Download as PDF, PPTX
![Cohen-Adad: Introduction to the Spinal Cord Toolbox (SCT)
“Spine Generic Protocol” initiative
27Alley et al., ISMRM 2018 ; “white paper” in preparation
• Solution: Establish a consensus set of qMRI acquisition
parameters for the spinal cord at 3T. [Alley18]
• Similar to NINDS-CDE initiative, but focusing on qMRI
metrics and not specific to particular diseases
• 21 international sites involved in the optimization
• Protocol available for GE, Philips and Siemens at:
www.spinalcordmri.org/protocols](https://image.slidesharecdn.com/20190808sctintroduction-190810135751/75/SCT-course-27-2048.jpg)
![Cohen-Adad: Introduction to the Spinal Cord Toolbox (SCT)
Data structure: BIDS
31Gorgolewski et al. Sci Data 3, 160044 ; [2] https://github.com/bids-standard/bep001
http://bids.neuroimaging.io/ spineGeneric_multiSubjects
!"" dataset_description.json
!"" participants.json
!"" participants.tsv
!"" sub-ucl01
!"" sub-ucl02
!"" sub-ucl03
!"" sub-ucl04
!"" sub-ucl05
#"" sub-ucl06
!"" anat
$ !"" sub-ucl06_T1w.json
$ !"" sub-ucl06_T1w.nii.gz
$ !"" sub-ucl06_T2star.json
$ !"" sub-ucl06_T2star.nii.gz
$ !"" sub-ucl06_T2w.json
$ !"" sub-ucl06_T2w.nii.gz
$ !"" sub-ucl06_acq-MToff_MTS.json
$ !"" sub-ucl06_acq-MToff_MTS.nii.gz
$ !"" sub-ucl06_acq-MTon_MTS.json
$ !"" sub-ucl06_acq-MTon_MTS.nii.gz
$ !"" sub-ucl06_acq-T1w_MTS.json
$ #"" sub-ucl06_acq-T1w_MTS.nii.gz
#"" dwi
!"" sub-ucl06_dwi.bval
!"" sub-ucl06_dwi.bvec
!"" sub-ucl06_dwi.json
#"" sub-ucl06_dwi.nii.gz
What is BIDS?
BIDS is a convention for organizing neuroimaging
data files and folders
Why BIDS?
Easier to share data and to process them with
BIDS-compatible processing pipelines.](https://image.slidesharecdn.com/20190808sctintroduction-190810135751/75/SCT-course-31-2048.jpg)
![Cohen-Adad: Introduction to the Spinal Cord Toolbox (SCT)
Manual correction
35https://github.com/sct-pipeline/spine-generic/blob/master/README.md#quality-control-rapid
• Number of subjects that needed
corrections:
• T1w seg: 17 (8%)
• T2w seg: 2 (1%)
• T2star GM seg: 15 (7%)
• T1w_ax: 8 (4%)
• Vertebral labeling (on T1w): 25 (12%)
• SOP for manual correction after
QC [1]](https://image.slidesharecdn.com/20190808sctintroduction-190810135751/75/SCT-course-35-2048.jpg)
SCT course
- 1. Introduction to the SpinalCord Toolbox Updated: 2019-08-06 Julien Cohen-Adad, PhD Associate Professor, Ecole Polytechnique de Montreal Associate Director, Functional Neuroimaging Unit, University of Montreal Canada Research Chair in Quantitative Magnetic Resonance Imaging SCT course August 8th, 2019, Beijing, China
- 2. Outline 1. Image analysiswith the Spinal Cord Toolbox 2. Standardize image acquisition: Spine Generic Protocol 3. Let’s communicate! Cohen-Adad: Introduction to the Spinal Cord Toolbox (SCT) 2
- 3. Outline 1. Image analysiswith the Spinal Cord Toolbox 2. Standardize image acquisition: Spine Generic Protocol 3. Let’s communicate! Cohen-Adad: Introduction to the Spinal Cord Toolbox (SCT) 3
- 4. Why do weneed spinal cord imaging?
