ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing. (June 2022) - Thibeau-Sutre, Elina; Díaz, Mauricio; Hassanaly, Ravi; Routier, Alexandre; Dormont, Didier; Colliot, Olivier; Burgos, Ninon
Highlights: ClinicaDL, an open-source deep learning software, was implemented to allow neuroimaging processing in a safe environment avoiding common pitfalls found in the literature (unclear preprocessing of neuroimaging data sets, data leakage, and lack of reproducibility). A large diversity of functionalities was implemented: by combining it with Clinica it allows easily handling raw imaging data sets, preprocessing images, checking their quality, training networks, inferring results on new data, and interpreting trained networks. The platform is meant to be extensible thanks to documentation and source code structure that enable the easy implementation of new options of existing functionalities by advanced users. Abstract: Background and Objective: As deep learning faces a reproducibility crisis and studies on deep learning applied to neuroimaging are contaminated by methodological flaws, there is an urgent need to provide a safe environment for deep learning users to help them avoid common pitfalls that will bias and discredit their results. Several tools have been proposed to help deep learning users design their framework for neuroimaging data sets. Software overview: We present here ClinicaDL, one of these software tools. ClinicaDL interacts with BIDS, a standard format in the neuroimaging field, and its derivatives, so it can be used with a large variety of data sets. Moreover, it checks the absence of data leakage when inferring the results of new data with trained networks, and saves all necessary information to guarantee the reproducibility of results. The combination of ClinicaDL and its companion project Clinica allows performing an end-to-end neuroimaging analysis, from the download of raw data sets to the interpretation of trained networks, including neuroimaging preprocessing, quality check, label definition, architecture search, and network training and evaluation. Conclusions: We implemented ClinicaDL to bring answers to three common issues encountered by deep learning users who are not always familiar with neuroimaging data: (1) the format and preprocessing of neuroimaging data sets, (2) the contamination of the evaluation procedure by data leakage and (3) a lack of reproducibility. We hope that its use by researchers will allow producing more reliable and thus valuable scientific studies in our field.