segment
¶
Module: segment.bundles
¶
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Instances of the Logger class represent a single logging channel. |
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Find bundle adjacency between two given tracks/bundles |
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Calculates bundle adjacency score between two given bundles |
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Clusters bundles |
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Calculates bundle shape similarity between two given bundles using bundle adjacency (BA) metric |
Module: segment.clustering
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Provides identity indexing functionality. |
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Provides functionalities for interacting with a cluster. |
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Provides functionalities for interacting with a cluster. |
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Provides functionalities for interacting with clustering outputs. |
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Provides functionalities for interacting with clustering outputs that have centroids. |
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Clusters streamlines using QuickBundles [Garyfallidis12]. |
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Clusters streamlines using QuickBundlesX. |
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Instances of the Logger class represent a single logging channel. |
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Run QuickBundlesX and then run again on the centroids of the last layer |
Module: segment.fss
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Return the nearest (smallest) for each row given an coup sparse matrix |
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Return the nearest (smallest) for each column given an coup sparse matrix |
Module: segment.mask
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Applies median filter multiple times on input data. |
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Mask vol with mask. |
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Compute the bounding box of nonzero intensity voxels in the volume. |
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Crops the input volume. |
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Simple brain extraction tool method for images from DWI data. |
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Segment the cfa inside roi using the values from threshold as bounds. |
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Cleans a segmentation of the corpus callosum so no random pixels are included. |
Module: segment.metric
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Computes the MDF (Minimum average Direct-Flip) distance [Garyfallidis12] between two streamlines. |
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Compute the average Manhattan-L1 distance (MDF without flip) |
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Compute the average Euclidean-L2 distance (MDF without flip) |
Module: segment.threshold
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Return threshold value based on Otsu's method. |
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Adjusts upper intensity boundary using rates |
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Find the upper bound for visualization of medical images |
Module: segment.tissue
¶
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This class contains the methods for tissue classification using the Markov Random Fields modeling approach |
RecoBundles
¶
- class dipy.segment.bundles.RecoBundles(streamlines, greater_than=50, less_than=1000000, cluster_map=None, clust_thr=15, nb_pts=20, rng=None, verbose=False)¶
Bases:
object
- __init__(streamlines, greater_than=50, less_than=1000000, cluster_map=None, clust_thr=15, nb_pts=20, rng=None, verbose=False)¶
Recognition of bundles
Extract bundles from a participants’ tractograms using model bundles segmented from a different subject or an atlas of bundles. See [Garyfallidis17] for the details.
Parameters¶
- streamlinesStreamlines
The tractogram in which you want to recognize bundles.
- greater_thanint, optional
Keep streamlines that have length greater than this value (default 50)
- less_thanint, optional
Keep streamlines have length less than this value (default 1000000)
- cluster_mapQB map, optional.
Provide existing clustering to start RB faster (default None).
- clust_thrfloat, optional.
Distance threshold in mm for clustering streamlines. Default: 15.
- nb_ptsint, optional.
Number of points per streamline (default 20)
- rngnp.random.Generator
If None define generator in initialization function. Default: None
- verbose: bool, optional.
If True, log information.
Notes¶
Make sure that before creating this class that the streamlines and the model bundles are roughly in the same space. Also default thresholds are assumed in RAS 1mm^3 space. You may want to adjust those if your streamlines are not in world coordinates.
References¶
[Garyfallidis17]Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.
- evaluate_results(model_bundle, pruned_streamlines, slr_select)¶
Compare the similarity between two given bundles, model bundle, and extracted bundle.
Parameters¶
model_bundle : Streamlines pruned_streamlines : Streamlines slr_select : tuple
Select the number of streamlines from model to neirborhood of model to perform the local SLR.
Returns¶
- ba_valuefloat
bundle adjacency value between model bundle and pruned bundle
- bmd_valuefloat
bundle minimum distance value between model bundle and pruned bundle
- recognize(model_bundle, model_clust_thr, reduction_thr=10, reduction_distance='mdf', slr=True, num_threads=None, slr_metric=None, slr_x0=None, slr_bounds=None, slr_select=(400, 600), slr_method='L-BFGS-B', pruning_thr=5, pruning_distance='mdf')¶
Recognize the model_bundle in self.streamlines
Parameters¶
- model_bundleStreamlines
model bundle streamlines used as a reference to extract similar streamlines from input tractogram
- model_clust_thrfloat
MDF distance threshold for the model bundles
- reduction_thrfloat, optional
Reduce search space in the target tractogram by (mm) (default 10)
- reduction_distancestring, optional
Reduction distance type can be mdf or mam (default mdf)
- slrbool, optional
Use Streamline-based Linear Registration (SLR) locally (default True)
- num_threadsint, optional
Number of threads to be used for OpenMP parallelization. If None (default) the value of OMP_NUM_THREADS environment variable is used if it is set, otherwise all available threads are used. If < 0 the maximal number of threads minus |num_threads + 1| is used (enter -1 to use as many threads as possible). 0 raises an error.
slr_metric : BundleMinDistanceMetric slr_x0 : array or int or str, optional
Transformation allowed. translation, rigid, similarity or scaling Initial parametrization for the optimization.
