Revisión crítica de los Métodos de Supresión Ósea en Imágenes de Rayos X de Tórax

Ernesto Santiesteban Torres; Eduardo  Garea Llano

Authors

Ernesto Santiesteban Torres Universidad Tecnológica de la Habana "José Antonio Echeverría"
Eduardo Garea Llano Centro de Neurociencias de Cuba

Keywords:

deep learning; CXR images; machine learning; bone suppression

Abstract

The examination of chest X-ray images is a method of evaluating the degree of effectiveness of the protocols applied to COVID-19 patients in serious or critical condition. An element that significantly influences the effectiveness of these examinations is the presence in the images of the bones that interfere with the correct detection and evaluation of the lesions caused by the disease. The aim of the work is the critical study of the Bone Suppression Methods (MSO) that have been proposed as a step for the pre-processing of chest radiography images. The methodology used was based on the search, selection, review and analysis of the most current works published on the subject. The most representative works were analyzed. An analysis of the role of bone suppression was performed to improve diagnostic efficiency. A taxonomic classification of the studied methods was presented. A possible solution proposal was made on the unsupervised learning approach, in order to improve the diagnostic performance of both radiologists and automated systems.

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