PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines

Kiser, Kendall J.; Ahmed, Sara; Stieb, Sonja; Mohamed, A.S.R.; Hesham, Elhalawani; Park, Peter Y. S.; Doyle, Nathan; Wang, Brandon J.; Barman, Arko; Li, Zhao; Zheng, Weili; Fuller, Clifton D.; Giancardo, Luca

doi:10.1002/mp.14424

Cited by 31 publications

(16 citation statements)

References 56 publications

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“…This dataset consists of left and right thoracic volume segmentations delineated on 402 CT scans from The Cancer Imaging Archive NSCLC Radiomics 29,30 …”

Section: Methodsmentioning

confidence: 99%

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Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Wang

An³

et al. 2021

Medical Physics

214

171

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Purpose Accurate segmentation of lung and infection in COVID‐19 computed tomography (CT) scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID‐19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. Methods To promote the development of data‐efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID‐19 cases, which contain current active research areas, for example, few‐shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. Results Based on the state‐of‐the‐art network, we provide more than 40 pretrained baseline models, which not only serve as out‐of‐the‐box segmentation tools but also save computational time for researchers who are interested in COVID‐19 lung and infection segmentation. We achieve average dice similarity coefficient (DSC) scores of 97.3%, 97.7%, and 67.3% and average normalized surface dice (NSD) scores of 90.6%, 91.4%, and 70.0% for left lung, right lung, and infection, respectively. Conclusions To the best of our knowledge, this work presents the first data‐efficient learning benchmark for medical image segmentation, and the largest number of pretrained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID‐19 CT segmentation with limited data.

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“…This dataset consists of left and right thoracic volume segmentations delineated on 402 CT scans from The Cancer Imaging Archive NSCLC Radiomics 29,30 …”

Section: Methodsmentioning

confidence: 99%

“…The CT scans in this dataset are the same as those in NSCLC left and right lung segmentation dataset, while pleural effusion is delineated for 78 cases 29–31 …”

Section: Methodsmentioning

confidence: 99%

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Wang

An³

et al. 2021

Medical Physics

214

171

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“…We also investigate how anatomic and pathologic variables impact autosegmentation correction time. In the process we have generated a library of 402 expert-vetted left and right thoracic cavity segmentations, as well as 78 pleural effusion segmentations, which we made publicly available through The Cancer Imaging Archive 52 (TCIA) at doi:10.7937/tcia.2020.6c7y-gq39 53 . The CT scans on which the segmentations were delineated are likewise publicly available 54 from TCIA.…”

Section: Introductionmentioning

confidence: 99%

Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better than Traditional Metrics in a Thoracic Cavity Segmentation Workflow

Kiser¹,

Barman

Stieb

et al. 2020

Preprint

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Introduction Automated segmentation templates can save clinicians time compared to de novo segmentation but may still take substantial time to review and correct. It has not been thoroughly investigated which automated segmentation-corrected segmentation similarity metrics best predict clinician correction time. Materials and Methods Bilateral thoracic cavity volumes in 329 CT scans were segmented by a UNet-inspired deep learning segmentation tool and subsequently corrected by a fourth-year medical student. Eight spatial similarity metrics were calculated between the automated and corrected segmentations and associated with correction times using Spearman′s rank correlation coefficients. Nine clinical variables were also associated with metrics and correction times using Spearman′s rank correlation coefficients or Mann-Whitney U tests. Results The added path length, false negative path length, and surface Dice similarity coefficient correlated better with correction time than traditional metrics, including the popular volumetric Dice similarity coefficient (respectively ρ = 0.69, ρ = 0.65, ρ = −0.48 versus ρ = −0.25; correlation p values < 0.001). Clinical variables poorly represented in the autosegmentation tool′s training data were often associated with decreased accuracy but not necessarily with prolonged correction time. Discussion Metrics used to develop and evaluate autosegmentation tools should correlate with clinical time saved. To our knowledge, this is only the second investigation of which metrics correlate with time saved. Validation of our findings is indicated in other anatomic sites and clinical workflows. Conclusion Novel spatial similarity metrics may be preferable to traditional metrics for developing and evaluating autosegmentation tools that are intended to save clinicians time.

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“…Nodules" by Fedorov, et al 12 In "PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines" by Kiser, et al 14 describe a dataset of thoracic cavity segmentations and discrete pleural effusion segmentations annotated on 402 CT scans acquired from patients with non-small cell lung cancer (NSCLC). These data can be used for developing image analysis pipelines such as lung structure segmentation, lesion detection, and radiomics feature extraction.…”

Section: Accepted Articlementioning

confidence: 99%

“…In "DICOM Re-encoding of Volumetrically Annotated Lung Imaging Data Consortium (LIDC) Nodules" by Fedorov et al 12 describe annotations for lung nodules from 875 of the subjects collected by the Lung Imaging Data Consortium and Image Database Resource Initiative (LIDC) converted into standard DICOM objects to simplify reuse of the data with the readily available open-source tools, and to improve adherence to FAIR (Findable, Accessible, Interoperable, Reusable) principles. 13 In "PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines" by Kiser et al 14 describe a dataset of thoracic cavity segmentations and discrete pleural effusion segmentations annotated on 402 CT scans acquired from patients with non-small cell lung cancer (NSCLC). These data can be used for developing image analysis pipelines such as lung structure segmentation, lesion detection, and radiomics feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Introduction to special issue on datasets hosted in The Cancer Imaging Archive (TCIA)

et al. 2020

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PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines

Cited by 31 publications

References 56 publications

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better than Traditional Metrics in a Thoracic Cavity Segmentation Workflow

Introduction to special issue on datasets hosted in The Cancer Imaging Archive (TCIA)

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