- If 1D array with:
a) 6 elements then only rigid registration is performed with the 3 first elements for translation and 3 for rotation. b) 7 elements also isotropic scaling is performed (similarity). c) 12 elements then translation, rotation (in degrees), scaling and shearing are performed (affine).
Here is an example of x0 with 12 elements:
x0=np.array([0, 10, 0, 40, 0, 0, 2., 1.5, 1, 0.1, -0.5, 0])
This has translation (0, 10, 0), rotation (40, 0, 0) in degrees, scaling (2., 1.5, 1) and shearing (0.1, -0.5, 0).
- If int:
- 6
x0 = np.array([0, 0, 0, 0, 0, 0])
- 7
x0 = np.array([0, 0, 0, 0, 0, 0, 1.])
- 12
x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0])
- If str:
- “rigid”
x0 = np.array([0, 0, 0, 0, 0, 0])
- “similarity”
x0 = np.array([0, 0, 0, 0, 0, 0, 1.])
- “affine”
``x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0])
(default None)
- slr_boundsarray, optional
(default None)
- slr_selecttuple, optional
Select the number of streamlines from model to neirborhood of model to perform the local SLR.
- slr_methodstring, optional
Optimization method ‘L_BFGS_B’ or ‘Powell’ optimizers can be used. (default ‘L-BFGS-B’)
- pruning_thrfloat, optional
Pruning after reducing the search space (default 5).
- pruning_distancestring, optional
Pruning distance type can be mdf or mam (default mdf)
Returns¶
- recognized_transfStreamlines
Recognized bundle in the space of the model tractogram
- recognized_labelsarray
Indices of recognized bundle in the original tractogram
References¶
[Garyfallidis17]Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.
- refine(model_bundle, pruned_streamlines, model_clust_thr, reduction_thr=14, reduction_distance='mdf', slr=True, slr_metric=None, slr_x0=None, slr_bounds=None, slr_select=(400, 600), slr_method='L-BFGS-B', pruning_thr=6, pruning_distance='mdf')¶
Refine and recognize the model_bundle in self.streamlines This method expects once pruned streamlines as input. It refines the first output of recobundle by applying second local slr (optional), and second pruning. This method is useful when we are dealing with noisy data or when we want to extract small tracks from tractograms. This time, search space is created using pruned bundle and not model bundle.
Parameters¶
- model_bundleStreamlines
model bundle streamlines used as a reference to extract similar streamlines from input tractogram
- pruned_streamlinesStreamlines
Recognized bundle from target tractogram by RecoBundles.
- model_clust_thrfloat
MDF distance threshold for the model bundles
- reduction_thrfloat
Reduce search space by (mm) (default 14)
- reduction_distancestring
Reduction distance type can be mdf or mam (default mdf)
- slrbool
Use Streamline-based Linear Registration (SLR) locally (default True)
slr_metric : BundleMinDistanceMetric slr_x0 : array or int or str
Transformation allowed. translation, rigid, similarity or scaling Initial parametrization for the optimization.
- If 1D array with:
a) 6 elements then only rigid registration is performed with the 3 first elements for translation and 3 for rotation. b) 7 elements also isotropic scaling is performed (similarity). c) 12 elements then translation, rotation (in degrees), scaling and shearing are performed (affine).
Here is an example of x0 with 12 elements:
x0=np.array([0, 10, 0, 40, 0, 0, 2., 1.5, 1, 0.1, -0.5, 0])
This has translation (0, 10, 0), rotation (40, 0, 0) in degrees, scaling (2., 1.5, 1) and shearing (0.1, -0.5, 0).
- If int:
- 6
x0 = np.array([0, 0, 0, 0, 0, 0])
- 7
x0 = np.array([0, 0, 0, 0, 0, 0, 1.])
- 12
x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0])
- If str:
- “rigid”
x0 = np.array([0, 0, 0, 0, 0, 0])
- “similarity”
x0 = np.array([0, 0, 0, 0, 0, 0, 1.])
