Pulmonary-Nodules-Segmentation | 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
DOI: 10.7937/K9/TCIA.2014.V7CVH1JO | Data Citation Required | Analysis Result
| Location | Subjects | Updated | ||
|---|---|---|---|---|
| Lung | Lung | 102 | 2015/02/24 |
Summary
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.
The FA segmentation engine has 2 free parameters, and the SA system has 3. These parameters are adaptively determined for each nodule in a search process guided by a regression neural network (RNN). The RNN uses a number of features computed for each candidate segmentation. We train and test our systems using the new Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) data. To the best of our knowledge, this is one of the first nodule-specific performance benchmarks using the new LIDC-IDRI dataset. We also compare the performance of the proposed methods with several previously reported results on the same data used by those other methods. Our results suggest that the proposed FA system improves upon the state-of-the-art, and the SA system offers a considerable boost over the FA system.
The download links provided below provide easy access to specific subsets of images from our study, which are described in much greater detail in our publication (https://doi.org/10.1016/j.media.2015.02.002).
Collections Used In This Analysis Result
| Title | Data Type | Format | Access Points | Subjects | License | |||
|---|---|---|---|---|---|---|---|---|
| Corresponding Original CT Images from LIDC-IDRI c ontaining the 66 testing nodules that are delineated by all four board certified radiologists | CT | DICOM | Requires NBIA Data Retriever |
61 | 61 | 61 | 13,573 | CC BY 3.0 |
| Corresponding Original CT Images from LIDC-IDRI containing the 77 LIDC testing nodules that are segmented by three, more radiologists | CT | DICOM | Requires NBIA Data Retriever |
41 | 44 | 44 | 8,002 | CC BY 3.0 |
Citations & Data Usage Policy
Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:
Data 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
|
Publications Using This Data
TCIA maintains a list of publications that leverage our data. If you have a manuscript you’d like to add please contact 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
Publication 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 |
TCIA 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. (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 |