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<article id="content">
<header>
<h1 class="title">Module <code>pymskt.image.cartilage_processing</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">import SimpleITK as sitk
import numpy as np
from scipy import ndimage as ndi
def CofM(array):
'''
Get center of mass for a row of a binary 2D image.
Parameters
----------
array : 1D array
Individual row of a 2D image.
Returns
-------
centerPixels :
Average location of 1s in the row
Notes
-----
Calculates the average location of cartilage for the row of image being analyzed.
Returns 0 if there are no pixels
'''
pixels = np.where(array==1)
centerPixels = np.mean(pixels)
nans = np.isnan(centerPixels)
if nans == True:
centerPixels = 0
return(centerPixels)
def get_y_CofM(flattenedSeg):
'''
Get CofM of femoral cartilage for each row of the flattened segmentation.
Parameters
----------
flattenedSeg : 2D array
Axial flattened, and filled in femoral cartilage segmentation.
Returns
-------
yCofM :
Find the CofM for each row of the image.
Notes
-----
Get the x/y coordinates for the CofM for each row of the flattened segmentation.
'''
locationFemur = np.where(flattenedSeg==1)
yCofM = np.zeros((flattenedSeg.shape[0], 2), dtype=int)
# only calculate for rows with cartilage.
minRow = np.min(locationFemur[0])
maxRow = np.max(locationFemur[0])
# iterate over rows of image, get CofM, store CofM for row.
for x in range(minRow, maxRow):
yCofM[x, 0] = x #store the x-coordinate (row) we calcualted CofM for.
yCofM[x, 1] = int(CofM(flattenedSeg[x, :])) # store the CofM value (make it an integer for indexing)
yCofM = yCofM[minRow+10:maxRow-10,:] # remove 10 most medial and most lateral pixels of femoral cartilage.
return(yCofM)
def absolute_CofM(flattenedSeg):
'''
Get absolute CofM of all the femoral cartilage pixels
Parameters
----------
flattenedSeg : 2D array
Axial flattened, and filled in femoral cartilage segmentation.
Returns
-------
centerX :
The CofM in the X direction for the segmentation
centerY :
The CofM in the Y direction for the segmentation
Notes
-----
Get the x/y coordinates for the CofM for the whole flattened segmentation
'''
femurPoints = np.where(flattenedSeg==1)
centerX = np.mean(femurPoints[0])
centerY = np.mean(femurPoints[1])
return(centerX, centerY)
def findNotch(flattenedSeg, trochleaPositionX=1000):
'''
Get the X Y position of the trochlear notch - where medial/lateral sides of the femur meet.
Parameters
----------
flattenedSeg : 2D array
Axial flattened, and filled in femoral cartilage segmentation.
Returns
-------
trochleaPositionY :
Y position of trochlear notch
trochleaPositionX :
X position of trochlear notch
Notes
-----
Get the x/y coordinates for the trochlear notch. This is an iterative method that assumes things about the shape the
femoral cartilage.
'''
# Goal is to find the most anterior point that is between the medial/lateral condyles
# First guess at the troch notch in the 1st axis (med/lat axis) is the location with the smallest value for
# the 2nd axis CofM. This is because in axis 1, negative is anterior and we expect the most anterior CofM should
# roughly align with the trochlear notch.
y_CofM = get_y_CofM(flattenedSeg)
first_guess = y_CofM[np.argmin(y_CofM[:,1]), 0]
# the second guess is just the CofM of the whole cartilage.
centerX, centerY = absolute_CofM(flattenedSeg)
second_guess = centerX
# We use the 2 guesses to help define a search space for the trochlear notch.
min_search = int(np.min((first_guess,second_guess))-20)
max_search = int(np.max((first_guess,second_guess))+20)
# now, we iterate over all of the rows (axis 1) of the search space (moving in the medial/lateral direction)
# we are looking for the row where the most posterior point (back of femur) is furthest anterior (notch).
for y in range(min_search, max_search):
# At each row, we find most posterior pixel labeled as cartilage.
try:
trochleaPosition_test = np.max(np.where(flattenedSeg[y,:]==1))
except ValueError:
# if there is no cartilage we'll get a ValueError exception.
# in that case, set this value to be the max it can be (the size of the first axis)
trochleaPosition_test = flattenedSeg.shape[1]
# if the most posterior point for this row is more anterior than the current trochleaPositionX,
# then update this to be the new trochlear notch.
if trochleaPosition_test < trochleaPositionX:
trochleaPositionX = trochleaPosition_test
trochleaPositionY = y
return(trochleaPositionY, trochleaPositionX+1)
def getAnteriorOfWeightBearing(segArray, femurIndex=1):
'''
Prepare full segmentation and extract the trochlear notch location.
Parameters
----------
flattenedSeg : 2D array
Axial flattened, and filled in femoral cartilage segmentation.
femurIndex : int
Index of the label used to localize the femur in the array.
Returns
-------
trochleaPositionY :
Y position of trochlear notch
trochleaPositionX :
X position of trochlear notch
Notes
-----
Get the x/y coordinates for the trochlear notch. This is an iterative method that assumes things about the shape the femoral cartilage.
First flatten and fill any holes in the segmentation.
'''
femurSegmentation = np.zeros_like(segArray)
femurSegmentation[segArray == femurIndex] = 1
flattenedSegmentation = np.amax(femurSegmentation, axis=1)
flattened_seg_filled = ndi.binary_fill_holes(flattenedSegmentation)
trochY, trochX = findNotch(flattened_seg_filled)
return(trochY, trochX)
def getCartilageSubRegions(segArray, anteriorWBslice, posteriorWBslice, trochY,
femurLabel=1, medTibiaLabel=2, latTibiaLabel=3, antFemurMask=5,
medWbFemurMask=6, latWbFemurMask=7, medPostFemurMask=8, latPostFemurMask=9):
'''
Take cartilage segmentation, and decompose femoral cartilage into subregions of interest.
Parameters
----------
segArray : array
3D array with segmentation for the cartialge regions.
anteriorWBslice : int
Slice that seperates the anterior and weight bearing femoral cartilage.
posteriorWBslice : int
Slice that seperates the weight bearing and posterior femoral cartilage.
trochY : int
Slice that differentiates medial / lateral femur - trochlear notch Y component.
femurLabel : int
Label that femur is in the segArray
medTibiaLabel : int
Label that medial tibia is in the segArray
latTibiaLabel : int
Label that lateral tibia is in the segArray
antFemurMask : int
Label anterior femur should be labeled in final segmentation.
medWbFemurMask : int
Label medial weight bearing femur should be labeled in final segmentation.
latWbFemurMask : int
Label lateral weight bearing femur should be labeled in final segmentation.
medPostFemurMask : int
Label medial posterior femur should be labeled in final segmentation.
latPostFemurMask : int
Label lateral posterior femur should be labeled in final segmentation.
Returns
-------
final_segmentation : array
3D array with the updated segmentations - including weightbearing, medial/latera, anterior, and posterior.