- 5. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Clinico-radiological paradox 5 Collaboration A.L. Oaklander, B. Buchbinder, MGH, Boston No abnormality above and below lesion with conventional MRI… is it healthy tissue? • 30 y.o. patient • Needle injury during corticosteroid injection at C6 level (malpractice)
- 6. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) What it “Quantitative MRI”? 6 Quantitative MRI of the Spinal Cord Edited by Julien Cohen-Adad Claudia A. M. Wheeler-Kingshott Neuronal activity fMRI Metabolism Spectroscopy F T1 Microstructure NODDIDTI_FADTI_MDqMT_kfMTsatB1+ MWF T2* MTR What is qMRI used for? •Diagnosis/prognosis traumas, neurological diseases, cancers •Objective biomarkers for testing new drugs
- 7. Problem: These quantitativeMRI data require complex data analysis
- 8. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Analysis software for spinal cord 8 problem: no standard processing tool for spinal cord brain data ?spinal cord data solution: SCT https://github.com/neuropoly/spinalcordtoolbox “SCT (Spinal Cord Toolbox) is a comprehensive and open-source library of analysis tools for multi- parametric MRI of the spinal cord.”
- 9. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Overview of SCT 9De Leener, Neuroimage 2016 Atlas-based analysis • 2D slice-by-slice • Regularized across slices & time • Robust for DWI (group-wise) Motion correction and many more features… Template and atlas C1 C3 C5 T1 Registration framework Segmentation T2w T1w SCT
- 10. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) PAM50: template of the spinal cord 10 De Leener, Neuroimage 2017 —> PAM50 Fonov, NeuroImage 2014 —> Methods for template creation Taso, MAGMA 2014 —> white matter probabilistic template Taso, NeuroImage 2015 —> white matter probabilistic template (new version) Lévy, NeuroImage 2015 —> white matter atlas T2-weighted template spinothalamic spinocerebellar corticospinal cuneatus gracilis C1 C5 T1 C3 gray matter white matter cerebrospinal fluid 0 1 0 1 Probabilistic structure White matter atlasT2-weighted template spinothalamic spinocerebellar corticospinal cuneatus gracilis C1 C5 T1 C3 gray matter white matter cerebrospinal fluid 0 1 0 1 Probabilistic structure White matter atlasSpinal cord & brainstem templates in ICBM space Benjamin De Leener Simon Lévy
- 11. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Deep learning for segmentation 11 Spinal cord segmentation • Models trained on ~3000 subjects from ~30 centers • Robust towards various pathologies Charley Gros Patient with cord compression Gros et al., Neuroimage:1805.06349 invivoexvivo Perone et al. Sci Rep 2018 Gray matter segmentation Christian Perone 4676 axial slices @ 100 μm isotropic
- 12. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Applications (150+ citations) Functional MRI • Kong et al. Intrinsically organized resting state networks in the human spinal cord. PNAS 2014 • Vahdat et al. Simultaneous Brain–Cervical Cord fMRI Reveals Intrinsic Spinal Cord Plasticity during Motor Sequence Learning. PLOS Biology 2015 • Eippert F. et al. Investigating resting-state functional connectivity in the cervical spinal cord at 3T. BioRxiv 2016 • Weber K.A. et al. Functional Magnetic Resonance Imaging of the Cervical Spinal Cord During Thermal Stimulation Across Consecutive Runs. Neuroimage 2016 • Eippert et al. Denoising spinal cord fMRI data: Approaches to acquisition and analysis. Neuroimage 2016 Quantitative structural MRI (diffusion, MT, etc.) • Taso et al. A reliable spatially normalized template of the human spinal cord — Applications to automated white matter/gray matter segmentation…. Neuroimage 2015 • Weber et al. Lateralization