- “affine”
``x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0])
(default None)
- slr_boundsarray
(default None)
- slr_selecttuple
Select the number of streamlines from model to neirborhood of model to perform the local SLR.
- slr_methodstring
Optimization method ‘L_BFGS_B’ or ‘Powell’ optimizers can be used. (default ‘L-BFGS-B’)
- pruning_thrfloat
Pruning after reducing the search space (default 6).
- pruning_distancestring
Pruning distance type can be mdf or mam (default mdf)
Returns¶
- recognized_transfStreamlines
Recognized bundle in the space of the model tractogram
- recognized_labelsarray
Indices of recognized bundle in the original tractogram
References¶
[Garyfallidis17]Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.
[Chandio2020]Chandio, B.Q., Risacher, S.L., Pestilli, F.,
Bullock, D., Yeh, FC., Koudoro, S., Rokem, A., Harezlak, J., and Garyfallidis, E. Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. Sci Rep 10, 17149 (2020)
check_range¶
- dipy.segment.bundles.check_range(streamline, gt, lt)¶
logger¶
- dipy.segment.bundles.logger()¶
Instances of the Logger class represent a single logging channel. A “logging channel” indicates an area of an application. Exactly how an “area” is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of “input processing” might include sub-areas “read CSV files”, “read XLS files” and “read Gnumeric files”). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be “input” for the upper level, and “input.csv”, “input.xls” and “input.gnu” for the sub-levels. There is no arbitrary limit to the depth of nesting.
bundle_adjacency¶
- dipy.segment.bundles.bundle_adjacency(dtracks0, dtracks1, threshold)¶
Find bundle adjacency between two given tracks/bundles
Parameters¶
- dtracks0Streamlines
White matter tract from one subject
- dtracks1Streamlines
White matter tract from another subject
- thresholdfloat
Threshold controls how much strictness user wants while calculating bundle adjacency between two bundles. Smaller threshold means bundles should be strictly adjacent to get higher BA score.
Returns¶
- resFloat
Bundle adjacency score between two tracts
References¶
[Garyfallidis12]Garyfallidis E. et al., QuickBundles a method for tractography simplification, Frontiers in Neuroscience, vol 6, no 175, 2012.
ba_analysis¶
- dipy.segment.bundles.ba_analysis(recognized_bundle, expert_bundle, nb_pts=20, threshold=6.0)¶
Calculates bundle adjacency score between two given bundles
Parameters¶
- recognized_bundleStreamlines
Extracted bundle from the whole brain tractogram (eg: AF_L)
- expert_bundleStreamlines
Model bundle used as reference while extracting similar type bundle from input tractogram
- nb_ptsinteger (default 20)
Discretizing streamlines to have nb_pts number of points
- thresholdfloat (default 6)
Threshold used for in computing bundle adjacency. Threshold controls how much strictness user wants while calculating bundle adjacency between two bundles. Smaller threshold means bundles should be strictly adjacent to get higher BA score.
Returns¶
Bundle adjacency score between two tracts
References¶
[Garyfallidis12]Garyfallidis E. et al., QuickBundles a method for tractography simplification, Frontiers in Neuroscience, vol 6, no 175, 2012.
cluster_bundle¶
- dipy.segment.bundles.cluster_bundle(bundle, clust_thr, rng, nb_pts=20, select_randomly=500000)¶
Clusters bundles
Parameters¶
- bundleStreamlines
White matter tract
- clust_thrfloat
clustering threshold used in quickbundlesX
- rngnp.random.Generator
numpy’s random generator for generating random values.
- nb_pts: integer (default 20)
Discretizing streamlines to have nb_points number of points
- select_randomly: integer (default 500000)
Randomly select streamlines from the input bundle
Returns¶
- centroidsStreamlines
clustered centroids of the input bundle
References¶
[Garyfallidis12]Garyfallidis E. et al., QuickBundles a method for tractography simplification, Frontiers in Neuroscience, vol 6, no 175, 2012.
bundle_shape_similarity¶
- dipy.segment.bundles.bundle_shape_similarity(bundle1, bundle2, rng, clust_thr=(5, 3, 1.5), threshold=6)¶
Calculates bundle shape similarity between two given bundles using bundle adjacency (BA) metric
Parameters¶
- bundle1Streamlines
White matter tract from one subject (eg: AF_L)
- bundle2Streamlines
White matter tract from another subject (eg: AF_L)
rng : np.random.Generator clust_thr : array-like, optional
list of clustering thresholds used in quickbundlesX
- thresholdfloat, optional
Threshold used for in computing bundle adjacency. Threshold controls how much strictness user wants while calculating shape similarity between two bundles. Smaller threshold means bundles should be strictly similar to get higher shape similarity score.
Returns¶
- ba_valueFloat
Bundle similarity score between two tracts
References¶
[Chandio2020]Chandio, B.Q., Risacher, S.L., Pestilli, F., Bullock, D.,
Yeh, FC., Koudoro, S., Rokem, A., Harezlak, J., and Garyfallidis, E. Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. Sci Rep 10, 17149 (2020)
[Garyfallidis12]Garyfallidis E. et al., QuickBundles a method for tractography simplification, Frontiers in Neuroscience, vol 6, no 175, 2012.
Identity
¶
- class dipy.segment.clustering.Identity¶
Bases:
object
Provides identity indexing functionality.
This can replace any class supporting indexing used for referencing (e.g. list, tuple). Indexing an instance of this class will return the index provided instead of the element. It does not support slicing.
- __init__()¶
Cluster
¶
- class dipy.segment.clustering.Cluster(id=0, indices=None, refdata=<dipy.segment.clustering.Identity object>)¶
Bases:
object
Provides functionalities for interacting with a cluster.
Useful container to retrieve index of elements grouped together. If a reference to the data is provided to cluster_map, elements will be returned instead of their index when possible.
Parameters¶
- cluster_mapClusterMap object
Reference to the set of clusters this cluster is being part of.
- idint
Id of this cluster in its associated cluster_map object.
- refdatalist (optional)
Actual elements that clustered indices refer to.
Notes¶
A cluster does not contain actual data but instead knows how to retrieve them using its ClusterMap object.
- __init__(id=0, indices=None, refdata=<dipy.segment.clustering.Identity object>)¶
ClusterCentroid
¶
- class dipy.segment.clustering.ClusterCentroid(centroid, id=0, indices=None, refdata=<dipy.segment.clustering.Identity object>)¶
Bases:
Cluster
Provides functionalities for interacting with a cluster.
Useful container to retrieve the indices of elements grouped together and the cluster’s centroid. If a reference to the data is provided to cluster_map, elements will be returned instead of their index when possible.
Parameters¶
- cluster_mapClusterMapCentroid object
Reference to the set of clusters this cluster is being part of.
- idint
Id of this cluster in its associated cluster_map object.
- refdatalist (optional)
Actual elements that clustered indices refer to.
Notes¶
A cluster does not contain actual data but instead knows how to retrieve them using its ClusterMapCentroid object.
- __init__(centroid, id=0, indices=None, refdata=<dipy.segment.clustering.Identity object>)¶
ClusterMap
¶
- class dipy.segment.clustering.ClusterMap(refdata=<dipy.segment.clustering.Identity object>)¶
Bases:
object
Provides functionalities for interacting with clustering outputs.
Useful container to create, remove, retrieve and filter clusters. If refdata is given, elements will be returned instead of their index when using Cluster objects.
Parameters¶
- refdatalist
Actual elements that clustered indices refer to.
- __init__(refdata=<dipy.segment.clustering.Identity object>)¶
- add_cluster(*clusters)¶
Adds one or multiple clusters to this cluster map.
Parameters¶
- *clustersCluster object, …
Cluster(s) to be added in this cluster map.
- clear()¶
Remove all clusters from this cluster map.
- property clusters¶
- clusters_sizes()¶
Gets the size of every cluster contained in this cluster map.
Returns¶
- list of int
Sizes of every cluster in this cluster map.
- get_large_clusters(min_size)¶
Gets clusters which contains at least min_size elements.
Parameters¶
- min_sizeint
Minimum number of elements a cluster needs to have to be selected.
Returns¶
- list of Cluster objects
Clusters having at least min_size elements.
- get_small_clusters(max_size)¶
Gets clusters which contains at most max_size elements.
Parameters¶
- max_sizeint
Maximum number of elements a cluster can have to be selected.
Returns¶
- list of Cluster objects
Clusters having at most max_size elements.
- property refdata¶
- remove_cluster(*clusters)¶
Remove one or multiple clusters from this cluster map.
Parameters¶
- *clustersCluster object, …
Cluster(s) to be removed from this cluster map.
- size()¶
Gets number of clusters contained in this cluster map.
ClusterMapCentroid
¶
- class dipy.segment.clustering.ClusterMapCentroid(refdata=<dipy.segment.clustering.Identity object>)¶
Bases:
ClusterMap
Provides functionalities for interacting with clustering outputs that have centroids.
