Fu, Y; Brown, NM; Saeed, SU; Casamitjana, A; Baum, ZMC; Delaunay, R; Yang, Q; ... Hu, Y; + view all Fu, Y; Brown, NM; Saeed, SU; Casamitjana, A; Baum, ZMC; Delaunay, R; Yang, Q; Grimwood, A; Min, Z; Blumberg, SB; Iglesias, JE; Barratt, DC; Bonmati, E; Alexander, DC; Clarkson, MJ; Vercauteren, T; Hu, Y; - view fewer (2020) DeepReg: a deep learning toolkit for medical image registration. The Journal of Open Source Software , 5 (55) , Article 2705. 10.21105/joss.02705.

Preview

Text 10.21105.joss.02705.pdf - Published Version Download (143kB) | Preview

Abstract

Image fusion is a fundamental task in medical image analysis and computer-assisted intervention. Medical image registration, computational algorithms that align different images together (Hill et al., 2001), has in recent years turned the research attention towards deep learning. Indeed, the representation ability to learn from population data with deep neural networks has opened new possibilities for improving registration generalisability by mitigating difficulties in designing hand-engineered image features and similarity measures for many realworld clinical applications (Fu et al., 2020; Haskins et al., 2020). In addition, its fast inference can substantially accelerate registration execution for time-critical tasks. DeepReg is a Python package using TensorFlow (Abadi et al., 2015) that implements multiple registration algorithms and a set of predefined dataset loaders, supporting both labelledand unlabelled data. DeepReg also provides command-line tool options that enable basic and advanced functionalities for model training, prediction and image warping. These implementations, together with their documentation, tutorials and demos, aim to simplify workflows for prototyping and developing novel methodology, utilising latest development and accessing quality research advances. DeepReg is unit tested and a set of customised contributor guidelines are provided to facilitate community contributions. A submission to the MICCAI Educational Challenge has utilised the DeepReg code and demos to explore the link between classical algorithms and deep-learning-based methods (Montana Brown et al., 2020), while a recently published research work investigated temporal changes in prostate cancer imaging, by using a longitudinal registration adapted from the DeepReg code (Yang et al., 2020).

Download activity - last month

Export as XMLCSVJSON

Download activity - last 12 months

Export as JSONCSVXML

Downloads by country - last 12 months

Export as XMLCSVJSON

Archive Staff Only

View Item