An observer comparison study to evaluate a machine learning model to quantify the infected pneumonia on lung CT images

Alireza Abdihamzehkolaei, Seyedehnafiseh Mirniaharikandehei, Angel Choquehuanca, Marco Aedo, Wilmer Pacheco, Laura Estacio, Victor Cahui, Luis Huallpa, Kevin Quiñonez, Valeria Calderón, Ana Maria Gutierrez, Ana Vargas, Dery Gamero, Eveling Castro-Gutierrez, Yuchen Qiu, Bin Zheng, Javier A. Jo

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Quantification of infected lung volume using computed tomography (CT) images can play a critical role in predicting the severity of pulmonary infectious disease. Manual segmentation of infected areas from several CT image slices, however, is not efficient and viable in clinical practice. To assist clinicians in overcoming this challenge, we developed a new method to automatically segment and quantify the percentage of the infected lung volume. First, we used a public dataset of 20 COVID-19 patients, which consists of manually annotated lung and infection masks, to train a new joint deep learning (DL) model for lung and infection segmentation. As for lung segmentation, a Mask-RCNN model was applied to the lung volume with a novel postprocessing technique. Following that, an ensemble model with a customized residual attention UNet model and feature pyramid network (FPN) models was employed for infection segmentation. Next, we assembled another set of 80 CT scans of Covid-19 patients. Two chest radiologists manually evaluated each CT scan and reported the infected lung volume percentage using a customized graphical user interface (GUI). The developed DL-model was also employed to process these CT images. Then, we compared the agreement between the radiologist (manual) and model-based (automated) percentages of diseased regions. Additionally, the GUI was used to let radiologists rate acceptance of the DL-model generated segmentation results. Analyzing the results demonstrate that the agreement between manual and automated segmentation is >95% in 28 testing cases. Furthermore, >53% of testing cases received the top assessment rating scores from two radiologists (between four-five- score). Thus, this study illustrates the feasibility of developing a DL-model based automated tool to effectively provide quantitative evaluation of infected lung regions to assist in improving the efficiency of radiologists in infection diagnosis.

Idioma originalInglés
Título de la publicación alojadaMedical Imaging 2023
Subtítulo de la publicación alojadaImage Perception, Observer Performance, and Technology Assessment
EditoresClaudia R. Mello-Thoms, Yan Chen
ISBN (versión digital)9781510660397
EstadoPublicada - 2023
EventoMedical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment - San Diego, Estados Unidos
Duración: 21 feb. 202323 feb. 2023

Serie de la publicación

NombreProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (versión impresa)1605-7422


ConferenciaMedical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment
País/TerritorioEstados Unidos
CiudadSan Diego

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