ABSTRACT
Objectives Define a clinically usable preprocessing pipeline for MRI data
Predict brain age using various machine learning and deep learning algorithms
Define Caveat against common machine learning traps
Data and Methods We used 1597 open-access T1 weighted MRI from 24 hospitals.
Preprocessing consisted in applying : N4 bias field correction, registration to MNI152 space, white and grey stripe intensity normalization, skull stripping and brain tissue segmentation
Prediction of brain age was done with growing complexity of data input (histograms, grey matter from segmented MRI, raw data) and models for training (linear models, non linear model such as gradient boosting over decision trees, and 2D and 3D convolutional neural networks).
Work on interpretability consisted in (i) proceeding on basic data visualization like correlations maps between age and voxels value, and generating (ii) weights maps of simpler models, (iii) heatmaps from CNNs model with occlusion method.
Results Processing time seemed feasible in a radiological workflow : 5 min for one 3D T1 MRI.
We found a significant correlation between age and gray matter volume with a correlation r = -0.74. Our best model obtained a mean absolute error of 3.60 years, with fine tuned convolution neural network (CNN) pretrained on ImageNet.
We carefully analyzed and interpreted the center effect.
Our work on interpretability on simpler models permitted to observe heterogeneity of prediction depending on brain regions known for being involved in ageing (grey matter, ventricles). Occlusion method of CNN showed the importance of Insula and deep grey matter (thalami, caudate nuclei) in predictions.
Conclusions Predicting the brain age using deep learning could be a standardized metric usable in daily neuroradiological reports. An explainable algorithm gives more confidence and acceptability for its use in practice. More clinical studies using this new quantitative biomarker in neurological diseases will show how to use it at its best.
About Owkin OWKIN was co-founded in 2016 by Thomas Clozel, MD, a clinical research doctor and former assistant professor in clinical hematology and Gilles Wainrib, PhD, a pioneer in the field of Artificial Intelligence in biology. OWKIN passed the proof-of-concept phase and is now providing its innovative AI algorithms to several of the largest cancer centers and pharmaceutical companies in Europe and in the US. With offices in New York and Paris, we pride ourselves in building a company culture around transparency, collaboration, challenge, optimism and fun.
Owkin’s team Owkin’s team is international, multidisciplinary with incredible talent in machine learning, medicine and business. Our data scientists are among the best in the world, with several Kaggle Masters (top global 100), a DREAM Challenge top performer, and publications in ICML, NIPS and other top scientific journals.
Tasks repartition Idea : Thomas Clozel, Roger Stup, Simon Jegou, Paul Herent
Bibliography : Paul Herent, Simon Jegou, Thomas Clozel
Data access : Simon Jegou, Paul Herent
Data cleaning : Simon Jegou, Paul Herent
Data analysis : Simon Jegou, Paul Herent
Data preprocessing : Simon Jegou, Paul Herent
Data analysis : Simon Jegou, Paul Herent
Training of models : Simon Jegou, Paul Herent
Work on interpretability : Simon Jegou, Paul Herent
Writing : Paul Herent
Rereading : Simon Jegou, Thomas Clozel, Julien Savatovsky, Roger Stupp, Olivier Elemento, Kim Gillier
Submission to medical congress : Paul Herent, Simon Jegou
Thanks to…
Simon Jegou, for your mentoring in Machine Learning,
Thomas Clozel and Gilles Wainrib, for your welcome at Owkin, very benevolent, Roger Stupp, for your support and re-reading,
Julien Savatovsky, for your support and re-reading,
Valentin Ame and Sylvain Toldo, for your help on the beautiful figures and the design of the related blogpost,
All the Owkin team members, for the great team work we did (and hope we’ll do) between Paris and New York : Anna Huyghues Despointes, Anna I. Bondarenko, Pierre Courtiol, Derek T. Russell-Kraft, Cedric Whitney, Meriem Sefta, Vincent Lepage, Adrian Gonzalez, Maxime HE,Paul Jehanno, Raphaël Léger, Alicia Simion, Eric Tramel, Mikhail Zaslavskiy, Pierre Manceron, Chloé Simpson, Paul Mabillot, Valentin Amé, Mathieu Galtier, Camille Marini, Sylvain Toldo, Charlie Saillard, Olivier Dehaene, Olivier Moindrot,
Pascal Roux, for your support, your help, your advices,
Axelle, for your patience and support.
