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Proceedings Paper

A concurrent computer aided detection (CAD) tool for articular cartilage disease of the knee on MR imaging using active shape models
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Paper Abstract

Osteoarthritis (OA) is the most common form of arthritis and a major cause of morbidity affecting millions of adults in the US and world wide. In the knee, OA begins with the degeneration of joint articular cartilage, eventually resulting in the femur and tibia coming in contact, and leading to severe pain and stiffness. There has been extensive research examining 3D MR imaging sequences and automatic/semi-automatic techniques for 2D/3D articular cartilage extraction. However, in routine clinical practice the most popular technique still remain radiographic examination and qualitative assessment of the joint space. This may be in large part because of a lack of tools that can provide clinically relevant diagnosis in adjunct (in near real time fashion) with the radiologist and which can serve the needs of the radiologists and reduce inter-observer variation. Our work aims to fill this void by developing a CAD application that can generate clinically relevant diagnosis of the articular cartilage damage in near real time fashion. The algorithm features a 2D Active Shape Model (ASM) for modeling the bone-cartilage interface on all the slices of a Double Echo Steady State (DESS) MR sequence, followed by measurement of the cartilage thickness from the surface of the bone, and finally by the identification of regions of abnormal thinness and focal/degenerative lesions. A preliminary evaluation of CAD tool was carried out on 10 cases taken from the Osteoarthritis Initiative (OAI) database. When compared with 2 board-certified musculoskeletal radiologists, the automatic CAD application was able to get segmentation/thickness maps in little over 60 seconds for all of the cases. This observation poses interesting possibilities for increasing radiologist productivity and confidence, improving patient outcomes, and applying more sophisticated CAD algorithms to routine orthopedic imaging tasks.

Paper Details

Date Published: 17 March 2008
PDF: 9 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69153G (17 March 2008); doi: 10.1117/12.773157
Show Author Affiliations
Bharath Ramakrishna, Univ. of Maryland, Baltimore County (United States)
Ganesh Saiprasad, Univ. of Maryland, Baltimore County (United States)
Nabile Safdar, Univ. of Maryland School of Medicine (United States)
Khan Siddiqui, VA Maryland Health Care System (United States)
Chein-I Chang, Univ. of Maryland, Baltimore County (United States)
Eliot Siegel, Univ. of Maryland School of Medicine (United States)
VA Maryland Health Care System (United States)

Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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