DeepvolBrain

ANR-18-CE45-0013 (2019-2023)

Abstract

Magnetic resonance (MR) imaging plays a crucial role in the detection of pathologies, the study of brain organization and the clinical research. Every day, a vast amount of data is produced in clinical settings and this number is increasing rapidly, which prevents the use of manual approaches for data analysis. As a result, the development of reliable segmentation techniques for the automatic extraction of anatomical structures is becoming an important field of quantitative MR analysis. In the DeepVolBrain project, the final goal is to develop a new generation of quantitative MRI analysis methods to cope with the rise of BigData in neuroimaging and, ultimately, to generate new knowledge. Moreover, the proposed methods will be implemented in open access to the entire community through a web platform.

Consortium

• • Pierrick Coupé (PI, CNRS, LaBRI UMR 5800 / Bordeaux, France): Medical Imaging

  • Vincent Lepetit (UB, LaBRI UMR 5800 / Bordeaux, France): Deep Learning

  • Vinh-Thong Ta (ENSEIRB, LaBRI UMR 5800 / Bordeaux, France): Image Processing 

• • Boris Mansencal (ENSEIRB, LaBRI UMR 5800 / Bordeaux, France): Computer science

  • Michael Clément (ENSEIRB, LaBRI UMR 5800 / Bordeaux, France): Image Processing

  • Floreal Morandat (ENSEIRB, LaBRI UMR 5800 / Bordeaux, France): Cloud-based platform

  • Rémi Giraud (ENSEIRB, IMS UMR 5218 / Bordeaux, France): Image Processing

  • Baudouin de Senneville (CNRS, IMB UMR 5251 / Bordeaux, France): Mathematics

  • Nicolas Papadakis (CNRS, IMB UMR 5251 / Bordeaux, France): Mathematics

  • Gwenaelle Catheline (EPHE, INCIA UMR 5287 / Bordeaux, France): Neuroscience

  • Thomas Tourdias (INSERM, Neurocentre Magendie U 1215 / Bordeaux, France): Neuroanatomy

  • Vincent Planche (INM CNRS 5293 / Bordeaux, France): Neurology

  • José Manjon (UPV, ITACA, Spain): Medical Imaging

Funded by DeepvolBrain or external fundings 

• • Xavier Amorena (IR, LaBRI): DevOps

  • Reda Kamraoui (Ph.D, LaBRI): Deep learning

  • Huy-Dung Nguyen  (Ph.D, LaBRI): Deep learning

  • Fanny Compaire (M2, IMB/LaBRI): Semi-supervised learning

Scientific Program

Objective A

First, we propose to develop novel methods by addressing the current limitations of Deep Learning (DL) in neuroimaging. DL is a fast-growing field in computer vision that has recently obtained many successes. However, so far, results obtained by DL for MRI segmentation are not as good as expected. The limited performance of DL in neuroimaging seems resulting from several factors such as few training data or large memory requirement. First, we propose to address the problem related to the limited number of training data by increasing the size of training library and by reducing the number of required training images. To this end, we will develop new data augmentation strategy and innovative DL architectures enable to improve learning speed and to reduce the number of required training images. Second, to address the memory issue related to DL, we will propose ensemble learning strategy based on locally adaptive 3D CNN. Finally, the last factor limiting the performance of DL is the quality of preprocessing to compensate for the image heterogeneity. Based on our extensive expertise, we will integrate the developed DL segmentation methods into robust pipelines.

Objective B

The emergence of very large datasets opens up new challenges related to BigData as defined by the usual 3Vs model (Volume, Variety and Velocity). The fast and robust pipelines developed for Objective A will address these challenges by proposing new tools able to process large Volume of data, to cope with image Variety from different datasets and to propose high Velocity thanks to GPU-based computing. However, two Vs have been recently added to the usual 3Vs Big Data model – Veracity and Value. In medical imaging, the reliability is related to the questions of quality control (QC) and traceability. Therefore, to ensure Veracity of the produced results, we need to propose advanced QC and to estimate a confidence of the produced results. In the DeepVolBrain project, automatic QC and error correction will be integrated into pipelines to increase the confidence in the results produced. Moreover, an extensive validation over large scale datasets will be carried out. Finally, the proposed tools will be applied to large datasets including pathological cases to demonstrate the Value and the capability of the proposed project to produce new knowledge.

Objective C

Finally, we will make our tools freely available by deploying a web platform. In the past, with the volBrain platform, we developed an original platform in a fully open access hosted at Valencia in Spain. This platform already processed more than 110.000 MRI in 3 years. This unexpected very high number of online processing pushes us to investigate new strategies to make our platform more scalable and to ensure its sustainability. In this project, we will first achieve the deployment of a second site in France at the LaBRI. Second, we will propose a new scalable and flexible architecture. This new architecture will enforce the security and privacy of the data. Finally, each of developed tools will be integrated in this new platform.

Publications:

1. P. Coupé, J. V. Manjon,  E. Lanuza, G. Catheline. Lifespan changes of the human brain in Alzheimer's disease. Nature Scientific Report, 2019.

2. K. Hett, V.-T. Ta, G. Catheline, T. Tourdias, J. V Manjón, P. Coupé. Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification. Nature Scientific Report, 9 (1), 1-19, 2019.

3. P. Coupé, B. Mansencal, M. Clément, R. Giraud, B. Denis de Senneville, V.-T Ta, V. Lepetit, J. V. Manjon.  AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation. MICCAI'19, 2019.