of cervical spinal cord activity during an isometric upper extremity motor task with functional magnetic resonance imaging. Neuroimage 2016 • Samson et al., ZOOM or non-ZOOM? Assessing Spinal Cord Diffusion Tensor Imaging protocols for multi-centre studies. PLOS One 2016 (in press) • Taso et al.Tract-specific and age-related variations of the spinal cord microstructure: a multi-parametric MRI study using diffusion tensor imaging (DTI) and ihMT. NMR Biomed 2016 • Massire A. et al. High-resolution multi-parametric quantitative magnetic resonance imaging of the human cervical spinal cord at 7T. Neuroimage 2016 • Duval et al. g-Ratio weighted imaging of the human spinal cord in vivo. Neuroimage 2016 Application in patients • Yiannakas et al. Fully automated segmentation of the cervical cord from T1-weighted MRI using PropSeg: Application to multiple sclerosis. NeuroImage: Clinical 2015 • Castellano et al., Quantitative MRI of the spinal cord and brain in adrenomyeloneuropathy: in vivo assessment of structural changes. Brain 2016 • Grabher et al., Voxel-based analysis of grey and white matter degeneration in cervical spondylotic myelopathy. Sci Rep 2016;6:24636. • Talbott JF, Narvid J, Chazen JL, Chin CT, Shah V. An Imaging Based Approach to Spinal Cord Infection. Seminars in Ultrasound, CT and MRI. 2016 • Martin et al. A Novel MRI Biomarker of Spinal Cord White Matter Injury: T2*-Weighted White Matter to Gray Matter Signal Intensity Ratio. AJNR 2017 • David et al. The efficiency of retrospective artifact correction methods in improving the statistical power of between-group differences in spinal cord DTI. Neuroimage 2017 • Peterson et al. Test-Retest and Interreader Reproducibility of Semiautomated Atlas-Based Analysis of Diffusion Tensor Imaging Data in Acute Cervical Spine Trauma in Adult Patients • Grabher et al. Neurodegeneration in the Spinal Ventral Horn Prior to Motor Impairment in Cervical Spondylotic Myelopathy. Journal of Neurotrauma 2017 • Smith et al. Lateral corticospinal tract damage correlates with motor output in incomplete spinal cord injury. Archives of Physical Medicine and Rehabilitation 2017 • McCoy et al. MRI Atlas-Based Measurement of Spinal Cord Injury Predicts Outcome in Acute Flaccid Myelitis. AJNR 2016 • Grabher et al., Voxel-based analysis of grey and white matter degeneration in cervical spondylotic myelopathy. Sci Rep 2016 • Hori et al., Application of Quantitative Microstructural MR Imaging with Atlas-based Analysis for the Spinal Cord in Cervical Spondylotic Myelopathy. Scientific Reports. 2018 12
- 13. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Integration into clinical workflow? 13 MRI PACS Server Clinician Workstation running SCT Dicom images Dicom images + quantitative metrics from SCT Example of clinical users or SCT:
- 14.
- 15. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Multi-center DTI study 15Samson, PLOS One 2016 Vanderbilt MontrealLondon A P R b=0FA 0.1 0.9 L Template Atlas London Vanderbilt M ontreal FA in fasciculus gracilis 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 L R L R L R • 3 sites • 5 subjects per site • Two MRI brands (Philips, Siemens)
- 16. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) SCT in patients 16 T2*-w WM atlas Syringo. @3T Cord seg CSA=77.4 mm2 WM/GM seg CSA=60.4/16.0 CSM @3T CSA=77.9 mm2 CSA=61.9/15.4 Martin & Fehlings Toronto Western Hospital CSM @3T CSA=65.3 mm2 CSA=54.2/12.9 Martin & Fehlings Toronto Western Hospital MS @7T CSA=70.6 mm2 CSA=58.9/11.7 Mainero MGH, Boston ALS @7T CSA=73.9 mm2 CSA=61.3/12.2 Atassi MGH, Boston SCI @7T CSA=71.5 mm2 CSA=61.0/11.8 Oaklander MGH, Boston De Leener, Neuroimage 2016
