Faculty Publications
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Item Segmentation of focal cortical dysplasia lesions from magnetic resonance images using 3D convolutional neural networks(Elsevier Ltd, 2021) Niyas, S.; Chethana Vaisali, S.; Show, I.; Chandrika, T.G.; Vinayagamani, S.; Kesavadas, C.; Rajan, J.Computer-aided diagnosis using advanced Artific ial Intelligence (AI) techniques has become much popular over the last few years. This work automates the segmentation of Focal Cortical Dysplasia (FCD) lesions from three-dimensional (3D) Magnetic Resonance (MR) images. FCD is a type of neuronal malformation in the brain cortex and is the leading cause of intractable epilepsy, irrespective of gender or age differences. Since the neuron related abnormalities are usually resistant to drug therapy, surgical resection has been the main treatment approach for patients with intractable epilepsy. Automating the identification and segmentation of FCD is useful for neuroradiologists in pre-surgical evaluations. Convolutional Neural Networks (CNNs) have the ability to learn appropriate features from the training data without any human intervention. But, most of the state-of-the-art FCD segmentation approaches use two-dimensional (2D) CNN models despite the availability of 3D Magnetic resonance imaging (MRI) volumes, and hence fail to leverage the inter-slice information present in the MRI volumes. The major hurdles in considering a 3D CNN model are the need for a large 3D dataset, big memory, and high computation cost. A deep 3D CNN segmentation model, which can extract inter-slice information and overcomes the drawbacks of conventional 3D CNN methods to an extent, is proposed in this paper. The model uses a 3D version of U-Net with residual blocks that works on shallow depth 3D sub-volumes generated from MRI volumes. The proposed method shows superior performance over the state-of-the-art FCD segmentation methods in both qualitative and quantitative analysis. © 2021 Elsevier LtdItem Perspective analysis of assistive robots for elderly in India(Taylor and Francis Ltd., 2024) Hegde, P.; Gadag, A.; Sontakke, S.; Kumar, M.; Kholia, A.; Patel, J.; Khan, A.; Jahnavi, E.; Nabala, R.; Thotappa, D.Purpose: Assistive technology for elderly are advancing, and this study aimed to analyse the Indian perspective on utilising assistive robot technology for aiding elderly individuals. Materials and Methods: A population-based survey was undertaken to collect data from three perspectives: Relatives of the elderly, Healthcare professionals and Elderly individuals. The survey gathered 389 responses. The responses are statistically analysed, and data is visualised with different plots for better understanding. Results: It is observed that the older people rate with less conviction on the use of technology when compared to the relatives and healthcare professionals. Out of the three target groups, the elderly individuals had the most correlating attributes to purchasing the robot. Also, healthcare personnel, relatives, and older people gave 82%, 63% and 55% affirmatives to the question on purchasing the robot, respectively. And the cost of the robot is preferred to be under 6 lakh rupees. Conclusions: Though the younger generation has more orientation towards technology, older people are skeptical about handling computer gadgets or robots. However, there are significant expectations and concerns expressed by three target groups such as conversational, navigational, reminder features, security and malfunction concerns. © 2024 Informa UK Limited, trading as Taylor & Francis Group.