Work

Deep learning in medical image registration: a review

Year: 2020

Type: review

Abstract: This paper presents a review of deep learning (DL)-based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potential. We provided a comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of DL-based medical image registration.

Source: Physics in Medicine and Biology

DOI: 10.1088/1361-6560/ab843e

Authors: Yabo Fu, Yang Lei, Tonghe Wang, Walter J Curran, Tian Liu, Xiaofeng Yang

Institutions: Emory University, Winship Cancer Institute

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: Emory University, National Institutes of Health, Winship Cancer Institute, National Cancer Institute

Award: Multiparametric MRI-guided Prostate HDR Brachytherapy with Focal Tumor Boost