NDLI: Optimizing the Use of Radiologist Seed Points for Improved Multiple Sclerosis Lesion Segmentation

Content Provider	IEEE Xplore Digital Library
Author	McAusland, J. Tam, R.C. Wong, E. Riddehough, A. Li, D.K.B.
Copyright Year	1964
Abstract	Many current methods for multiple sclerosis (MS) lesion segmentation require radiologist seed points as input, but do not necessarily allow the expert to work in an intuitive or efficient way. Ironically, most methods also assume that the points are placed optimally. This paper examines how seed points can be processed with intuitive heuristics, which provide improved segmentation accuracy while facilitating quick and natural point placement. Using a large set of MRIs from an MS clinical trial, two radiologists are asked to seed the lesions while unaware that the points would be fed into a classifier, based on Parzen windows, that automatically delineates each marked lesion. To evaluate the impact of the new heuristics, an interactive region-growing method is used to provide ground truth and the Dice coefficient (DC) and Spearman's rank correlation are used as the primary measures of agreement. A stratified analysis is performed to determine the effect on scans with low-, medium-, and high lesion loads. Compared to the unenhanced classifier, the heuristics dramatically improve the DC (+32.91 pt.) and correlation (+0.50) for the scans with low lesion loads, and also improve the DC (+14.55 pt.) and correlation (+0.15) for the scans with medium lesion loads, while having a minimal effect for the scans with high lesion loads, which are already segmented accurately by Parzen windows. With the heuristics, the DC is close to 80% and the correlation is above 0.9 for all three load categories.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	10
File Size	420047
Starting Page	2689
Ending Page	2698
File Format	PDF
ISSN	00189294
Volume Number	57
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multiple sclerosis Lesions Magnetic resonance imaging Image segmentation Pathology Permission Clinical trials Performance analysis Visualization Aging white matter lesions (WMLs) Magnetic resonance imaging (MRI) multiple sclerosis (MS) seed points segmentation
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Optimizing the use of radiologist seed points for improved multiple sclerosis lesion segmentation.

Review of advanced computational approaches on multiple sclerosis segmentation and classification

A unified approach for lesion segmentation on MRI of multiple sclerosis

Automatic Segmentation and Classification of Multiple Sclerosis in Multichannel MRI

Automatic contrast enhancement of white matter lesions in FLAIR MRI

Hierarchical segmentation of multiple sclerosis lesions in multi-sequence MRI

Automated segmentation of multiple sclerosis lesions using statistical approach

Brief review of multiple sclerosis lesions segmentation methods on conventional magnetic resonance imaging

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Optimizing the Use of Radiologist Seed Points for Improved Multiple Sclerosis Lesion Segmentation

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