This repository contains the trained tensorflow models for the 3D Segmentation of Perivascular Spaces on either T1-Weighted or multi-modal T1-Weighted + FLAIR MR Images with a 3D U-Shaped Neural Network (U-net) as described in the scientific publication cited below.
The inferences created by these models should not be used for clinical purposes.
The segmentation models in this repository have been registered at the french 'Association de Protection des Programmes' under the number:
IDDN.FR.001.240015.000.S.P.2022.000.31230.
The segmentation models in this repository are provided under the Creative Common Licence BY-NC-SA.
For mono-modal models trained with T1-Weighted images, the models were trained with images with a size of 160 × 214 × 176 x 1 voxels. The training was done with images with an isotropic voxel size of 1 × 1 × 1 mm3 and with normalized voxel values in [0, 1] (min-max normalization with the max set to the 99th percentile of the brain voxel values to avoid "hot spots"). The brain is supposed to be centered, the models are trained with and without a brain mask applied on images.
For multi-modal models trained with T1 + FLAIR images, the models were trained with FLAIR images coregistered to the T1 and added as a second channel: 160 × 214 × 176 x 2 voxels.
A segmentation can be computed as the average of the inference of several models (depending on the number of folds used in the training for a particular model), the provided models can be found in the directories:
- PVS/v1/T1.PVS: is a segmentation model with incremental architecture enhancements done since the publication and is trained with a nonlinear voxel augmentation strategy that makes it more robust when used with degraded or resampled images.
- due to file size limitation the models can be found here : https://cloud.efixia.com/sharing/wknXOu07H
- Checksum : 90376aaa340e8cb0459f29a9f5f2007a
- PVS/v1/T1-FLAIR.PVS: is a multimodal segmentation model with the same architecture as the one above, using FLAIR provides a small performance increase.
- due to file size limitation the models can be found here : https://cloud.efixia.com/sharing/Dg49eKSPR
- Checksum : bef270c685f5d9bffaa28ab78576ba59
- PVS/v0/T1.PVS: is the segmentation model described in the publication
- due to file size limitation the models can be found here : https://cloud.efixia.com/sharing/dDqjx2DCq
- Checksum : 655938f815763c4a454370147f8d13e2
The resulting segmentation is an image with voxels values in [0, 1] (proxy for the probability of detection of WMH) that must be thresholded to get the final segmentation. A threshold of 0.5 has been used successfully but that depends on the preferred balance between precision and sensitivity.
The models were trained with Tensorflow >= 2.7 used with Python 3.7, they are stored in the H5 format (there is a compatibility problem when reading tendorflow H5 files by using Python version > 3.7).
The provided python script predict_one_file.py can be used as an example of usage of the model. It needs the nibabel python library to be able to read NIfTI files.
A NVIDIA GPU with at least 9Go of RAM is needed to compute inferences with the trained models.
This work has been done in collaboration between the Fealinx company and the GIN laboratory (Groupe d'Imagerie Neurofonctionelle, UMR5293, IMN, Univ. Bordeaux, CEA , CNRS) with grants from the Agence Nationale de la Recherche (ANR) with the projects GinesisLab (ANR 16-LCV2-0006-01) and SHIVA (ANR-18-RHUS-0002)
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Implementation of a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all “visible” PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm3 and 0.95 for PVSs larger than 15 mm3. We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.
@ARTICLE{10.3389/fninf.2021.641600,
AUTHOR={Boutinaud, Philippe and Tsuchida, Ami and Laurent, Alexandre and Adonias, Filipa and Hanifehlou, Zahra and Nozais, Victor and Verrecchia, Violaine and Lampe, Leonie and Zhang, Junyi and Zhu, Yi-Cheng and Tzourio, Christophe and Mazoyer, Bernard and Joliot, Marc},
TITLE={3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network},
JOURNAL={Frontiers in Neuroinformatics},
VOLUME={15},
YEAR={2021},
URL={https://www.frontiersin.org/article/10.3389/fninf.2021.641600},
DOI={10.3389/fninf.2021.641600},
ISSN={1662-5196},
}