Development of Ground Truth Data for Automatic Lumbar Spine MRI Image Segmentation
| Autor: | Ala S. Al-Kafri, Mohammed Al-Jumaily, Nunik Afriliana, Hira Meidia, Mohammad Bashtawi, Friska Natalia, Andrew Simpson, Sud Sudirman, Ali Sophian, Wasfi Al-Rashdan |
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| Rok vydání: | 2018 |
| Předmět: |
QA75
Computer science media_common.quotation_subject Image processing 02 engineering and technology Machine learning computer.software_genre 030218 nuclear medicine & medical imaging 03 medical and health sciences Consistency (database systems) 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering medicine Quality (business) media_common Ground truth medicine.diagnostic_test business.industry Supervised learning Magnetic resonance imaging Image segmentation R1 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | HPCC/SmartCity/DSS 2018 IEEE 20th International Conference on High Performance Computing and Communications |
| DOI: | 10.1109/hpcc/smartcity/dss.2018.00239 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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