4. J. V. Manjon, A Berto, J. E. Romero, E. Lanuza, R. Vivo-Hernando, F. Aparici-Robles, P. Coupé.  pBrain: A novel pipeline for Parkinson related brain structure segmentation. Neuroimage Clinical, 2020.

5. P. Coupé, B. Mansencal, M. Clément, R. Giraud, B. Denis de Senneville, V.-T Ta, V. Lepetit, J. V. Manjon.  AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation. NeuroImage, 219, 117026, 2020. 

6. de Senneville, B.D., Manjon, J.V. and Coupé, P., 2020. RegQCNET: Deep quality control for image-to-template brain MRI affine registration. Physics in Medicine & Biology, 65(22), p.225022.

7. J. V. Manjon, J. E. Romero, R. Vivo-Hernando, G. Rubio, F. Aparici, M. Iglesia-Vaya, T. Tourdias, P. Coupé. Blind MRI Brain lesion inpainting using Deep Learning. SASHIMI workshop at MICCAI2020, 2020

8. K. Hett, V-T. Ta,  I. Oguz, J. V. Manjon, P. Coupé. Multi-scale Graph-based Grading for Alzheimer's Disease Prediction. Medical Image Analysis, 67, 101850, 2021.

9. H.-D. Nguyen, M. Clément, B. Mansencal, P. Coupé. Deep Grading based on Collective Artificial Intelligence for AD Diagnosis and Prognosis. Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2021, 2021

10. R. A. Kamraoui, V.-T. Ta, N. Papadakis, F. Compaire, J. V. Manjon, P Coupé. POPCORN: Progressive Pseudo-Labeling with Consistency Regularization and Neighboring. MICCAI 2021, Sep 2021, Strasbourg, pp.373-382, 2021

11. Pin, G., Coupé, P., Nadal, L., Manjon, J.V., Helmer, C., Amieva, H., Mazoyer, B., Dartigues, J.F., Catheline, G. and Planche, V., 2021. Distinct hippocampal subfields atrophy in older people with vascular brain injuries. Stroke, 52(5), pp.1741-1750.

12. R. A. Kamraoui, V.-T. Ta, T. Tourdias, B. Mansencal, J. V. Manjon, P. Coupé. DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation, Medical Image Analysis, 76, 102312, 2022.

13. J.E. Romero, P. Coupé, E. Lanuza, G. Catheline, J. V. Manjon. Towards a unified analysis of cerebellum maturation and aging across the entire lifespan: A MRI analysis. Human Brain Mapping, 2021.

14. V. Planche, J. V. Manjon, B. Mansencal, E. Lanuza, T. Tourdias, G. Catheline, P. Coupé. Structural progression of Alzheimer’s disease over decades: the MRI staging scheme.  Brain Communications 4, no. 3 (2022): fcac109.

15. J. V. Manjon, J.E. Romero, R. Vivo-Hernando, G. Rubio, F. Aparici, M. de la Iglesia-Vaya, P. Coupé. vol2Brain: A new online Pipeline for whole Brain MRI analysis, Frontiers in Neuroinformatics, volume 16, Article 862805, 2022.

16. P. Coupé, J. V. Manjón, B. Mansencal, T. Tourdias, G. Catheline, V. Planche. Hippocampal‐amygdalo‐ventricular atrophy score: Alzheimer disease detection using normative and pathological lifespan models. Human Brain Mapping (2022).

17. R. Kamraoui, B. Mansencal, J.V. Manjon, P. Coupé. Longitudinal detection of new MS lesions using Deep Learning. Frontiers in Neuroimaging-Brain Imaging Methods. 2022

18. H.-D. Nguyen, M. Clément, B. Mansencal, P. Coupé. Interpretable differential diagnosis for Alzheimer’s disease and Frontotemporal dementia. MICCAI 2022, 2022

19. Huy-Dung Nguyen, Michaël Clément, Boris Mansencal, Pierrick Coupé. Towards better interpretable and generalizable AD detection using collective artificial intelligence, Computerized Medical Imaging and Graphics. 2023

20. Vincent Planche, Boris Mansencal, José V Manjon, Thomas Tourdias, Gwenaëlle Catheline, Pierrick Coupé. Anatomical MRI staging of frontotemporal dementia variants.  Alzehiemer's & Dementia, 2023.

21. Nguyen, H.D., Clément, M., Planche, V., Mansencal, B. and Coupé, P., 2023. Deep grading for MRI-based differential diagnosis of Alzheimer’s disease and Frontotemporal dementia. Artificial Intelligence in Medicine, 144, p.102636.

22. Coupé, P., Planche, V., Mansencal, B., Kamroui, R.A., Koubiyr, I., Manjòn, J.V. and Tourdias, T., 2023. Lifespan neurodegeneration of the human brain in multiple sclerosis. Human Brain Mapping, 44(17), pp.5602-5611.

23. Nguyen, H.D., Clément, M., Mansencal, B. and Coupé, P., 2023, October. 3D Transformer based on deformable patch location for differential diagnosis between Alzheimer’s disease and Frontotemporal dementia. In International Workshop on Machine Learning in Medical Imaging (pp. 53-63). Cham: Springer Nature Switzerland.

24. Nguyen, H.D., Clément, M., Mansencal, B. and Coupé, P., 2023. Brain Structure Ages--A new biomarker for multi-disease classification. Human Brain Mapping, 2024.