Infusion extraction and measurement on CT images based on computer vision and neural network

Abstract

This paper presents a new approach to the automated detection and quantification of pulmonary emphysema and pneumoconiosis using computed tomography images. The proposed method employs computer vision and neural network algorithms to improve the accuracy and speed of lung diagnosis, as well as the monitoring of emphysema and its changes over time. The study analyzes existing approaches and demonstrates the novelty of the proposed method. The paper reports high accuracy of emphysema extraction and size measurements based on three different patient cases, as evaluated by an expert, and the successful segmentation of pneumosclerosis. The proposed method has the potential to significantly improve medical image segmentation, particularly in the detection and diagnosis of diseases such as Chronic Obstructive Pulmonary Disease (COPD) and COVID-19. The study concludes that the proposed method may also be useful in other areas of medical imaging, contributing to the ongoing effort to develop new and improved methods for medical image analysis and interpretation.