Notes
-----
'''
#array to store final segmentation
final_segmentation = np.zeros_like(segArray)
#create masks for ant/wb/posterior femur
anterior_femur_mask = np.zeros_like(segArray)
anterior_femur_mask[:,:,:anteriorWBslice] = 1
wb_femur_mask = np.zeros_like(segArray)
wb_femur_mask[:,:,anteriorWBslice:posteriorWBslice] = 1
posterior_femur_mask = np.zeros_like(segArray)
posterior_femur_mask[:,:,posteriorWBslice:] = 1
#create seg of just femur - and then break it into the sub-regions
femurSegArray = np.zeros_like(segArray)
femurSegArray[segArray==femurLabel] = 1
#find the center of the medial/lateral tibia - use to distinguish M/L femur ROIs
locationMedialTibia = np.asarray(np.where(segArray==medTibiaLabel))
locationLateralTibia = np.asarray(np.where(segArray==latTibiaLabel))
centerMedialTibia = locationMedialTibia.mean(axis=1)
centerLateralTibia = locationLateralTibia.mean(axis=1)
med_femur_mask = np.zeros_like(segArray)
lat_femur_mask = np.zeros_like(segArray)
if centerMedialTibia[0] > trochY:
med_femur_mask[trochY:,:,:] = 1
lat_femur_mask[:trochY,:,:] = 1
else:
med_femur_mask[:trochY,:,:] = 1
lat_femur_mask[trochY:,:,:] = 1
final_segmentation[segArray!=femurLabel] = segArray[segArray!=femurLabel]
final_segmentation += (femurSegArray * anterior_femur_mask) * antFemurMask
final_segmentation += (femurSegArray * wb_femur_mask * med_femur_mask) * medWbFemurMask
final_segmentation += (femurSegArray * wb_femur_mask * lat_femur_mask) * latWbFemurMask
final_segmentation += (femurSegArray * posterior_femur_mask * med_femur_mask) * medPostFemurMask
final_segmentation += (femurSegArray * posterior_femur_mask * lat_femur_mask) * latPostFemurMask
return(final_segmentation)
def verify_and_correct_med_lat_tib_cart(
seg_array, #sitk.GetArrayViewFromImage(seg)
tib_label=6,
med_tib_cart_label=2,
lat_tib_cart_label=3,
ml_axis=0
):
'''
Verify that the medial and lateral tibial cartilage are correctly labeled.
Parameters
----------
seg_array : array
3D array with segmentation for the cartilage/bone regions.
tib_label : int
Label that tibial cartilage is in the seg_array
med_tib_cart_label : int
Label that medial tibial cartilage is in the seg_array
lat_tib_cart_label : int
Label that lateral tibial cartilage is in the seg_array
ml_axis : int
Medial/lateral axis of the acquired knee MRI.
Returns
-------
seg_array : array
3D array with segmentation for the cartilage/bone regions.
The tibial cartilage regions will have been updated to ensure
all tib cart on med/lat sides are correctly classified.
'''
#get binary array for tibia
array_tib = np.zeros_like(seg_array)
array_tib[seg_array == tib_label] = 1
#get binary array for tib cart
array_tib_cart = np.zeros_like(seg_array)
array_tib_cart[(seg_array == lat_tib_cart_label) + (seg_array == med_tib_cart_label)] = 1
#get the locatons of med/lat cartilage & get their centroids
med_cart_locs = np.asarray(np.where(seg_array == med_tib_cart_label))
lat_cart_locs = np.asarray(np.where(seg_array == lat_tib_cart_label))
middle_med_cart = med_cart_locs[ml_axis,:].mean()
middle_lat_cart = lat_cart_locs[ml_axis,:].mean()
#get location of tibia to get centroid of tibial plateau
tib_locs = np.asarray(np.where(seg_array == tib_label))
middle_tib = tib_locs[ml_axis, :].mean()
center_tibia_slice = int(middle_tib)
# infer the direction(s) for medial/lateral
med_direction = np.sign(middle_med_cart - middle_tib)
lat_direction = np.sign(middle_lat_cart - middle_tib)
if med_direction == lat_direction:
raise Exception('Middle of med and lat tibial cartilage on same side of centerline!')
#create med/lat cartilage masks - binary for updating seg masks
med_tib_cart_mask = np.zeros_like(seg_array)
lat_tib_cart_mask = np.zeros_like(seg_array)
if med_direction > 0:
med_tib_cart_mask[center_tibia_slice:,...] = 1
lat_tib_cart_mask[:center_tibia_slice,...] = 1
elif med_direction < 0:
med_tib_cart_mask[:center_tibia_slice,...] = 1
lat_tib_cart_mask[center_tibia_slice:,...] = 1
# create new med/lat cartilage arrays
new_med_cart_array = array_tib_cart * med_tib_cart_mask
new_lat_cart_array = array_tib_cart * lat_tib_cart_mask
#make copy of original segmentation array & update
# med/lat tibial cartilage labels
new_seg_array = seg_array.copy()
new_seg_array[new_med_cart_array == 1] = med_tib_cart_label
new_seg_array[new_lat_cart_array == 1] = lat_tib_cart_label
return new_seg_array
def get_knee_segmentation_with_femur_subregions(seg_image,
fem_cart_label_idx=1,
wb_region_percent_dist=0.6,
# femur_label=1,
med_tibia_label=2,
lat_tibia_label=3,
ant_femur_mask=11,
med_wb_femur_mask=12,
lat_wb_femur_mask=13,
med_post_femur_mask=14,
lat_post_femur_mask=15,
verify_med_lat_tib_cart=True,
tibia_label=6,
ml_axis=0
):
"""
Give seg image of knee. Return seg image with all sub-regions of femur included.
Parameters
----------
seg_image : SimpleITK.Image
SimpleITK image of the segmentation to be processed.
fem_cart_label_idx : int, optional
Label of femoral cartilage, by default 1
wb_region_percent_dist : float, optional
How large weightbearing region is (from not to posterior of condyles), by default 0.6
femur_label : int, optional
Seg label for the femur cartilage, by default 1
med_tibia_label : int, optional
Seg label for the medial tibia cartilage, by default 2
lat_tibia_label : int, optional
Seg label for the lateral tibia cartilage, by default 3
ant_femur_mask : int, optional
Seg label for the anterior femur region, by default 11
med_wb_femur_mask : int, optional
Seg label for medial weight-bearing femur, by default 12
lat_wb_femur_mask : int, optional
Seg label for lateral weight-bearing femur, by default 13
med_post_femur_mask : int, optional
Seg label for medial posterior femur, by default 14
lat_post_femur_mask : int, optional
Seg label for lateral posterior femur, by default 15
verify_med_lat_tib_cart : bool, optional
Whether to verify that medial and lateral tibial cartilage is on same side of centerline, by default True
tibia_label : int, optional
Seg label for the tibia, by default 6
ml_axis : int, optional
Medial/lateral axis of the acquired knee MRI, by default 0
Returns
-------
SimpleITK.Image
Image of the new/updated segmentation
"""
troch_notch_y, troch_notch_x = getAnteriorOfWeightBearing(sitk.GetArrayViewFromImage(seg_image),
femurIndex=fem_cart_label_idx)
loc_fem_z, loc_fem_y, loc_fem_x = np.where(sitk.GetArrayViewFromImage(seg_image) == fem_cart_label_idx)
post_femur_slice = np.max(loc_fem_x)
posterior_wb_slice = np.round((post_femur_slice - troch_notch_x) * wb_region_percent_dist + troch_notch_x).astype(int)
new_seg_array = getCartilageSubRegions(sitk.GetArrayViewFromImage(seg_image),
anteriorWBslice=troch_notch_x,
posteriorWBslice=posterior_wb_slice,
trochY=troch_notch_y,
femurLabel=fem_cart_label_idx,
medTibiaLabel=med_tibia_label,
latTibiaLabel=lat_tibia_label,
antFemurMask=ant_femur_mask,
medWbFemurMask=med_wb_femur_mask,
latWbFemurMask=lat_wb_femur_mask,
medPostFemurMask=med_post_femur_mask,
latPostFemurMask=lat_post_femur_mask
)
if verify_med_lat_tib_cart:
new_seg_array = verify_and_correct_med_lat_tib_cart(new_seg_array,
tib_label=tibia_label,
med_tib_cart_label=med_tibia_label,
lat_tib_cart_label=lat_tibia_label,
ml_axis=ml_axis)
seg_label_image = sitk.GetImageFromArray(new_seg_array)
seg_label_image.CopyInformation(seg_image)
return seg_label_image</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="pymskt.image.cartilage_processing.CofM"><code class="name flex">
<span>def <span class="ident">CofM</span></span>(<span>array)</span>
</code></dt>
<dd>
<div class="desc"><p>Get center of mass for a row of a binary 2D image.
