Skip to main content

OAI meniscus segmentation work accepted for publication in Osteoarthritis and Cartilage Journal

A method for the semi-automated segmentation of the meniscus in OAI images developed by the CIALAB Osteoarthritis Research Group has been accepted for publication in the Osteoarthritis and Cartilage Journal. The authors of the paper were Mark Swanson, a Medical Student with Roessler Scholarship, Jeff Prescott, an MD/PhD student in the lab, Thomas Best MD/PhD, Kimberly Powell, Rebecca Jackson MD, Furqan Haq PhD, and Metin Gurcan PhD. The abstract of the manuscript appears below.

Abstract:
Objective: The goal of this study was to develop an algorithm to semi-automatically segment the meniscus in a series of magnetic resonance (MR) images to use for normal knees and those with moderate osteoarthritis (OA).
Method: The segmentation method was developed then evaluated on 10 baseline magnetic resonance images obtained from subjects with no evidence, symptoms, or risk factors of knee (OA), and 14 from subjects with established knee OA enrolled in the Osteoarthritis Initiative (OAI). After manually choosing a seed point within the meniscus, a threshold level was calculated through a Gaussian fit model. Under anatomical, intensity, and range constraints, a threshold operation was completed followed by conditional dilation and post-processing. The post-processing operation reevaluates the pixels included and excluded in the area surrounding the meniscus to improve accuracy. The developed method was evaluated for both normal and degenerative menisci by comparing the segmentation algorithm results with manual segmentations from five human readers.
Results: The semi-automated segmentation method produces results similar to those of trained observers, with an average similarity index over 0.80 for normal participants and 0.75, 0.67, and 0.64 for participants with established knee osteoarthritis with Osteoarthritis Research International Society International (OARSI) joint space narrowing scores of 0,1, and 2 respectively.
Conclusion: The semi-automatic segmentation method produced accurate and consistent segmentations of the meniscus when compared to manual segmentations in the assessment of normal menisci in mild to moderate OA. Future studies will examine the change in volume, thickness, and intensity characteristics at different stages of OA.

Comments

Post a Comment

Popular posts from this blog

CIALAB encouraging talented young minds with summer internships

CIALAB is pleased to introduce the three interns namely Tong Gan, Rosana Rodriguez Milanes and Michael Priddy working through summer’09. Rosana Rodriguez Milanes - I am a third year undergraduate student in Electronic Engineering from Universidad del Norte, Colombia. My experience as a volunteer foreign student in the Clinical Image Analysis Laboratory has been an edifying, gratifying and enriching. Being able to participate, to learn and to collaborate in the Clinical Image Analysis Laboratory during the past two weeks has allowed me to improve my analytical and interpretative skills in processing histopathological and MRI images. I have been able to learn about segmentation, region growing, splitting and merging algorithms development. I have also had the privilege of knowing and interacting with excellent engineers who have helped me improve my skills as a foreign student. I am grateful for the opportunity that the Ohio State University has given me to collaborate and to learn with...
1 comment
Read more

Dr. Gurcan to present a tutorial at IEEE ICASSP 2010

Dr. Gurcan will present a tutorial at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2010 entitled “Biomedical Image Processing and Analysis Techniques.” ( http://www.icassp2010.org/Tutorial_03.asp) Biomedical image processing and analysis requires coordinated efforts of medical professionals, algorithmic and software engineers, and statisticians. Basic image processing techniques are frequently used in every aspect of the development from initial pre-processing techniques for noise reduction, to segmentation of lesions, to registration of lesions. Recent advances in hardware and software have made it possible to create digital scans of whole slides. These images are relatively large (100k x 100k) and in color, hence processing them present new challenges. Similarly, new computed tomography and magnetic resonance imaging scanners produce thousands of slices of images. The processing need for these images are enormous. Although biomedical image analy...
Post a Comment
Read more

Meniscus research to be presented at 2009 OARSI World Congress on Osteoarthritis

CIALAB work detailing relationships between the meniscus and cartilage, and their implications in the development and progression of osteoarthritis, has been accepted for presentation at the 2009 OARSI World Congress on Osteoarthritis. The analysis was performed by Jeff Prescott, Furqan Haq PhD, Tom Best MD/PhD, Micheal Pennell PhD, Rebecca Jackson MD, and Metin Gurcan PhD. The full abstract appears below. Purpose : To determine associations between meniscus volume and knee cartilage morphological measurements in subjects with osteoarthritis (OA). Methods : All data was obtained from the Osteoarthritis Initiative’s (OAI) public use dataset (www.oai.ucsf.edu). A sample of progression subjects produced by the OAI was used for the study (Subgroup B, release 0.B.2). Out of the 160 subjects in the sample, two had no discernable meniscus and were excluded, leaving 158 subjects in the analysis. The current study focused on the right knee, as this was the only knee for which cartilage measur...
1 comment
Read more