2019/08/17 by Avinash Kori, Kori, Avinash, Ganapathi Krishnamurthi +1
Computer Science · Engineering · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #Medical Imaging and Analysis
paper · pdf · doi:10.48550/arxiv.1908.06213
openalex publication_date 2019/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this report we present an unsupervised image registration framework, using a pre-trained deep neural network as a feature extractor. We refer this to zero-shot learning, due to nonoverlap between training and testing dataset (none of the network modules in the processing pipeline were trained specifically for the task of medical image registration). Highlights of our technique are: (a) No requirement of a training dataset (b) Keypoints i.e.locations of important features are automatically estimated (c) The number of key points in this model is fixed and can possibly be tuned as a hyperparameter. (d) Uncertaintycalculation of the proposed, transformation estimates (e) Real-time registration of images. Our technique was evaluated on BraTS, ALBERT, and collaborative hospital Brain MRI data. Results suggest that the method proved to be robust for affine transformation models and the results are practically instantaneous, irrespective of the size of the input image