.. AUTO-GENERATED FILE -- DO NOT EDIT!

.. _example_tracking_tissue_classifier:


=================================================
Using Various Tissue Classifiers for Tractography
=================================================
The tissue classifier determines if the tracking stops or continues at each
tracking position. The tracking stops when it reaches an ending region
(e.g. low FA, gray matter or corticospinal fluid regions) or exits the image
boundaries. The tracking also stops if the direction getter has no direction
to follow.

Each tissue classifier determines if the stopping is 'valid' or
'invalid'. A streamline is 'valid' when the tissue classifier determines if
the streamline stops in a position classified as 'ENDPOINT' or 'OUTSIDEIMAGE'.
A streamline is 'invalid' when it stops in a position classified as
'TRACKPOINT' or 'INVALIDPOINT'. These conditions are described below. The
'LocalTracking' generator can be set to output all generated streamlines
or only the 'valid' ones.

This example is an extension of the
:ref:`example_deterministic_fiber_tracking` example. We begin by loading the
data, fitting a Constrained Spherical Deconvolution (CSD) reconstruction
model and creating the maximum deterministic direction getter.

::
  
  import numpy as np
  
  from dipy.data import read_stanford_labels, default_sphere
  from dipy.direction import DeterministicMaximumDirectionGetter
  from dipy.io.trackvis import save_trk
  from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel,
                                     auto_response)
  from dipy.tracking.local import LocalTracking
  from dipy.tracking import utils
  from dipy.viz import fvtk
  from dipy.viz.colormap import line_colors
  
  ren = fvtk.ren()
  
  hardi_img, gtab, labels_img = read_stanford_labels()
  data = hardi_img.get_data()
  labels = labels_img.get_data()
  affine = hardi_img.get_affine()
  
  seed_mask = labels == 2
  white_matter = (labels == 1) | (labels == 2)
  seeds = utils.seeds_from_mask(seed_mask, density=2, affine=affine)
  
  response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7)
  csd_model = ConstrainedSphericalDeconvModel(gtab, response)
  csd_fit = csd_model.fit(data, mask=white_matter)
  
  dg = DeterministicMaximumDirectionGetter.from_shcoeff(csd_fit.shm_coeff,
                                                        max_angle=30.,
                                                        sphere=default_sphere)
  

Threshold Tissue Classifier
---------------------------
A scalar map can be used to define where the tracking stops. The threshold
tissue classifier uses a scalar map to stop the tracking whenever the
interpolated scalar value is lower than a fixed threshold. Here, we show
an example using the fractional anisotropy (FA) map of the DTI model.
The threshold tissue classifier uses a trilinear interpolation at the
tracking position.

**Parameters**

- metric_map: numpy array [:, :, :]
- threshold: float

**Stopping criterion**

- 'ENDPOINT': metric_map < threshold,
- 'OUTSIDEIMAGE': tracking point outside of metric_map,
- 'TRACKPOINT': stop because no direction is available,
- 'INVALIDPOINT': N/A.

::
  
  import matplotlib.pyplot as plt
  import dipy.reconst.dti as dti
  from dipy.reconst.dti import fractional_anisotropy
  from dipy.tracking.local import ThresholdTissueClassifier
  
  tensor_model = dti.TensorModel(gtab)
  tenfit = tensor_model.fit(data, mask=labels > 0)
  FA = fractional_anisotropy(tenfit.evals)
  
  threshold_classifier = ThresholdTissueClassifier(FA, .2)
  
  fig = plt.figure()
  mask_fa = FA.copy()
  mask_fa[mask_fa < 0.2] = 0
  plt.xticks([])
  plt.yticks([])
  plt.imshow(mask_fa[:, :, data.shape[2] / 2].T, cmap='gray', origin='lower',
             interpolation='nearest')
  fig.tight_layout()
  fig.savefig('threshold_fa.png')
  

.. figure:: threshold_fa.png
 :align: center

 **Thresholded fractional anisotropy map.**

::
  
  all_streamlines_threshold_classifier = LocalTracking(dg,
                                                       threshold_classifier,
                                                       seeds,
                                                       affine,
                                                       step_size=.5,
                                                       return_all=True)
  
  save_trk("deterministic_threshold_classifier_all.trk",
           all_streamlines_threshold_classifier,
           affine,
           labels.shape)
  
  streamlines = [sl for sl in all_streamlines_threshold_classifier]
  
  fvtk.clear(ren)
  fvtk.add(ren, fvtk.line(streamlines, line_colors(streamlines)))
  fvtk.record(ren, out_path='all_streamlines_threshold_classifier.png',
              size=(600, 600))
  

.. figure:: all_streamlines_threshold_classifier.png
 :align: center

 **Deterministic tractography using a thresholded fractional anisotropy.**

::
  
  

Binary Tissue Classifier
------------------------
A binary mask can be used to define where the tracking stops. The binary
tissue classifier stops the tracking whenever the tracking position is outside
the mask. Here, we show how to obtain the binary tissue classifier from
the white matter mask defined above. The binary tissue classifier uses a
nearest-neighborhood interpolation at the tracking position.