- 17. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Gray matter atrophy in ALS 17Paquin, AJNR 2017 Collaboration: PF Pradat (Pitié-Salpêtrière, Paris) p-value: p=0.0041 p-value: p=0.0203 A. B. 0.0065 0.0116 0.0024 0.0707 0.0045 0.0530 0.0337 0.0412 GM atrophy is a good biomarker for ALS diagnosis ALSFRS-R1year PredictionError Clinical predictors Clinical predictors + SCCSA Clinical predictors + GMCSA + WM/GMCSA Mean error (Best value: 0.0) 2.05 ± 12.97 1.80 ± 8.67 1.63 ± 8.42 Prediction at 1 year Charley Gros Marie-Eve Paquin Spinal cord Gray Matter segmentation We need better biomarkers of motoneuron degeneration for earlier diagnosis and treatment monitoring
- 18. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Mapping MS lesions in the cord 18Kearney, Nat Rev Neuroscience 2015 Charley GrosDominique Eden brain spinal cord
- 19. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Mapping MS lesions in the cord 19Eden et al. Brain 2019 R. Bakshi (USA) C. Mainero (USA) T. Shepherd (USA) S. Smith (USA) J. Talbott (USA) D. Reich (USA) O. Ciccarelli (UK) E. Bannier (France) V. Callot (France) OFSEP (France) M. Filippi (Italy) T. Granberg (Sweden) M. Hori (Japan) K. Kamiya (Japan) Y. Tachibana (Japan) J. Talbott (USA) T. Granberg (Sweden) A. Badji (Canada) J. Maranzano (Canada) R. Zhuoquiong (China) SCTSpinal Cord Toolbox Charley GrosDominique Eden • 12 MS clinical centers —> 600 patients • 8 MD/neuroradiologists who segmented lesions • Automatic analysis with SCT
- 20. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Mapping MS lesions in the cord 20Eden et al. Brain 2019 Charley GrosDominique Eden
- 21. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) ProbabilisticdensityofMSlesions(%) Vertebral Level Mapping MS lesions in the cord 21Eden et al. Brain 2019 Vertebral Level ProbabilisticdensityofMSlesions(%) Lesion Probability Map (N=600) Charley GrosDominique Eden
- 22. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Lesion segmentation with deep learning x 22Gros, Neuroimage 2018 Charley Gros Dice: 63.1 Dice: 70.1 Dice: 76.6 Dice: 63.1 Dice: 77.3 T2*-w axial orientations T2-w sagittal orientations Raw Manual Auto Raw Manual Auto • Results: • Robust to image contrast and orientation. • Overall good sensitivity (85%) but 30% false positive rate (i.e. spurious lesions)
- 23. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Shape analysis of cord compression 23Martin et al. BMJ Open 2018 Collaboration: Drs. Allan Martin & Michael Fehlings (U Toronto) • SCT provides tools to automatically analyze the shape of the spinal cord in the axial plane • Relevant metrics include antero-posterior and right-left dimensions. • Particularly interesting for studying traumatic and non-traumatic cord compression Patient with degenerative cervical myelopathy A-Pdiameter (mm) 4 5 6 7 8 9 Area(mm2) 30 40 50 60 70 Eccentricity 0.7 0.75 0.8 0.85 0.9 0.95 Symmetry 0.9 0.92 0.94 0.96 0.98 1 Orientation -10 -5 0 5 10 Superior-inferior direction (Slice #) R-Ldiameter (mm) 7 9 11 13 Metrics of spinal cord shape sensitive for cord compression
- 24. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Detecting anomalies in stroke patients 24
- 25. Outline 1. Image analysiswith the Spinal Cord Toolbox 2. Standardize image acquisition: Spine Generic Protocol 3. Let’s communicate! Cohen-Adad: Introduction to the Spinal Cord Toolbox (SCT) 25
- 26. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) qMRI: What protocol to use? 26 If a researcher not familiar with qMRI was to start a project involving spinal cord imaging, what sequence and parameters to choose? qMTMTR MT_sat MWF CHARMED NODDI DTI DKI g-ratio ultra-short TE T1 T2* T2 PD This can become overwhelming!
- 27. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) “Spine Generic Protocol” initiative 27Alley et al., ISMRM 2018 ; “white paper” in preparation • Solution: Establish a consensus set of qMRI acquisition parameters for the spinal cord at 3T. [Alley18] • Similar to NINDS-CDE initiative, but focusing on qMRI metrics and not specific to particular diseases • 21 international sites involved in the optimization • Protocol available for GE, Philips and Siemens at: www.spinalcordmri.org/protocols
- 28. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) “Spine Generic Protocol” initiative 28Alley et al., ISMRM 2018 ; “white paper” in preparation Total acquisition time: 20-30 minutes Diffusion Tensor Imaging - Demyelination in WM - Axon degeneration Magnetization Transfer - Demyelination in WM 2D multi-echo GRE - Gray matter atrophy 3D T1w 1mm - Brain & Spine assessment 3D T2w 0.8mm - Cord atrophy
- 29. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Multi-site, multi-subjects 29 Goals • assessing efficacy (i.e. best of the best) vs. efficiency (in situ usage) • Provide publicly-available database: could be used for generating normative values, developing new analysis methods (train models, organize challenges, etc.) Dataset • ~6 subjects (3 males, 3 females), 20-40 y.o. • # sites: 32 acquired, 4 confirmed, 5 maybe —> ~250 subjects • It is not too late to participate! Drop me an email if you are interested.
- 30. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Open access • Each subject is 25-40 MB • Total size: 6.8 GB (207 subjects) 30 Data available at: https://openneuro.org/datasets/ds001919/
- 31. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Data structure: BIDS 31Gorgolewski et al. Sci Data 3, 160044 ; [2] https://github.com/bids-standard/bep001 http://bids.neuroimaging.io/ spineGeneric_multiSubjects !"" dataset_description.json !"" participants.json !"" participants.tsv !"" sub-ucl01 !"" sub-ucl02 !"" sub-ucl03 !"" sub-ucl04 !"" sub-ucl05 #"" sub-ucl06 !"" anat $ !"" sub-ucl06_T1w.json $ !"" sub-ucl06_T1w.nii.gz $ !"" sub-ucl06_T2star.json $ !"" sub-ucl06_T2star.nii.gz $ !"" sub-ucl06_T2w.json $ !"" sub-ucl06_T2w.nii.gz $ !"" sub-ucl06_acq-MToff_MTS.json $ !"" sub-ucl06_acq-MToff_MTS.nii.gz $ !"" sub-ucl06_acq-MTon_MTS.json $ !"" sub-ucl06_acq-MTon_MTS.nii.gz $ !"" sub-ucl06_acq-T1w_MTS.json $ #"" sub-ucl06_acq-T1w_MTS.nii.gz #"" dwi !"" sub-ucl06_dwi.bval !"" sub-ucl06_dwi.bvec !"" sub-ucl06_dwi.json #"" sub-ucl06_dwi.nii.gz What is BIDS? BIDS is a convention for organizing neuroimaging data files and folders Why BIDS? Easier to share data and to process them with BIDS-compatible processing pipelines.
- 32. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Processing • Full analysis pipeline + documentation: https://spine-generic.rtfd.io • Processing of 207 subjects done in ~6h (2.2GHz x 64, 512 GB RAM) 32
- 33. (*) T8 T1w T2w Cord & CSF WM& GM WM atlas PAM50 Template Cord CSA Cord CSA GM CSA MTR MTsat T1 (*) Powered by multiple open-source libraries, incl. ANTs, dipy, nibabel, numpy, scipy, etc. FA MD RD … T1w Seg Vert. labeling Warping field Coregistration ME-GRE MTon MToff T1w Seg WM T2w WMSeg DWI Motion correction DWI GM Seg
- 34. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Processing: QC (Live example) 34
- 35. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Manual correction 35https://github.com/sct-pipeline/spine-generic/blob/master/README.md#quality-control-rapid • Number of subjects that needed corrections: • T1w seg: 17 (8%) • T2w seg: 2 (1%) • T2star GM seg: 15 (7%) • T1w_ax: 8 (4%) • Vertebral labeling (on T1w): 25 (12%) • SOP for manual correction after QC [1]
- 36. Results of themulti-subject spine generic protocol (*) (*) To reproduce: Data: https://openneuro.org/datasets/ds001919/versions/1.0.0 Processing pipeline (include manual corrections): https://github.com/sct-pipeline/spine-generic/releases/tag/1.0.0
- 37. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) T1w: Image quality 37
- 38. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) T1w: Cord CSA 38 Cord cross-sectional area (CSA) averaged between C2-C3 A P R 1 mm isotropic L
- 39. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) T2w: Image quality 39
- 40. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) T2w: Cord CSA 40 A P R L 0.8 mm isotropic Cord cross-sectional area (CSA) averaged between C2-C3
- 41. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Diffusion MRI: Data quality 41
- 42. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Diffusion MRI (Fractional Anisotropy) 42 A P R L 0.9 x 0.9 x 5 mm3 Fractional Anisotropy (FA) averaged in WM between C2-C5
- 43. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Magnetization Transfer: Data Quality 43
- 44. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Magnetization Transfer Ratio (MTR) 44 A P R L 0.9 x 0.9 x 5 mm3 MTR averaged in WM between C2-C5
- 45. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Next steps & Perspectives Next steps? • Finalize data collection • 35 sites: processed & shared • 5 sites: recently added (processing todo) • New sites? • Finalize manuscript What can this project be useful for? • Normative values could be used for clinical studies • Neuroscientific investigations: normalizing CSA, effect of sex and age on CSA and other qMRI metrics, etc. • Ground truth spinal cord segmentations can be used for training novel deep-learning algorithms 45
- 46. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Limitation of deep-learning in medical imaging 46Perone & Cohen-Adad, Journal of Medical Artificial Intelligence 2019 People are different… but not that much! Imaging parameters, MRI model, etc. create the largest variability:
- 47. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) S ∝ sin(α) . (1 − e−TR/T1) . e−TE/T2* 1 − cos(α) . e−TR/T1 • Bloch equation (for gradient echo): Deep-learning informed by MR physics 47 we know we don’t know (model will learn) • Problem: How to introduce this equation in the architecture? param_T1{} param_T2{} param_T2-IR{} Segmentations Modular DL Architecture • Idea: Learn image features based on acquisition parameters that have an impact on the contrast (TR, TE, etc.)
- 48. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) FiLM: Visual Reasoning with General Conditioning Layer 48Perez, arXiv:1709.07871, 2018 Input Image Output Segmentation ConvolutionalLayer ConvolutionalLayer … ReLU ReLU Convolutional Neural NetworkConvolutional Neural NetworkConvolutional Neural Network Collaboration with J.P. Cohen (Mila)
- 49. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) FiLM: Visual Reasoning with General Conditioning Layer 49Perez, arXiv:1709.07871, 2018 Input Image Output Segmentation ConvolutionalLayer ConvolutionalLayer … ReLU ReLU FiLMLayer Convolutional Neural NetworkConvolutional Neural NetworkConvolutional Neural Network modulated by acquisition parameters FiLMLayer • Flip Angle • Echo Time • Repetition Time • … Input Acquisition Parameters FiLM Generator (γ,β) (γ,β) (γ,β) FiLM(x)=γ(z)⊙x+β(z). x: Feature map z: conditioning input γ, β: FiLM parameters Collaboration with J.P. Cohen (Mila)
- 50. Results using the“Spine Generic” database
- 51. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) FiLM: Results 51 Charley Gros Christian Perone Benoit Sauty-de-Chalon Valentine Louis-Lucas Training MTSMToff MTon T2star T1w Data from 144 subjects, scanned at 24 sites, with 5 contrasts (~10k 2D images) Testing Data from 24 subjects, scanned in 4 unseen sites, with 1 unseen contrast: T2w Dice Precision Recall Unet 95.1 96.4 95.8 FiLMedUnet 96.2 97.0 95.5 Results (preliminary) Collaboration with J.P. Cohen (Mila)
- 52. Outline 1. Image analysiswith the Spinal Cord Toolbox 2. Standardize image acquisition: Spine Generic Protocol 3. Let’s communicate! Cohen-Adad: Introduction to the Spinal Cord Toolbox (SCT) 52
- 53. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) How to foster clinical translation? 53 problem: • Despite efforts in open science, these advanced acquisition/processing techniques are difficult to translate because they require expert knowledge to use them • Publishing articles is not enough • How to make them available to a larger pool of clinics worldwide? solution: Communication & Training!