Allows to retrieve easily the centroid of every cluster. Also, it is a useful container to create, remove, retrieve and filter clusters. If refdata is given, elements will be returned instead of their index when using ClusterCentroid objects.
Parameters¶
- refdatalist
Actual elements that clustered indices refer to.
- __init__(refdata=<dipy.segment.clustering.Identity object>)¶
- property centroids¶
Clustering
¶
- class dipy.segment.clustering.Clustering¶
Bases:
object
- __init__()¶
- abstract cluster(data, ordering=None)¶
Clusters data.
Subclasses will perform their clustering algorithm here.
Parameters¶
- datalist of N-dimensional arrays
Each array represents a data point.
- orderingiterable of indices, optional
Specifies the order in which data points will be clustered.
Returns¶
- ClusterMap object
Result of the clustering.
QuickBundles
¶
- class dipy.segment.clustering.QuickBundles(threshold, metric='MDF_12points', max_nb_clusters=2147483647)¶
Bases:
Clustering
Clusters streamlines using QuickBundles [Garyfallidis12]. Given a list of streamlines, the QuickBundles algorithm sequentially assigns each streamline to its closest bundle in \(\mathcal{O}(Nk)\) where \(N\) is the number of streamlines and \(k\) is the final number of bundles. If for a given streamline its closest bundle is farther than threshold, a new bundle is created and the streamline is assigned to it except if the number of bundles has already exceeded max_nb_clusters. Parameters ———- threshold : float The maximum distance from a bundle for a streamline to be still considered as part of it. metric : str or Metric object (optional) The distance metric to use when comparing two streamlines. By default, the Minimum average Direct-Flip (MDF) distance [Garyfallidis12] is used and streamlines are automatically resampled so they have 12 points. max_nb_clusters : int Limits the creation of bundles. Examples ——– >>> from dipy.segment.clustering import QuickBundles >>> from dipy.data import get_fnames >>> from dipy.io.streamline import load_tractogram >>> from dipy.tracking.streamline import Streamlines >>> fname = get_fnames(‘fornix’) >>> fornix = load_tractogram(fname, ‘same’, … bbox_valid_check=False).streamlines >>> streamlines = Streamlines(fornix) >>> # Segment fornix with a threshold of 10mm and streamlines resampled >>> # to 12 points. >>> qb = QuickBundles(threshold=10.) >>> clusters = qb.cluster(streamlines) >>> len(clusters) 4 >>> list(map(len, clusters)) [61, 191, 47, 1] >>> # Resampling streamlines differently is done explicitly as follows. >>> # Note this has an impact on the speed and the accuracy (tradeoff). >>> from dipy.segment.featurespeed import ResampleFeature >>> from dipy.segment.metricspeed import AveragePointwiseEuclideanMetric >>> feature = ResampleFeature(nb_points=2) >>> metric = AveragePointwiseEuclideanMetric(feature) >>> qb = QuickBundles(threshold=10., metric=metric) >>> clusters = qb.cluster(streamlines) >>> len(clusters) 4 >>> list(map(len, clusters)) [58, 142, 72, 28] References ———- .. [Garyfallidis12] Garyfallidis E. et al., QuickBundles a method for tractography simplification, Frontiers in Neuroscience, vol 6, no 175, 2012.
- __init__(threshold, metric='MDF_12points', max_nb_clusters=2147483647)¶
- cluster(streamlines, ordering=None)¶
Clusters streamlines into bundles.
Performs quickbundles algorithm using predefined metric and threshold.
Parameters¶
- streamlineslist of 2D arrays
Each 2D array represents a sequence of 3D points (points, 3).
- orderingiterable of indices
Specifies the order in which data points will be clustered.
Returns¶
- ClusterMapCentroid object
Result of the clustering.
QuickBundlesX
¶
- class dipy.segment.clustering.QuickBundlesX(thresholds, metric='MDF_12points')¶
Bases:
Clustering
Clusters streamlines using QuickBundlesX.
Parameters¶
- thresholdslist of float
Thresholds to use for each clustering layer. A threshold represents the maximum distance from a cluster for a streamline to be still considered as part of it.
- metricstr or Metric object (optional)
The distance metric to use when comparing two streamlines. By default, the Minimum average Direct-Flip (MDF) distance [Garyfallidis12] is used and streamlines are automatically resampled so they have 12 points.
References¶
[Garyfallidis12]Garyfallidis E. et al., QuickBundles a method for tractography simplification, Frontiers in Neuroscience, vol 6, no 175, 2012.
