Development of ground truth data for automatic lumbar spine MRI image segmentation

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...

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Main Authors:	Natalia, Friska, Meidia, Hira, Afriliana, Nunik, Al-Kafri, Ala S., Sudirman, Sud, Simpson, Andrew, Sophian, Ali, Al-Jumaily, Mohammed, Al-Rashdan, Wasfi, Bashtawi, Mohammad
Format:	Conference or Workshop Item
Language:	English English English
Published:	Institute of Electrical and Electronics Engineers Inc. 2019
Subjects:	T Technology (General) TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TL Motor vehicles. Aeronautics. Astronautics
Online Access:	http://irep.iium.edu.my/72709/ http://irep.iium.edu.my/72709/ http://irep.iium.edu.my/72709/ http://irep.iium.edu.my/72709/1/72709_Development%20of%20Ground%20Truth%20Data_complete.pdf http://irep.iium.edu.my/72709/2/72709_Development%20of%20Ground%20Truth%20Data_scopus.pdf http://irep.iium.edu.my/72709/3/72709_Development%20of%20Ground%20Truth%20Data_article%20from%20website_wos.pdf

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recordtype	eprints
spelling	iium-727092019-06-18T09:05:11Z http://irep.iium.edu.my/72709/ Development of ground truth data for automatic lumbar spine MRI image segmentation Natalia, Friska Meidia, Hira Afriliana, Nunik Al-Kafri, Ala S. Sudirman, Sud Simpson, Andrew Sophian, Ali Al-Jumaily, Mohammed Al-Rashdan, Wasfi Bashtawi, Mohammad T Technology (General) TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TL Motor vehicles. Aeronautics. Astronautics 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. Institute of Electrical and Electronics Engineers Inc. 2019-01-22 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/72709/1/72709_Development%20of%20Ground%20Truth%20Data_complete.pdf application/pdf en http://irep.iium.edu.my/72709/2/72709_Development%20of%20Ground%20Truth%20Data_scopus.pdf application/pdf en http://irep.iium.edu.my/72709/3/72709_Development%20of%20Ground%20Truth%20Data_article%20from%20website_wos.pdf Natalia, Friska and Meidia, Hira and Afriliana, Nunik and Al-Kafri, Ala S. and Sudirman, Sud and Simpson, Andrew and Sophian, Ali and Al-Jumaily, Mohammed and Al-Rashdan, Wasfi and Bashtawi, Mohammad (2019) Development of ground truth data for automatic lumbar spine MRI image segmentation. In: IEEE 20th International Conference on High Performance Computing and Communications, 16th IEEE International Conference on Smart City and 4th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018, 28-30 June 2018, Exeter; United Kingdom. https://ieeexplore.ieee.org/document/8622977 10.1109/HPCC/SmartCity/DSS.2018.00239
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English
topic	T Technology (General) TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TL Motor vehicles. Aeronautics. Astronautics
spellingShingle	T Technology (General) TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TL Motor vehicles. Aeronautics. Astronautics Natalia, Friska Meidia, Hira Afriliana, Nunik Al-Kafri, Ala S. Sudirman, Sud Simpson, Andrew Sophian, Ali Al-Jumaily, Mohammed Al-Rashdan, Wasfi Bashtawi, Mohammad Development of ground truth data for automatic lumbar spine MRI image segmentation
description	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.
format	Conference or Workshop Item
author	Natalia, Friska Meidia, Hira Afriliana, Nunik Al-Kafri, Ala S. Sudirman, Sud Simpson, Andrew Sophian, Ali Al-Jumaily, Mohammed Al-Rashdan, Wasfi Bashtawi, Mohammad
author_facet	Natalia, Friska Meidia, Hira Afriliana, Nunik Al-Kafri, Ala S. Sudirman, Sud Simpson, Andrew Sophian, Ali Al-Jumaily, Mohammed Al-Rashdan, Wasfi Bashtawi, Mohammad
author_sort	Natalia, Friska
title	Development of ground truth data for automatic lumbar spine MRI image segmentation
title_short	Development of ground truth data for automatic lumbar spine MRI image segmentation
title_full	Development of ground truth data for automatic lumbar spine MRI image segmentation
title_fullStr	Development of ground truth data for automatic lumbar spine MRI image segmentation
title_full_unstemmed	Development of ground truth data for automatic lumbar spine MRI image segmentation
title_sort	development of ground truth data for automatic lumbar spine mri image segmentation
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2019
url	http://irep.iium.edu.my/72709/ http://irep.iium.edu.my/72709/ http://irep.iium.edu.my/72709/ http://irep.iium.edu.my/72709/1/72709_Development%20of%20Ground%20Truth%20Data_complete.pdf http://irep.iium.edu.my/72709/2/72709_Development%20of%20Ground%20Truth%20Data_scopus.pdf http://irep.iium.edu.my/72709/3/72709_Development%20of%20Ground%20Truth%20Data_article%20from%20website_wos.pdf
first_indexed	2023-09-18T21:43:03Z
last_indexed	2023-09-18T21:43:03Z
_version_	1777413267849740288

Development of ground truth data for automatic lumbar spine MRI image segmentation

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