Parameters</p>
<hr>
<dl>
<dt><strong><code>array</code></strong> : <code>1D array</code></dt>
<dd>Individual row of a 2D image.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>centerPixels</code></strong></dt>
<dd>Average location of 1s in the row</dd>
</dl>
<h2 id="notes">Notes</h2>
<p>Calculates the average location of cartilage for the row of image being analyzed.
Returns 0 if there are no pixels</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def CofM(array):
'''
Get center of mass for a row of a binary 2D image.
Parameters
----------
array : 1D array
Individual row of a 2D image.
Returns
-------
centerPixels :
Average location of 1s in the row
Notes
-----
Calculates the average location of cartilage for the row of image being analyzed.
Returns 0 if there are no pixels
'''
pixels = np.where(array==1)
centerPixels = np.mean(pixels)
nans = np.isnan(centerPixels)
if nans == True:
centerPixels = 0
return(centerPixels)</code></pre>
</details>
</dd>
<dt id="pymskt.image.cartilage_processing.absolute_CofM"><code class="name flex">
<span>def <span class="ident">absolute_CofM</span></span>(<span>flattenedSeg)</span>
</code></dt>
<dd>
<div class="desc"><p>Get absolute CofM of all the femoral cartilage pixels
Parameters</p>
<hr>
<dl>
<dt><strong><code>flattenedSeg</code></strong> : <code>2D array</code></dt>
<dd>Axial flattened, and filled in femoral cartilage segmentation.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>centerX</code></strong></dt>
<dd>The CofM in the X direction for the segmentation</dd>
<dt><strong><code>centerY</code></strong></dt>
<dd>The CofM in the Y direction for the segmentation</dd>
</dl>
<h2 id="notes">Notes</h2>
<p>Get the x/y coordinates for the CofM for the whole flattened segmentation</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def absolute_CofM(flattenedSeg):
'''
Get absolute CofM of all the femoral cartilage pixels
Parameters
----------
flattenedSeg : 2D array
Axial flattened, and filled in femoral cartilage segmentation.
Returns
-------
centerX :
The CofM in the X direction for the segmentation
centerY :
The CofM in the Y direction for the segmentation
Notes
-----
Get the x/y coordinates for the CofM for the whole flattened segmentation
'''
femurPoints = np.where(flattenedSeg==1)
centerX = np.mean(femurPoints[0])
centerY = np.mean(femurPoints[1])
return(centerX, centerY)</code></pre>
</details>
</dd>
<dt id="pymskt.image.cartilage_processing.findNotch"><code class="name flex">
<span>def <span class="ident">findNotch</span></span>(<span>flattenedSeg, trochleaPositionX=1000)</span>
</code></dt>
<dd>
<div class="desc"><p>Get the X Y position of the trochlear notch - where medial/lateral sides of the femur meet.
Parameters</p>
<hr>
<dl>
<dt><strong><code>flattenedSeg</code></strong> : <code>2D array</code></dt>
<dd>Axial flattened, and filled in femoral cartilage segmentation.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>trochleaPositionY</code></strong></dt>
<dd>Y position of trochlear notch</dd>
<dt><strong><code>trochleaPositionX</code></strong></dt>
<dd>X position of trochlear notch</dd>
</dl>
<h2 id="notes">Notes</h2>
<p>Get the x/y coordinates for the trochlear notch. This is an iterative method that assumes things about the shape the
femoral cartilage.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def findNotch(flattenedSeg, trochleaPositionX=1000):
'''
Get the X Y position of the trochlear notch - where medial/lateral sides of the femur meet.
