CC BY-NC-ND 4.0 · Yearb Med Inform 2022; 31(01): 277-295
DOI: 10.1055/s-0042-1742517
Section 12: Sensor, Signal and Imaging Informatics

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

Diedre Carmo*
1   School of Electrical and Computer Engineering, University of Campinas, Brazil
Jean Ribeiro*
1   School of Electrical and Computer Engineering, University of Campinas, Brazil
Sergio Dertkigil
2   School of Medical Sciences, University of Campinas, Brazil
Simone Appenzeller
2   School of Medical Sciences, University of Campinas, Brazil
Roberto Lotufo
1   School of Electrical and Computer Engineering, University of Campinas, Brazil
Leticia Rittner
1   School of Electrical and Computer Engineering, University of Campinas, Brazil
› Author Affiliations


Objectives: Automated computational segmentation of the lung and its lobes and findings in X-Ray based computed tomography (CT) images is a challenging problem with important applications, including medical research, surgical planning, and diagnostic decision support. With the increase in large imaging cohorts and the need for fast and robust evaluation of normal and abnormal lungs and their lobes, several authors have proposed automated methods for lung assessment on CT images. In this paper we intend to provide a comprehensive summarization of these methods.

Methods: We used a systematic approach to perform an extensive review of automated lung segmentation methods. We chose Scopus, PubMed, and Scopus to conduct our review and included methods that perform segmentation of the lung parenchyma, lobes or internal disease related findings. The review was not limited by date, but rather by only including methods providing quantitative evaluation.

Results: We organized and classified all 234 included articles into various categories according to methodological similarities among them. We provide summarizations of quantitative evaluations, public datasets, evaluation metrics, and overall statistics indicating recent research directions of the field.

Conclusions: We noted the rise of data-driven models in the last decade, especially due to the deep learning trend, increasing the demand for high-quality data annotation. This has instigated an increase of semi-supervised and uncertainty guided works that try to be less dependent on human annotation. In addition, the question of how to evaluate the robustness of data-driven methods remains open, given that evaluations derived from specific datasets are not general.

* Equal contribution

Supplementary Material

Publication History

Article published online:
04 December 2022

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