Falcão, Ericson Bagatin, and Gustavo Souza Meirelles "A computational method to aid the detection and annotation of pleural lesions in CT images of the thorax", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109490Z (15 March 2019); https://doi.org/10.1117/12.2513049

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