The Design and Development of Automated Knee Cartilage Segmentation Framework Knee cartilage manual segmentation is tedious. Hence, an automated approach is preferable. Convolutional neural networks (CNNs) have various application in automatic medical image segmentation task, including knee cartilage segmentation. Generative adversarial networks are popular recently due to the capability in generating images that are similar to real images. In this paper, we formulate the segmentation problem in an adversarial training approach to improve CNNbased cartilage segmentation. Two individual networks are introduced which are segmentation network and evaluation network. Segmentation network learn the feature of cartilage and generate segmented probability map while evaluation network will benchmark it. This new approach has been evaluated on medical image database and benchmarked with UNet. The proposed method achieved accuracy of 96.8% when tested on 50 images.

Assessing Knee OA Severity with CNN attentionbased endtoend architectures This work proposes a novel endtoend convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using XRay images, which incorporates trainable attention modules acting as unsupervised finegrained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing stateoftheart CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST).