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Title: High quality white matter reference tracts

Type Dataset Wasserthal Jakob, Neher Peter, Maier-Hein Klaus (2018): High quality white matter reference tracts. Zenodo. Dataset. https://zenodo.org/record/1477956

Authors: Wasserthal Jakob (Division of Medical Image Computing, German Cancer Research Center) ; Neher Peter (Division of Medical Image Computing, German Cancer Research Center) ; Maier-Hein Klaus (Division of Medical Image Computing, German Cancer Research Center) ;

Links

Summary

Overview

This dataset contains segmentations of 72 white matter tracts obtained from 105 subjects included in the Human Connectome Project (HCP) young adult dataset (https://www.humanconnectome.org/study/hcp-young-adult). The folder names correspond to the ID of the HCP subjects. This dataset only contains the tracts. It does not contain the original DWI data. This has to be downloaded from the HCP website (it is free, but you have to register to get access).

The data is part of the following publication:  Wasserthal et al., TractSeg - Fast and accurate white matter bundle segmentation. NeuroImage (2018). If you use the data please cite the paper.

 

Details for generating corresponding whole brain tractograms

The tracts were extracted semi-automatically from whole-brain tractograms. For a detailed description of the tract segmentation process please refer to the paper. The following MRtrix (http://www.mrtrix.org/) commands were used to obtain the whole-brain tractograms:

5ttgen fsl T1w_acpc_dc_restore_brain.nii.gz 5TT.mif -premasked dwi2response msmt_5tt Diffusion.nii.gz 5TT.mif RF_WM.txt RF_GM.txt RF_CSF.txt -voxels RF_voxels.mif -fslgrad Diffusion.bvecs Diffusion.bvals dwi2fod msmt_csd Diffusion.nii.gz RF_WM.txt WM_FODs.mif RF_GM.txt GM.mif RF_CSF.txt CSF.mif -mask nodif_brain_mask.nii.gz -fslgrad Diffusion.bvecs Diffusion.bvals tckgen -algorithm iFOD2 WM_FODs.mif output.tck -act 5TT.mif -backtrack -crop_at_gmwmi -seed_image nodif_brain_mask.nii.gz -maxlength 250 -minlength 40 -number 10M -cutoff 0.06 -maxnum 0

For "CA", "IFO_left", "IFO_right", "UF_left", "UF_right" we used tracking without anatomical constraints:

tckgen -algorithm iFOD2 WM_FODs.mif output.tck -seed_image nodif_brain_mask.nii.gz -maxlength 250 -minlength 40 -number 10M -cutoff 0.06 -maxnum 0

Due to their enormous size, the whole brain tractograms corresponding to the segmented tracts are not included this dataset. Please contact the author of the paper if you are interested in these tractograms.

 

Included tracts

1: AF_left         (Arcuate fascicle) 2: AF_right 3: ATR_left        (Anterior Thalamic Radiation) 4: ATR_right 5: CA              (Commissure Anterior) 6: CC_1            (Rostrum) 7: CC_2            (Genu) 8: CC_3            (Rostral body (Premotor)) 9: CC_4            (Anterior midbody (Primary Motor)) 10: CC_5           (Posterior midbody (Primary Somatosensory)) 11: CC_6           (Isthmus) 12: CC_7           (Splenium) 13: CG_left        (Cingulum left) 14: CG_right    15: CST_left       (Corticospinal tract 16: CST_right  17: MLF_left       (Middle longitudinal fascicle) 18: MLF_right 19: FPT_left       (Fronto-pontine tract) 20: FPT_right  21: FX_left        (Fornix) 22: FX_right 23: ICP_left       (Inferior cerebellar peduncle) 24: ICP_right  25: IFO_left       (Inferior occipito-frontal fascicle)  26: IFO_right 27: ILF_left       (Inferior longitudinal fascicle)  28: ILF_right  29: MCP            (Middle cerebellar peduncle) 30: OR_left        (Optic radiation)  31: OR_right 32: POPT_left      (Parieto‐occipital pontine) 33: POPT_right  34: SCP_left       (Superior cerebellar peduncle) 35: SCP_right  36: SLF_I_left     (Superior longitudinal fascicle I) 37: SLF_I_right  38: SLF_II_left    (Superior longitudinal fascicle II) 39: SLF_II_right 40: SLF_III_left   (Superior longitudinal fascicle III) 41: SLF_III_right  42: STR_left       (Superior Thalamic Radiation) 43: STR_right  44: UF_left        (Uncinate fascicle)  45: UF_right  46: CC             (Corpus Callosum - all) 47: T_PREF_left    (Thalamo-prefrontal) 48: T_PREF_right  49: T_PREM_left    (Thalamo-premotor) 50: T_PREM_right  51: T_PREC_left    (Thalamo-precentral) 52: T_PREC_right  53: T_POSTC_left   (Thalamo-postcentral) 54: T_POSTC_right  55: T_PAR_left     (Thalamo-parietal) 56: T_PAR_right  57: T_OCC_left     (Thalamo-occipital) 58: T_OCC_right  59: ST_FO_left     (Striato-fronto-orbital) 60: ST_FO_right  61: ST_PREF_left   (Striato-prefrontal) 62: ST_PREF_right  63: ST_PREM_left   (Striato-premotor) 64: ST_PREM_right  65: ST_PREC_left   (Striato-precentral) 66: ST_PREC_right  67: ST_POSTC_left  (Striato-postcentral) 68: ST_POSTC_right 69: ST_PAR_left    (Striato-parietal) 70: ST_PAR_right  71: ST_OCC_left    (Striato-occipital) 72: ST_OCC_right  

Cross-validation data splits

The following data splits were used for cross-validation in the TractSeg paper:

fold1 = ['992774', '991267', '987983', '984472', '983773', '979984', '978578', '965771', '965367', '959574', '958976', '957974', '951457', '932554', '930449', '922854', '917255', '912447', '910241', '907656', '904044'] fold2 = ['901442', '901139', '901038', '899885', '898176', '896879', '896778', '894673', '889579', '887373', '877269', '877168', '872764', '872158', '871964', '871762', '865363', '861456', '859671', '857263', '856766'] fold3 = ['849971', '845458', '837964', '837560', '833249', '833148', '826454', '826353', '816653', '814649', '802844', '792766', '792564', '789373', '786569', '784565', '782561', '779370', '771354', '770352', '765056'] fold4 = ['761957', '759869', '756055', '753251', '751348', '749361', '748662', '748258', '742549', '734045', '732243', '729557', '729254', '715647', '715041', '709551', '705341', '704238', '702133', '695768', '690152'] fold5 = ['687163', '685058', '683256', '680957', '679568', '677968', '673455', '672756', '665254', '654754', '645551', '644044', '638049', '627549', '623844', '622236', '620434', '613538', '601127', '599671', '599469']

Hyperparameters were optimized using fold 1-3 for training and fold 4 for validation.

The final 5-fold cross-validation (results reported in the TractSeg paper) was done by always training on 3 folds, selecting the best epoch by evaluating on the fourth fold and then reporting the final results (of the model from the best epoch) on the fifth fold.

The pretrained TractSeg model which will automatically be used when you download TractSeg was trained on fold1+fold2+fold3.

Please use the same data splits to make your work comparable.

 

Data format

From version 1.2.0 of this dataset onwards it uses the newest trackvis (trk) standard (using nibabel.streamlines API). Streamlines are saved in native voxel space and when loaded are transformed to coordinate space using the affine stored in the trk file header. In the previous versions of the dataset the older nibabel.trackvis API was used (streamlines are saved in real coordinate space and no affine is applied when loading them).

More information

  • DOI: 10.5281/zenodo.1477956

Subjects

  • dMRI, Tractography, White Matter, Human Connectome Project, Diffusion MRI, Connectomics, Segmentation

Dates

  • Publication date: 2018
  • Issued: November 05, 2018

Notes

Other: {"references": ["Wasserthal et al.: Tractseg - fast and accurate white matter tract segmentation. NeuroImage (2018)", "https://github.com/MIC-DKFZ/TractSeg/"]}

Rights

Much of the data past this point we don't have good examples of yet. Please share in #rdi slack if you have good examples for anything that appears below. Thanks!

Format

electronic resource

Relateditems

DescriptionItem typeRelationshipUri
IsSupplementTohttps://doi.org/10.1016/j.neuroimage.2018.07.070
IsVersionOfhttps://doi.org/10.5281/zenodo.1088277
IsPartOfhttps://zenodo.org/communities/dmri
IsPartOfhttps://zenodo.org/communities/zenodo