[Garyfallidis16]Garyfallidis E. et al. QuickBundlesX: Sequential clustering of millions of streamlines in multiple levels of detail at record execution time. Proceedings of the, International Society of Magnetic Resonance in Medicine (ISMRM). Singapore, 4187, 2016.
- __init__(thresholds, metric='MDF_12points')¶
- cluster(streamlines, ordering=None)¶
Clusters streamlines into bundles.
Performs QuickbundleX using a predefined metric and thresholds.
Parameters¶
- streamlineslist of 2D arrays
Each 2D array represents a sequence of 3D points (points, 3).
- orderingiterable of indices
Specifies the order in which data points will be clustered.
Returns¶
- TreeClusterMap object
Result of the clustering.
TreeCluster
¶
TreeClusterMap
¶
logger¶
- dipy.segment.clustering.logger()¶
Instances of the Logger class represent a single logging channel. A “logging channel” indicates an area of an application. Exactly how an “area” is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of “input processing” might include sub-areas “read CSV files”, “read XLS files” and “read Gnumeric files”). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be “input” for the upper level, and “input.csv”, “input.xls” and “input.gnu” for the sub-levels. There is no arbitrary limit to the depth of nesting.
qbx_and_merge¶
- dipy.segment.clustering.qbx_and_merge(streamlines, thresholds, nb_pts=20, select_randomly=None, rng=None, verbose=False)¶
Run QuickBundlesX and then run again on the centroids of the last layer
Running again QuickBundles at a layer has the effect of merging some of the clusters that may be originally divided because of branching. This function help obtain a result at a QuickBundles quality but with QuickBundlesX speed. The merging phase has low cost because it is applied only on the centroids rather than the entire dataset.
Parameters¶
streamlines : Streamlines thresholds : sequence
List of distance thresholds for QuickBundlesX.
- nb_ptsint
Number of points for discretizing each streamline
- select_randomlyint
Randomly select a specific number of streamlines. If None all the streamlines are used.
- rngnumpy.random.Generator
If None then generator is initialized internally.
- verbosebool, optional.
If True, log information. Default False.
Returns¶
- clustersobj
Contains the clusters of the last layer of QuickBundlesX after merging.
References¶
[Garyfallidis12]Garyfallidis E. et al., QuickBundles a method for tractography simplification, Frontiers in Neuroscience, vol 6, no 175, 2012.
[Garyfallidis16]Garyfallidis E. et al. QuickBundlesX: Sequential clustering of millions of streamlines in multiple levels of detail at record execution time. Proceedings of the, International Society of Magnetic Resonance in Medicine (ISMRM). Singapore, 4187, 2016.
FastStreamlineSearch
¶
- class dipy.segment.fss.FastStreamlineSearch(ref_streamlines, max_radius, nb_mpts=4, bin_size=20.0, resampling=24, bidirectional=True)¶
Bases:
object
- __init__(ref_streamlines, max_radius, nb_mpts=4, bin_size=20.0, resampling=24, bidirectional=True)¶
Fast Streamline Search (FFS)
Generate the Binned K-D Tree structure with reference streamlines, using streamlines barycenter and mean-points. See [StOnge2022] for further details.
Parameters¶
- ref_streamlinesStreamlines
Streamlines (ref) to generate the tree structure.
- max_radiusfloat
The maximum radius (distance) for subsequent streamline search. Used to compute the overlap in-between bins.
- nb_mptsint, optional
Number of means points to improve computation speed. (this only changes computation time)
- bin_sizefloat, optional
The bin size to separate streamlines in groups. (this only changes computation time)
- resamplingint, optional
Number of points used to reshape each streamline.
- bidirectionalbool, optional
Compute the smallest distance with and without flip.
Notes¶
Make sure that streamlines are aligned in the same space. Preferably in millimeter space (voxmm or rasmm).
References¶
[StOnge2022]St-Onge E. et al. Fast Streamline Search: An Exact Technique for Diffusion MRI Tractography. Neuroinformatics, 2022.
- radius_search(streamlines, radius, use_negative=True)¶
Radius Search using Fast Streamline Search
For each given streamlines, return all reference streamlines within the given radius. See [StOnge2022] for further details.
Parameters¶
- streamlinesStreamlines
Streamlines to generate the tree structure.
- radiusfloat
Search radius (with MDF / average L2 distance) must be smaller than max_radius when FFS was initialized.
- use_negativebool, optional
When used with bidirectional, negative values are returned for reversed order neighbors.