**Parameters**

- mask: numpy array [:, :, :]

**Stopping criterion**

- 'ENDPOINT': mask = 0
- 'OUTSIDEIMAGE': tracking point outside of mask
- 'TRACKPOINT': no direction is available
- 'INVALIDPOINT': N/A

::
  
  from dipy.tracking.local import BinaryTissueClassifier
  
  binary_classifier = BinaryTissueClassifier(white_matter)
  
  fig = plt.figure()
  plt.xticks([])
  plt.yticks([])
  fig.tight_layout()
  plt.imshow(white_matter[:, :, data.shape[2] / 2].T, cmap='gray', origin='lower',
             interpolation='nearest')
  fig.savefig('white_matter_mask.png')
  

.. figure:: white_matter_mask.png
 :align: center

 **White matter binary mask.**

::
  
  all_streamlines_binary_classifier = LocalTracking(dg,
                                                    binary_classifier,
                                                    seeds,
                                                    affine,
                                                    step_size=.5,
                                                    return_all=True)
  
  save_trk("deterministic_binary_classifier_all.trk",
           all_streamlines_binary_classifier,
           affine,
           labels.shape)
  
  streamlines = [sl for sl in all_streamlines_binary_classifier]
  fvtk.clear(ren)
  fvtk.add(ren, fvtk.line(streamlines, line_colors(streamlines)))
  fvtk.record(ren, out_path='all_streamlines_binary_classifier.png',
              size=(600, 600))
  

.. figure:: all_streamlines_binary_classifier.png
 :align: center

 **Deterministic tractography using a binary white matter mask.**

::
  

ACT Tissue Classifier
---------------------
Anatomically-constrained tractography (ACT) [Smith2012]_ uses information from
anatomical images to determine when the tractography stops. The 'include_map'
defines when the streamline reached a 'valid' stopping region (e.g. gray
matter partial volume estimation (PVE) map) and the 'exclude_map' defines when
the streamline reached an 'invalid' stopping region (e.g. corticospinal fluid
PVE map). The background of the anatomical image should be added to the
'include_map' to keep streamlines exiting the brain (e.g. through the
brain stem). The ACT tissue classifier uses a trilinear interpolation
at the tracking position.

**Parameters**

- include_map: numpy array [:, :, :],
- exclude_map: numpy array [:, :, :],

**Stopping criterion**

- 'ENDPOINT': include_map > 0.5,
- 'OUTSIDEIMAGE': tracking point outside of include_map or exclude_map,
- 'TRACKPOINT': no direction is available,
- 'INVALIDPOINT': exclude_map > 0.5.

::
  
  from dipy.data import read_stanford_pve_maps
  from dipy.tracking.local import ActTissueClassifier
  
  img_pve_csf, img_pve_gm, img_pve_wm = read_stanford_pve_maps()
  
  background = np.ones(img_pve_gm.shape)
  background[(img_pve_gm.get_data() +
              img_pve_wm.get_data() +
              img_pve_csf.get_data()) > 0] = 0
  
  include_map = img_pve_gm.get_data()
  include_map[background > 0] = 1
  exclude_map = img_pve_csf.get_data()
  
  act_classifier = ActTissueClassifier(include_map, exclude_map)
  
  fig = plt.figure()
  plt.subplot(121)
  plt.xticks([])
  plt.yticks([])
  plt.imshow(include_map[:, :, data.shape[2] / 2].T, cmap='gray', origin='lower',
             interpolation='nearest')
  plt.subplot(122)
  plt.xticks([])
  plt.yticks([])
  plt.imshow(exclude_map[:, :, data.shape[2] / 2].T, cmap='gray', origin='lower',
             interpolation='nearest')
  fig.tight_layout()
  fig.savefig('act_maps.png')
  

.. figure:: act_maps.png
 :align: center

 **Include (left) and exclude (right) maps for ACT.**

::
  
  all_streamlines_act_classifier = LocalTracking(dg,
                                                 act_classifier,
                                                 seeds,
                                                 affine,
                                                 step_size=.5,
                                                 return_all=True)
  
  save_trk("deterministic_act_classifier_all.trk",
           all_streamlines_act_classifier,
           affine,
           labels.shape)
  
  streamlines = [sl for sl in all_streamlines_act_classifier]
  
  fvtk.clear(ren)
  fvtk.add(ren, fvtk.line(streamlines, line_colors(streamlines)))
  fvtk.record(ren, out_path='all_streamlines_act_classifier.png',
              size=(600, 600))
  

.. figure:: all_streamlines_act_classifier.png
 :align: center

 **Deterministic tractography using ACT stopping criterion.**

::
  
  valid_streamlines_act_classifier = LocalTracking(dg,
                                                   act_classifier,
                                                   seeds,
                                                   affine,
                                                   step_size=.5,
                                                   return_all=False)
  
  save_trk("deterministic_act_classifier_valid.trk",
           valid_streamlines_act_classifier,
           affine,
           labels.shape)
  
  streamlines = [sl for sl in valid_streamlines_act_classifier]
  
  fvtk.clear(ren)
  fvtk.add(ren, fvtk.line(streamlines, line_colors(streamlines)))
  fvtk.record(ren, out_path='valid_streamlines_act_classifier.png',
              size=(600, 600))
  

.. figure:: valid_streamlines_act_classifier.png
 :align: center

 **Deterministic tractography using a anatomically-constrained tractography
 stopping criterion. Streamlines ending in gray matter region only.**

::
  

The threshold and binary tissue classifiers use respectively a scalar map and a
binary mask to stop the tracking. The ACT tissue classifier use partial volume
fraction (PVE) maps from an anatomical image to stop the tracking. Additionally,
the ACT tissue classifier determines if the tracking stopped in expected regions
(e.g. gray matter) and allows the user to get only streamlines stopping in those
regions.

Notes
------
Currently in ACT the proposed method that cuts streamlines going through
subcortical gray matter regions is not implemented. The backtracking technique
for streamlines reaching INVALIDPOINT is not implemented either.


References
----------

.. [Smith2012] Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A.
    Anatomically-constrained tractography: Improved diffusion MRI
    streamlines tractography through effective use of anatomical
    information. NeuroImage, 63(3), 1924-1938, 2012.

        
.. admonition:: Example source code

   You can download :download:`the full source code of this example <./tracking_tissue_classifier.py>`.
   This same script is also included in the dipy source distribution under the
   :file:`doc/examples/` directory.

