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

Abstract

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.

Inclusive Digital Health

“…Challenges are further addressed in the survey paper where Koehle et al stress that "it may be easy to look at the current health equity landscape as irreparable, having been built on hundreds of years of oppression, marginalization, and discrimination. In this work, we have emphasized collaboration with user and patient groups to define priorities, ensure accessibility and localization, and consider risks in development and utilization of digital health tools" [6].…”

“…The survey paper of the Signals, Sensors, and Imaging Informatics (SSII) section presents a systematic review of 223 articles related to automated computational segmentation of the lung and its lobes [33]. The authors classified the different works according to the types of methods developed, namely those based on: (i) thresholding, (ii) region, (iii) shape or pattern, (iv) traditional machine learning, (v) deep learning, and finally those (iv) guided by neighborhood anatomy.…”

Deep learning-based bronchial tree-guided semi-automatic segmentation of pulmonary segments in computed tomography images

“…The rapid advancement of deep learning has led to its increasing prevalence in the segmentation of the lung, lobes, airways, and vessels within the lungs ( 19 - 21 ). These advancements hold promise in assisting with the segmentation of pulmonary segments.…”

GPU-accelerated lung CT segmentation based on level sets and texture analysis

“…The current state-of-the-art in lung segmentation is dominated by DL methods, but more traditional region- and shape-based methods are still being proposed 27 . In the case of DL, the quality and availability of annotated training data are often as important as the employed algorithmic methodology 28 .…”