Automated measurement of sulcus angle on axial knee magnetic resonance images. Issue 1 (1st June 2021)

• Record Type: Journal Article
• Authors: Ridhma, ; Kaur, Manvjeet; Sofat, Sanjeev; Chouhan, Devendra K.; Prakash, Mahesh
• Is Part Of: International journal of imaging systems and technology. Volume 32:Issue 1(2022)
• Page Start: 251
• Page End: 265
• Publication Date: 2021-06-01
• Subjects: deep learning -- MRI -- patellar dislocation -- patellar instability -- residual network -- sulcus angle; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22612
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Patellar dislocation is a disorder in the human knee where patella slips out of its expected position. The orthopedic experts need to manually measure the parameters indicating patellar instability from knee scans, which is a laborious and time‐consuming task. An automated method for measuring these parameters can resolve the issue of inter‐observer and intra‐observer variations and significantly lessen the burden of the experts. Therefore, in this work, a two‐step approach has been proposed for the automated measurement of the sulcus angle from knee magnetic resonance (MR) images to assist the experts in the diagnosis of patellar dislocation. Firstly, a variant of U‐Net architecture has been proposed harnessing the capabilities of the residual network to segment the region of interest (ROI) from the axial knee MR images. Secondly, the contour of ROI is used for the detection of key points required to compute sulcus angle. A T2‐weighted knee magnetic resonance imaging dataset of 50 patients obtained from Post Graduate Institute of Medical Education & Research, Chandigarh, has been used. Out of 50 patients, the randomly selected knee MR images of 25 patients have been used to train the segmentation model. The intersection over union and dice similarity coefficient values of 94.71% and 97.28%, respectively, were achieved for the proposed segmentation model. The automated measurement of the sulcus angle resulted in a mean error of 2.52 ± 1.52° when tested on another set of axial knee MR images of 25 patients. The proposed framework produced results comparable to the manual expert measurements.