This talk deals with two aspects of medical Imaging: image reconstruction and segmentation. The first part covers the image reconstruction for magnetic resonance imaging (MRI). Despite the capability of providing high-resolution images, the difficulties associated with lengthy acquisition time necessitate reconstruction of the final image from a limited number of k-space samples. The reconstruction problem falls under the broad class of ill-posed inverse problems, and regularization is necessary for obtaining stable and meaningful solutions. However, the accuracy of regularized output depends on the regularization parameter choice. The adaptive estimation of the regularization parameter from the data for sparsity-promoting methods will be discussed. The second part focuses on nerve segmentation in ultrasound images. The automated segmentation of the median nerve at the wrist and from wrist to elbow using different deep learning models along with the associated challenges will be discussed. Finally, the talk will conclude with a discussion on the implementation of the model in real-time. Speaker(s): Dr. Raji Susan Mathew, Virtual: https://events.vtools.ieee.org/m/377685