Returns¶
- resscipy COOrdinates sparse matrix (nb_slines x nb_slines_ref)
Adjacency matrix containing all neighbors within the given radius
Notes¶
Given streamlines should be already aligned with ref streamlines. Preferably in millimeter space (voxmm or rasmm).
References¶
[StOnge2022]St-Onge E. et al. Fast Streamline Search: An Exact Technique for Diffusion MRI Tractography. Neuroinformatics, 2022.
nearest_from_matrix_row¶
- dipy.segment.fss.nearest_from_matrix_row(coo_matrix)¶
Return the nearest (smallest) for each row given an coup sparse matrix
Parameters¶
- coo_matrixscipy COOrdinates sparse matrix (nb_slines x nb_slines_ref)
Adjacency matrix containing all neighbors within the given radius
Returns¶
- non_zero_idsnumpy array (nb_non_empty_row x 1)
Indices of each non-empty slines (row)
- nearest_idnumpy array (nb_non_empty_row x 1)
Indices of the nearest reference match (column)
- nearest_distnumpy array (nb_non_empty_row x 1)
Distance for each nearest match
nearest_from_matrix_col¶
- dipy.segment.fss.nearest_from_matrix_col(coo_matrix)¶
Return the nearest (smallest) for each column given an coup sparse matrix
Parameters¶
- coo_matrixscipy COOrdinates sparse matrix (nb_slines x nb_slines_ref)
Adjacency matrix containing all neighbors within the given radius
Returns¶
- non_zero_idsnumpy array (nb_non_empty_col x 1)
Indices of each non-empty reference (column)
- nearest_idnumpy array (nb_non_empty_col x 1)
Indices of the nearest slines match (row)
- nearest_distnumpy array (nb_non_empty_col x 1)
Distance for each nearest match
multi_median¶
- dipy.segment.mask.multi_median(data, median_radius, numpass)¶
Applies median filter multiple times on input data.
Parameters¶
- datandarray
The input volume to apply filter on.
- median_radiusint
Radius (in voxels) of the applied median filter
- numpass: int
Number of pass of the median filter
Returns¶
- datandarray
Filtered input volume.
applymask¶
- dipy.segment.mask.applymask(vol, mask)¶
Mask vol with mask. Parameters ———- vol : ndarray Array with \(V\) dimensions mask : ndarray Binary mask. Has \(M\) dimensions where \(M <= V\). When \(M < V\), we append \(V - M\) dimensions with axis length 1 to mask so that mask will broadcast against vol. In the typical case vol can be 4D, mask can be 3D, and we append a 1 to the mask shape which (via numpy broadcasting) has the effect of applying the 3D mask to each 3D slice in vol (
vol[..., 0]
tovol[..., -1
). Returns ——- masked_vol : ndarray vol multiplied by mask where mask may have been extended to match extra dimensions in vol
bounding_box¶
crop¶
median_otsu¶
- dipy.segment.mask.median_otsu(input_volume, vol_idx=None, median_radius=4, numpass=4, autocrop=False, dilate=None)¶
Simple brain extraction tool method for images from DWI data.
It uses a median filter smoothing of the input_volumes vol_idx and an automatic histogram Otsu thresholding technique, hence the name median_otsu.
This function is inspired from Mrtrix’s bet which has default values
median_radius=3
,numpass=2
. However, from tests on multiple 1.5T and 3T data from GE, Philips, Siemens, the most robust choice ismedian_radius=4
,numpass=4
.Parameters¶
- input_volumendarray
3D or 4D array of the brain volume.
- vol_idxNone or array, optional.
1D array representing indices of
axis=3
of a 4D input_volume. None is only an acceptable input ifinput_volume
is 3D.- median_radiusint
Radius (in voxels) of the applied median filter (default: 4).
- numpass: int
Number of pass of the median filter (default: 4).
- autocrop: bool, optional
if True, the masked input_volume will also be cropped using the bounding box defined by the masked data. Should be on if DWI is upsampled to 1x1x1 resolution. (default: False).
- dilateNone or int, optional
number of iterations for binary dilation
Returns¶
- maskedvolumendarray
Masked input_volume
- mask3D ndarray
The binary brain mask
Notes¶
Copyright (C) 2011, the scikit-image team All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the name of skimage nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS’’ AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
segment_from_cfa¶
- dipy.segment.mask.segment_from_cfa(tensor_fit, roi, threshold, return_cfa=False)¶
Segment the cfa inside roi using the values from threshold as bounds.
Parameters¶
- tensor_fitTensorFit object
TensorFit object
- roindarray
A binary mask, which contains the bounding box for the segmentation.
