LNDetector: A Flexible Gaze Characterisation Collaborative Platform for Pulmonary Nodule Screening
Lung cancer is the deadliest type of cancer worldwide and late detection is one of the major factors for the low survival rate of patients. Low dose computed tomography has been suggested as a potential early screening tool but manual screening is costly, time-consuming and prone to interobserver variability. This has fueled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules but its application to the clinical routine is challenging. In this study, a platform for the development, deployment and testing of pulmonary nodule computer-aided strategies is presented: LNDetector. LNDetector integrates image exploration and nodule annotation tools as well as advanced nodule detection, segmentation and classification methods and gaze characterisation. Different processing modules can easily be implemented or replaced to test their efficiency in clinical environments and the use of gaze analysis allows for the development of collaborative strategies. The potential use of this platform is shown through a combination of visual search, gaze characterisation and automatic nodule detection tools for an efficient and collaborative computer-aided strategy for pulmonary nodule screening.
KeywordsLung cancer Low dose computed tomography Pulmonary nodules Computer-aided diagnosis
This work was financed by the European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness - COMPETE 2020 Programme and by National Funds through the Portuguese Funding agency, FCT - Fundação para a Ciência e Tecnologia within project: PTDC/EEI-SII/6599 /2014 (POCI-01-0145-FEDER-016673).
Conflict of Interest
The authors declare that there is no conflict of interest.
- 1.Tobii Gaming. https://gaming.tobii.com/
- 2.Aresta, G., Araújo, T., Jacobs, C., van Ginneken, B., Cunha, A., Ramos, I., Campilho, A.: Towards an automatic lung cancer screening system in low dose computed tomography. In: Image Analysis for Moving Organ, Breast, and Thoracic Images, pp. 310–318. Springer (2018)Google Scholar
- 3.Armato, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011)CrossRefGoogle Scholar
- 8.Kundel, H.L.: Reader error, object recognition, and visual search. In: Medical Imaging 2004: Image Perception, Observer Performance, and Technology Assessment, vol. 5372, pp. 1–12. International Society for Optics and Photonics (2004)Google Scholar
- 9.Machado, M., Aresta, G., Leitão, P., Carvalho, A.S., Rodrigues, M., Ramos, I., Cunha, A., Campilho, A.: Radiologists’ gaze characterization during lung nodule search in thoracic CT. In: 2018 International Conference on Graphics and Interaction (ICGI), pp. 1–7. IEEE (2018)Google Scholar
- 10.Millodot, M.: Dictionary of Optometry and Visual Science E-Book. Elsevier Health Sciences, London (2014)Google Scholar
- 11.Redmon, J., Farhadi, A.: YOLOV3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
- 12.Setio, A.A.A., Traverso, A., De Bel, T., Berens, M.S., van den Bogaard, C., Cerello, P., Chen, H., Dou, Q., Fantacci, M.E., Geurts, B., et al.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med. Image Anal. 42, 1–13 (2017)CrossRefGoogle Scholar
- 14.The National Lung Screening Trial Research Team: Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 365(5), 395–409 (2011)Google Scholar
- 16.Wu, B., Zhou, Z., Wang, J., Wang, Y.: Joint learning for pulmonary nodule segmentation, attributes and malignancy prediction. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1109–1113. IEEE (2018)Google Scholar