Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT

Gao, Riqiang; Khan, Mirza S.; Tang, Yucheng; Xu, Kaiwen; Deppen, Steve; Huo, Yuankai; Sandler, Kim L.; Massion, Pierre P.; Landman, Bennett A.

doi:10.48550/arxiv.2107.12842

Cited by 1 publication

(1 citation statement)

References 32 publications

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“…Across 887 subjects in the program, we collected 1490 chest CT scans (Table 1) that passed basic quality control, ensuring no artifact occluding the lung fields, proper field of view, slice contiguity, and realistic physical dimensions. 29 TotalSegmentator (TotalSeg) 30 is a publicly available dataset of clinically collected CTs sampled from the University Hospital Basel, Switzerland, containing images of various protocols, slice thicknesses, resolutions, and reconstruction kernels. One hundred and four anatomical structures, including the pulmonary lobes, were annotated with the supervision of a boardcertified radiologist.…”

Section: Datasetsmentioning

confidence: 99%

Quantifying emphysema in lung screening computed tomography with robust automated lobe segmentation

Lee

et al. 2023

J. Med. Imag.

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Anatomy-based quantification of emphysema in a lung screening cohort has the potential to improve lung cancer risk stratification and risk communication. Segmenting lung lobes is an essential step in this analysis, but leading lobe segmentation algorithms have not been validated for lung screening computed tomography (CT).Approach: In this work, we develop an automated approach to lobar emphysema quantification and study its association with lung cancer incidence. We combine selfsupervised training with level set regularization and finetuning with radiologist annotations on three datasets to develop a lobe segmentation algorithm that is robust for lung screening CT. Using this algorithm, we extract quantitative CT measures for a cohort (n ¼ 1189) from the National Lung Screening Trial and analyze the multivariate association with lung cancer incidence.Results: Our lobe segmentation approach achieved an external validation Dice of 0.93, significantly outperforming a leading algorithm at 0.90 (p < 0.01). The percentage of low attenuation volume in the right upper lobe was associated with increased lung cancer incidence (odds ratio: 1.97; 95% CI: [1.06, 3.66]) independent of PLCO m2012 risk factors and diagnosis of whole lung emphysema. Quantitative lobar emphysema improved the goodness-of-fit to lung cancer incidence (χ 2 ¼ 7.48, p ¼ 0.02). Conclusions:We are the first to develop and validate an automated lobe segmentation algorithm that is robust to smoking-related pathology. We discover a quantitative risk factor, lending further evidence that regional emphysema is independently associated with increased lung cancer incidence. The algorithm is provided at https://github.com/MASILab/EmphysemaSeg.

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Section: Datasetsmentioning

confidence: 99%

Quantifying emphysema in lung screening computed tomography with robust automated lobe segmentation

Lee

et al. 2023

J. Med. Imag.

View full text Add to dashboard Cite

show abstract

Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT

Cited by 1 publication

References 32 publications

Quantifying emphysema in lung screening computed tomography with robust automated lobe segmentation

Quantifying emphysema in lung screening computed tomography with robust automated lobe segmentation

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