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  • Segmentation of Pulmonary Nodules in Computed Tomography Using a Regression Neural Network Approach and its Application to the Lung Image Database Consortium and Image Database Resource Initiative Dataset (Pulmonary-Nodules-Segmentation)

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Description

We present new pulmonary nodule segmentation algorithms for computed tomography (CT). These include a fully--automated (FA) system, a semi-automated (SA) system, and a hybrid system. Like most traditional systems, the new FA system requires only a single user-supplied cue point. On the other hand, the SA system represents a new algorithm class requiring 8 user-supplied control points. This does increase the burden on the user, but we show that the resulting system is highly robust and can handle a variety of challenging cases. The proposed hybrid system starts with the FA system. If improved segmentation results are needed, the SA system is then deployed.

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titleData Access

Data Access

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Collections Used in this Third Party Analysis

Below is a list of the Collections used in these analyses:

Images nodules 
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Corresponding Original CT Images from LIDC-IDRI cImages containing the 66 testing nodules that are delineated by all four board certified radiologists (DICOM) 

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Corresponding Original CT Images from LIDC-IDRI containing the 77 LIDC testing nodules that are segmented by three or more radiologists (DICOM)

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titleCitations & Data Usage Policy

Citations & Data Usage Policy 

These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:

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Info
titleData Citation
Messay T, Hardie RC,  Tuinstra TR. (2014). Segmentation of Pulmonary Nodules in Computed Tomography Using a Regression Neural Network Approach and its Application to the Lung Image Database Consortium and Image Database Resource Initiative Dataset (Pulmonary-Nodules-Segmentation). The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2014.V7CVH1JO



Info
titlePublication Citation

Messay T, Hardie RC,  Tuinstra TR. (2015). Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset. Medical Image Analysis. Elsevier BV. https://doi.org/10.1016/j.media.2015.02.002



Info
titleTCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F.  The (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, . In Journal of Digital Imaging , Volume (Vol. 26, Number Issue 6, December, 2013, pp 1045-1057. (paper)

In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:

Info
titlePublication Citation

Messay T, Hardie RC,  Tuinstra TR. (2015). Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset. Medical Image Analysis. Elsevier BV. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1016/j.media.2015.02.0021007/s10278-013-9622-7 PMCID: PMC3824915

Other Publications Using This Data

TCIA maintainsmaintains a list of publications that leverage TCIA our data. If you have a manuscript you'd like to add pleaseplease contact the TCIA's Helpdesk.

  • Gomes, J. H. O. (2017). Pulmonary nodule segmentation in computed tomography with deep learning. (M.S. Thesis). Instituto Universitário de Lisboa, Retrieved from http://hdl.handle.net/10071/15479


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titleVersions

Version 1 (Current): 2015/02/24


Data TypeDownload all or Query/Filter

Images containing the 66 testing nodules that are delineated by all four board certified radiologists (DICOM) 

Images containing the 77 LIDC testing nodules that are segmented by three or more radiologists (DICOM)



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