Parameters
----------
flattenedSeg : 2D array
Axial flattened, and filled in femoral cartilage segmentation.
Returns
-------
trochleaPositionY :
Y position of trochlear notch
trochleaPositionX :
X position of trochlear notch
Notes
-----
Get the x/y coordinates for the trochlear notch. This is an iterative method that assumes things about the shape the
femoral cartilage.
'''
# Goal is to find the most anterior point that is between the medial/lateral condyles
# First guess at the troch notch in the 1st axis (med/lat axis) is the location with the smallest value for
# the 2nd axis CofM. This is because in axis 1, negative is anterior and we expect the most anterior CofM should
# roughly align with the trochlear notch.
y_CofM = get_y_CofM(flattenedSeg)
first_guess = y_CofM[np.argmin(y_CofM[:,1]), 0]
# the second guess is just the CofM of the whole cartilage.
centerX, centerY = absolute_CofM(flattenedSeg)
second_guess = centerX
# We use the 2 guesses to help define a search space for the trochlear notch.
min_search = int(np.min((first_guess,second_guess))-20)
max_search = int(np.max((first_guess,second_guess))+20)
# now, we iterate over all of the rows (axis 1) of the search space (moving in the medial/lateral direction)
# we are looking for the row where the most posterior point (back of femur) is furthest anterior (notch).
for y in range(min_search, max_search):
# At each row, we find most posterior pixel labeled as cartilage.
try:
trochleaPosition_test = np.max(np.where(flattenedSeg[y,:]==1))
except ValueError:
# if there is no cartilage we'll get a ValueError exception.
# in that case, set this value to be the max it can be (the size of the first axis)
trochleaPosition_test = flattenedSeg.shape[1]
# if the most posterior point for this row is more anterior than the current trochleaPositionX,
# then update this to be the new trochlear notch.
if trochleaPosition_test < trochleaPositionX:
trochleaPositionX = trochleaPosition_test
trochleaPositionY = y
return(trochleaPositionY, trochleaPositionX+1)</code></pre>
</details>
</dd>
<dt id="pymskt.image.cartilage_processing.getAnteriorOfWeightBearing"><code class="name flex">
<span>def <span class="ident">getAnteriorOfWeightBearing</span></span>(<span>segArray, femurIndex=1)</span>
</code></dt>
<dd>
<div class="desc"><p>Prepare full segmentation and extract the trochlear notch location.
Parameters</p>
<hr>
<dl>
<dt><strong><code>flattenedSeg</code></strong> : <code>2D array</code></dt>
<dd>Axial flattened, and filled in femoral cartilage segmentation.</dd>
<dt><strong><code>femurIndex</code></strong> : <code>int</code></dt>
<dd>Index of the label used to localize the femur in the array.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>trochleaPositionY</code></strong></dt>
<dd>Y position of trochlear notch</dd>
<dt><strong><code>trochleaPositionX</code></strong></dt>
<dd>X position of trochlear notch</dd>
</dl>
<h2 id="notes">Notes</h2>
<p>Get the x/y coordinates for the trochlear notch. This is an iterative method that assumes things about the shape the femoral cartilage.
First flatten and fill any holes in the segmentation.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def getAnteriorOfWeightBearing(segArray, femurIndex=1):
'''
Prepare full segmentation and extract the trochlear notch location.
Parameters
----------
flattenedSeg : 2D array
Axial flattened, and filled in femoral cartilage segmentation.
femurIndex : int
Index of the label used to localize the femur in the array.
Returns
-------
trochleaPositionY :
Y position of trochlear notch
trochleaPositionX :
X position of trochlear notch
Notes
-----
Get the x/y coordinates for the trochlear notch. This is an iterative method that assumes things about the shape the femoral cartilage.
First flatten and fill any holes in the segmentation.
