Training Data Independent Image Registration With GANs Using Transfer Learning And Segmentation Information

2019/03/25 by Dwarikanath Mahapatra, Zongyuan Ge, Mahapatra, Dwarikanath +1
Computer Science · #AI in cancer detection #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Medical Image Segmentation Techniques

paper · pdf · doi:10.48550/arxiv.1903.10139

openalex publication_date 2019/03/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Registration is an important task in automated medical image analysis. Although deep learning (DL) based image registration methods out perform time consuming conventional approaches, they are heavily dependent on training data and do not generalize well for new images types. We present a DL based approach that can register an image pair which is different from the training images. This is achieved by training generative adversarial networks (GANs) in combination with segmentation information and transfer learning. Experiments on chest Xray and brain MR images show that our method gives better registration performance over conventional methods.

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