- thresholdarray-like
An iterable that defines the min and max values to use for the thresholding. The values are specified as (R_min, R_max, G_min, G_max, B_min, B_max)
- return_cfabool, optional
If True, the cfa is also returned.
Returns¶
- maskndarray
Binary mask of the segmentation.
- cfandarray, optional
Array with shape = (…, 3), where … is the shape of tensor_fit. The color fractional anisotropy, ordered as a nd array with the last dimension of size 3 for the R, G and B channels.
clean_cc_mask¶
mdf¶
- dipy.segment.metric.mdf(s1, s2)¶
Computes the MDF (Minimum average Direct-Flip) distance [Garyfallidis12] between two streamlines.
Streamlines must have the same number of points.
Parameters¶
- s12D array
A streamline (sequence of N-dimensional points).
- s22D array
A streamline (sequence of N-dimensional points).
Returns¶
- double
Distance between two streamlines.
References¶
[Garyfallidis12]Garyfallidis E. et al., QuickBundles a method for tractography simplification, Frontiers in Neuroscience, vol 6, no 175, 2012.
mean_manhattan_distance¶
- dipy.segment.metric.mean_manhattan_distance(a, b)¶
Compute the average Manhattan-L1 distance (MDF without flip)
Arrays are representing a single streamline or a list of streamlines that have the same number of N-dimensional points (two last axis).
Parameters¶
- a2D or 3D array
A streamline or concatenated streamlines (array of S streamlines by P points in N dimension).
- b2D or 3D array
A streamline or concatenated streamlines (array of S streamlines by P points in N dimension).
Returns¶
- 1D array
Distance between each S streamlines
mean_euclidean_distance¶
- dipy.segment.metric.mean_euclidean_distance(a, b)¶
Compute the average Euclidean-L2 distance (MDF without flip)
Arrays are representing a single streamline or a list of streamlines that have the same number of N-dimensional points (two last axis).
Parameters¶
- a2D or 3D array
A streamline or concatenated streamlines (array of S streamlines by P points in N dimension).
- b2D or 3D array
A streamline or concatenated streamlines (array of S streamlines by P points in N dimension).
Returns¶
- 1D array
Distance between each S streamlines
otsu¶
- dipy.segment.threshold.otsu(image, nbins=256)¶
Return threshold value based on Otsu’s method. Copied from scikit-image to remove dependency.
Parameters¶
- imagearray
Input image.
- nbinsint
Number of bins used to calculate histogram. This value is ignored for integer arrays.
Returns¶
- thresholdfloat
Threshold value.
upper_bound_by_rate¶
- dipy.segment.threshold.upper_bound_by_rate(data, rate=0.05)¶
Adjusts upper intensity boundary using rates
It calculates the image intensity histogram, and based on the rate value it decide what is the upperbound value for intensity normalization, usually lower bound is 0. The rate is the ratio between the amount of pixels in every bins and the bins with highest pixel amount
Parameters¶
- datafloat
Input intensity value data
- ratefloat
representing the threshold whether a specific histogram bin that should be count in the normalization range
Returns¶
high : float
the upper_bound value for normalization
upper_bound_by_percent¶
- dipy.segment.threshold.upper_bound_by_percent(data, percent=1)¶
Find the upper bound for visualization of medical images
Calculate the histogram of the image and go right to left until you find the bound that contains more than a percentage of the image.
Parameters¶
data : ndarray percent : float
Returns¶
upper_bound : float
TissueClassifierHMRF
¶
- class dipy.segment.tissue.TissueClassifierHMRF(save_history=False, verbose=True)¶
Bases:
object
This class contains the methods for tissue classification using the Markov Random Fields modeling approach
- __init__(save_history=False, verbose=True)¶
- classify(image, nclasses, beta, tolerance=None, max_iter=None)¶
This method uses the Maximum a posteriori - Markov Random Field approach for segmentation by using the Iterative Conditional Modes and Expectation Maximization to estimate the parameters.
Parameters¶
- imagendarray,
3D structural image.
- nclassesint,
number of desired classes.
- betafloat,
smoothing parameter, the higher this number the smoother the output will be.
- tolerance: float,
value that defines the percentage of change tolerated to prevent the ICM loop to stop. Default is 1e-05.
- max_iterfloat,
fixed number of desired iterations. Default is 100. If the user only specifies this parameter, the tolerance value will not be considered. If none of these two parameters
Returns¶
- initial_segmentationndarray,
3D segmented image with all tissue types specified in nclasses.
- final_segmentationndarray,
3D final refined segmentation containing all tissue types.
- PVEndarray,
3D probability map of each tissue type.