- 54. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) www.spinalcordmri.org 54 You can find my slides there!
- 55. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) www.spinalcordmri.org 55
- 56. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Spinal Cord MRI Workshops 56 Would you like to be on the mailing list or sponsor the event? Visit: www.spinalcordmri.org Toronto’15 45 attendees Spinal cord MR software Singapore’16 65 attendees Gray matter segmentation Spinal Cord MR Hack Organizers: PaulSummersUniMoRE,Modena,Italy CarloPorroUniMoRE,Modena,Italy JulienCohen-AdadPolyMTL,Montreal,Canada Registrationviaemail:paul.summers@unimore.it EventsponsoredbytheInternationalSpinalResearchTrust. Friday 16 May, 2014 12:00 – 18:00 Enterprise Hotel Corso Sempione 91 Milan, Italy Milan’14 35 attendees Standardize protocols across vendors Hawaii’17 70 attendees Paris’18 70 attendees London’19 60 attendees Montreal’19 65 registered
- 57. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) What’s next? • Next year: London, January 21-22, 2020 • Organizers: Ferran Prados, Julien Cohen-Adad • Concept: Abstract submission, then we pick 4-6 to present • Topics: • Killer applications for 7T • Atrophy (GM, whole cord, longitudinal) • Diseases (MS, SCI) • Lower cord applications and challenges • Histopatology • Certificate/diploma for attendees 57
- 58. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Conclusion Analysis tools for spinal cord MRI —> promote reproducibility • Standardize analysis tools: more transparent, enable cross-validation of published studies. • Automatic pipelines: prevent user bias (e.g., manual delineation of ROIs), leverage large multi-center studies • Open-source analysis tools for spinal cord MRI: SCT, spinalfmri8, JIM, etc. List of software: http://www.spinalcordmri.org/software/ Spine Generic Protocol —> promote replicability, dissemination of knowledge • Minimize wasted time & $$$ spent on pilot scans for optimization • Minimize variability in multi-site, multi-vendor studies • Images are well suited for analysis with SCT • “One-fit-all” protocol: should be adapted to specific needs • Already implemented in ~50 clinical sites, multicenter initiatives: INSPIRED (Wheeler-Kingshott), CanProCo (Kolind), NAIMS (paper under revision), NINDS-CDE (Flanders) Communication • Discussions about unmet needs between physicists, clinicians and MRI vendors is key! 58
- 59. Cohen-Adad: Introduction tothe Spinal Cord Toolbox (SCT) Acknowledgements 59 *** Open Positions! Send me an email *** Pierre Bellec Carollyn Hurst André Cyr Michael Fehlings Allan Martin Ali Akbar Claudia Gandini Wheeler-Kingshott Ferran Prados Becky Samson Joseph Paul Cohen Christopher Pal Yoshua Bengio Shigeki Aoki Masaaki Hori Akifumi Hagiwara Gerald Moran Bart Schraa Guillaume Gilbert Suchandrima Banerjee Nikola Stikov Agah Karakuzu Aldo Zaimi Alexandru Foias Atef Badji Aurélien Gilliot Benjamin De Leener B. Sauty-de-Chalon Charley Gros Christian Perone Dominique Eden Gabriel Mangeat Grégoire Germain Harris Nami Ilana Leppert Jennifer Campbell Lucas Rouhier Matthieu Parizet Mathieu Boudreau Mélanie Lubrano Nibardo Lopez-Rios Nicolas Pinon Oumayma Bounou Ryan Topfer Tanguy Duval Tommy Boshkovski Valentine Louis-Lucas