Lung field segmentation in chest radiographs: a historical review, current status, and expectations from deep learning. Issue 11 (1st November 2017)

• Record Type: Journal Article
• Authors: Mittal, Ajay; Hooda, Rahul; Sofat, Sanjeev
• Is Part Of: IET image processing. Volume 11:Issue 11(2017)
• Page Start: 937
• Page End: 952
• Publication Date: 2017-11-01
• Subjects: lung -- image segmentation -- medical image processing -- learning (artificial intelligence) -- diagnostic radiography; lung field segmentation -- deep learning -- region‐of‐interest -- radiologic signs -- chest radiographic computer‐aided diagnostic system; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0526
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Lung field defines a region‐of‐interest in which specific radiologic signs such as septal lines, pulmonary opacities, cavities, consolidations, and lung nodules are searched by a chest radiographic computer‐aided diagnostic system. Thus, its precise segmentation is extremely important. To precisely segment it, numerous methods have been developed during the last four decades. However, no exclusive survey consolidating the advancements in these methods has been presented till date, thus indicating a void and the need. This study fills the void by presenting a comprehensive survey of these methods with a focus on their underlying principle, the dataset used, reported performance, and relative merits and demerits. It refrains from doing a hard comparative evaluation by bringing all of them on a common platform, since the datasets used in their development and testing are of varied quality, complexity, and are not publicly available. It also provides a glimpse of deep learning, the present state of deep‐learning‐based lung field segmentation methods, expectations from it, and the challenges ahead of it.