Development of Ground Truth Data for Automatic Lumbar Spine MRI Image Segmentation

Friska Natalia, Hira Meidia, Nunik Afriliana, Ala S. Al-Kafri, Sud Sudirman, Andrew Simpson, Ali Sophian, Mohammed Al-Jumaily, Wasfi Al-Rashdan, Mohammad Bashtawi

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Artificial Intelligence through supervised machine learning remains an attractive and popular research area in medical image processing. The objective of such research is often tied to the development of an intelligent computer aided diagnostic system whose aim is to assist physicians in their task of diagnosing diseases. The quality of the resulting system depends largely on the availability of good data for the machine learning algorithm to train on. Training data of a supervised learning process needs to include ground truth, i.e., data that have been correctly annotated by experts. Due to the complex nature of most medical images, human error, experience, and perception play a strong role in the quality of the ground truth. In this paper, we present the results of annotating lumbar spine Magnetic Resonance Imaging images for automatic image segmentation and propose confidence and consistency metrics to measure the quality and variability of the resulting ground truth data, respectively.
    Original languageEnglish
    Title of host publicationProceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1449-1454
    Number of pages6
    ISBN (Print)9781538666142
    DOIs
    Publication statusPublished - 22 Jan 2019
    Event20th International Conference on High Performance Computing andCommunications, 16th International Conference on Smart City and 4th International Conferenceon Data Science and Systems - Exeter, United Kingdom
    Duration: 28 Jun 201830 Jun 2018

    Conference

    Conference20th International Conference on High Performance Computing andCommunications, 16th International Conference on Smart City and 4th International Conferenceon Data Science and Systems
    Abbreviated titleHPCC/SmartCity/DSS 2018
    CountryUnited Kingdom
    CityExeter
    Period28/06/1830/06/18

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  • Cite this

    Natalia, F., Meidia, H., Afriliana, N., Al-Kafri, A. S., Sudirman, S., Simpson, A., Sophian, A., Al-Jumaily, M., Al-Rashdan, W., & Bashtawi, M. (2019). Development of Ground Truth Data for Automatic Lumbar Spine MRI Image Segmentation. In Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018 (pp. 1449-1454). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00239