Merge pull request #39 from jnolan14/master

Created guide for labels

Author: jnolan14

docs/guide/labels.md

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+Labels
+======
+
+Surfa provides utilities for working with image label mappings. Label mappings
+store information to correlate integer values in the image with a label name and
+RGBA color representation in an image viewer. The classes that surfa provides
+for working with labels are primarily built on top of dictionaries.
+
+**LabelLookup** objects hold the label information in an ordered dictionary,
+where each key is the int corresponding to the label value in the image, and each value is a `LabelElement`.
+
+**LabelElement** objects are mutable objects with two fields, `name` and `color`
+- `name` is a string representing the label name
+- `color` is a 4 element list of integer values representing the RGBA values
+
+## Label Basics
+
+`LabelLookup` objects can be instantiated by loading a FreeSurfer color lookup
+table file or defined manually by the user. There are also some utilities for
+creating `LabelLookup` objects representing label mapping commonly used in
+FreeSurfer.
+
+```python
+>>> import surfa as sf
+
+# instantiate `LabelLookup` from a file
+>>> labels_from_file = sf.load_label_lookup('/usr/local/freesurfer/7.4.1/luts/FreeSurferColorLUT.txt')
+
+# instantiate from a commonly used label mapping (Destrieux cortical atlas)
+>>> destrieux_labels = sf.freesurfer.destrieux()
+
+# instantiate manually
+>>> manual_labels = sf.LabelLookup()
+>>> manual_labels[0] = ('Unknown', [0,0,0,0])
+>>> manual_labels[24] = ('CSF', [255,255,255,0])
+>>> manual_labels[100] = ('CSF-Ventricle', [128,128,128,0])
+
+>>> manual_labels[24].name
+'CSF'
+>>> manual_labels[24].color
+array([255., 255., 255.,   0.])
+```
+
+Labels can be accessed by indexing the label value in the `LabelLookup`. The
+index of a label can be searched for by passing a string to the `search` method.
+By default the search is greedy and not case sensitive, this can be changed to
+an exact match via the `exact` arg.
+
+```python
+# greedy search
+>>> manual_labels.search('CSF')
+[24, 100]
+
+# exact search
+>>> manual_labels.search('CSF',exact=True)
+24
+```
+
+Subsets of a `LabelLookup` can be extracted into `LabelLookup` objects if
+working with the full label mapping contains lots of unused labels.
+
+```python
+>>> import numpy as np
+
+# load an aseg, find unique labels
+>>> aseg = sf.load_volume('aseg.mgz')
+>>> aseg_label_indices = np.unique(aseg.data)
+>>> aseg_label_indices
+array([  0,   2,   3,   4,   5,   7,   8,  10,  11,  12,  13,  14,  15,
+        16,  17,  18,  24,  26,  28,  30,  31,  41,  42,  43,  44,  46,
+        47,  49,  50,  51,  52,  53,  54,  58,  60,  62,  63,  72,  77,
+        85, 251, 252, 253, 254, 255], dtype=int32)
+
+>>> aseg_label_lookup = sf.freesurfer.labels().extract(aseg_label_indices)
+
+# sanity check
+>>> np.array([*aseg_label_lookup.keys()], dtype='int32') == aseg_label_indices
+array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
+        True,  True,  True,  True,  True,  True,  True,  True,  True,
+        True,  True,  True,  True,  True,  True,  True,  True,  True,
+        True,  True,  True,  True,  True,  True,  True,  True,  True,
+        True,  True,  True,  True,  True,  True,  True,  True,  True])
+
+>>> aseg_label_lookup
+0   Unknown                          0,    0,    0,  1.00
+2   Left-Cerebral-White-Matter     245,  245,  245,  1.00
+3   Left-Cerebral-Cortex           205,   62,   78,  1.00
+4   Left-Lateral-Ventricle         120,   18,  134,  1.00
+5   Left-Inf-Lat-Vent              196,   58,  250,  1.00
+...
+85  Optic-Chiasm                   234,  169,   30,  1.00
+251 CC_Posterior                     0,    0,   64,  1.00
+252 CC_Mid_Posterior                 0,    0,  112,  1.00
+253 CC_Central                       0,    0,  160,  1.00
+254 CC_Mid_Anterior                  0,    0,  208,  1.00
+255 CC_Anterior                      0,    0,  255,  1.00
+```
+
+## Label Recoding
+
+Often times, label indices do not match up between label lookups, especially in
+the case we are recoding labels to merge classes. The `LabelRecoder` class
+provides the functionality to do this.
+
+The `LabelRecoder` class has two fields:
+
+- `mapping` a dictionary where the keys are the indices of the labels in the
+current `LabelLookup`, and the values are the index to recode it to
+- `target_labels` a `LabelLookup` that defines the target label names/colors. 
+Can be `None`
+
+> [!NOTE]
+> The `target_labels` field will be assigned to the `labels` field of a label
+> volume that is recoded using a `LabelRecoder`. If this field is `None`, then
+> the recoded segmentation volume will have no label mapping, however, that can
+> be assigned to after the recoded segmentation volume is created
+
+Some predefined `LabelLookup` and `LabelRecoder` objects can be found under
+`surfa.freesurfer`
+
+```python
+# recode the aseg volume labels to the tissue type
+>>> recoder = sf.freesurfer.tissue_type_recoder()
+>>> seg_tissue_types = sf.labels.recode(aseg,recoder)
+>>> seg_tissue_types.labels
+0 Unknown                    0,    0,    0,  1.00
+1 Cortex                   205,   62,   78,  1.00
+2 Subcortical-Gray-Matter  230,  148,   34,  1.00
+3 White-Matter             245,  245,  245,  1.00
+4 CSF                      120,   18,  134,  1.00
+5 Head                     150,  150,  200,  1.00
+6 Lesion                   255,  165,    0,  1.00
+```
+
+## Label Metrics
+
+There are also some utilities for calculating the dice score and jaccard
+coefficients for two sets of label volumes. By default both will compute the
+metric for all unique labels found in the label volumes, or the user can specify
+a list of label indices to compute the metrics for.
+
+```python
+# load another segmentation to compare to the aseg
+>>> seg = sf.load_volume('aseg_2.mgz')
+
+>>> target_labels = [2,3,4,255]
+>>> sf.labels.dice(aseg,seg, target_labels)
+{2: 0.5299304443534139, 3: 0.42780355492851396, 4: 0.29737467850730337, 255: 0.21085134539038378}
+>>> sf.labels.jaccard(aseg,seg, target_labels)
+{2: 0.3604798441801161, 3: 0.2721056622851365, 4: 0.1746565581713504, 255: 0.1178500986193294}
+```