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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. [ABSTRACT FROM AUTHOR]

Leprosy is a chronic granulomatous disease with a wide spectrum of characteristics. It is caused by Mycobacterium leprae and is still endemic in many parts of the world. Transmission occurs via inhalation of the bacilli. It is considered to be the most common cause of non-traumatic peripheral neuropathy due to its predilection for Schwann cell. Lazarine leprosy is the ulcerating type 1 reaction. [ABSTRACT FROM AUTHOR]

Knee disorders are common among the human population. Knee osteoarthritis (OA) is the most widespread knee joint disorder, which may require surgical treatment. The detection and diagnosis of knee joint disorders from medical images demand enormous human effort and time. The development of a computer‐aided diagnosis (CAD) system can notably minimise the burden of medical experts and remove the intra‐observer and inter‐observer variations. To achieve the goal, the highly challenging research problem of knee image segmentation has been frequently paid attention in past years, which can be efficiently applied in the development of the CAD system. Knee image segmentation is a challenging task owing to the image contrasts, intensity variations, shape irregularities, and the presence of thin cartilage structures. Therefore, this paper presents a literature review of automated segmentation approaches mainly focused on the segmentation of knee cartilage and bone, with respect to the underlying technical aspects, datasets used, and the performance reported. The paper also presents the growth from classical segmentation approaches towards the deep learning approaches in the knee image segmentation. Owing to the varying quality and complexity of different knee image datasets, this paper abstains from doing a rigorous comparative evaluation of image segmentation approaches. [ABSTRACT FROM AUTHOR]