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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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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url | https://wiki.cancerimagingarchive.net/download/attachments/19038755/LIDC-66-nodules.tcia?api=v2 |
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(Download requires the | | NBIA Data Retriever) | | Corresponding Original CT Images from LIDC-IDRI | Images containing the 77 LIDC testing | nodules nodules that are segmented by three or more radiologists (DICOM) | |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19038755/LIDC-77-nodules.tcia?api=v2 |
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| 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 |
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title | Publication Citation |
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| Messay T, Hardie RC, Tuinstra 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 |
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| Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7 PMCID: PMC3824915 |
Other Publications Using This DataTCIA 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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| Version 1 (Current): 2015/02/24
Data Type | Download all or Query/Filter |
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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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