'''
femurSegmentation = np.zeros_like(segArray)
femurSegmentation[segArray == femurIndex] = 1
flattenedSegmentation = np.amax(femurSegmentation, axis=1)
flattened_seg_filled = ndi.binary_fill_holes(flattenedSegmentation)
trochY, trochX = findNotch(flattened_seg_filled)
return(trochY, trochX)</code></pre>
</details>
</dd>
<dt id="pymskt.image.cartilage_processing.getCartilageSubRegions"><code class="name flex">
<span>def <span class="ident">getCartilageSubRegions</span></span>(<span>segArray, anteriorWBslice, posteriorWBslice, trochY, femurLabel=1, medTibiaLabel=2, latTibiaLabel=3, antFemurMask=5, medWbFemurMask=6, latWbFemurMask=7, medPostFemurMask=8, latPostFemurMask=9)</span>
</code></dt>
<dd>
<div class="desc"><p>Take cartilage segmentation, and decompose femoral cartilage into subregions of interest.<br>
Parameters</p>
<hr>
<dl>
<dt><strong><code>segArray</code></strong> : <code>array</code></dt>
<dd>3D array with segmentation for the cartialge regions.</dd>
<dt><strong><code>anteriorWBslice</code></strong> : <code>int</code></dt>
<dd>Slice that seperates the anterior and weight bearing femoral cartilage.</dd>
<dt><strong><code>posteriorWBslice</code></strong> : <code>int</code></dt>
<dd>Slice that seperates the weight bearing and posterior femoral cartilage.</dd>
<dt><strong><code>trochY</code></strong> : <code>int</code></dt>
<dd>Slice that differentiates medial / lateral femur - trochlear notch Y component.</dd>
<dt><strong><code>femurLabel</code></strong> : <code>int</code></dt>
<dd>Label that femur is in the segArray</dd>
<dt><strong><code>medTibiaLabel</code></strong> : <code>int</code></dt>
<dd>Label that medial tibia is in the segArray</dd>
<dt><strong><code>latTibiaLabel</code></strong> : <code>int</code></dt>
<dd>Label that lateral tibia is in the segArray</dd>
<dt><strong><code>antFemurMask</code></strong> : <code>int</code></dt>
<dd>Label anterior femur should be labeled in final segmentation.</dd>
<dt><strong><code>medWbFemurMask</code></strong> : <code>int</code></dt>
<dd>Label medial weight bearing femur should be labeled in final segmentation.</dd>
<dt><strong><code>latWbFemurMask</code></strong> : <code>int</code></dt>
<dd>Label lateral weight bearing femur should be labeled in final segmentation.</dd>
<dt><strong><code>medPostFemurMask</code></strong> : <code>int</code></dt>
<dd>Label medial posterior femur should be labeled in final segmentation.</dd>
<dt><strong><code>latPostFemurMask</code></strong> : <code>int</code></dt>
<dd>Label lateral posterior femur should be labeled in final segmentation.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>final_segmentation</code></strong> : <code>array</code></dt>
<dd>3D array with the updated segmentations - including weightbearing, medial/latera, anterior, and posterior.</dd>
</dl>
<h2 id="notes">Notes</h2></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def getCartilageSubRegions(segArray, anteriorWBslice, posteriorWBslice, trochY,
femurLabel=1, medTibiaLabel=2, latTibiaLabel=3, antFemurMask=5,
medWbFemurMask=6, latWbFemurMask=7, medPostFemurMask=8, latPostFemurMask=9):
'''
Take cartilage segmentation, and decompose femoral cartilage into subregions of interest.
Parameters
----------
segArray : array
3D array with segmentation for the cartialge regions.
anteriorWBslice : int
Slice that seperates the anterior and weight bearing femoral cartilage.
posteriorWBslice : int
Slice that seperates the weight bearing and posterior femoral cartilage.
trochY : int
Slice that differentiates medial / lateral femur - trochlear notch Y component.
femurLabel : int
Label that femur is in the segArray
medTibiaLabel : int
Label that medial tibia is in the segArray
latTibiaLabel : int
Label that lateral tibia is in the segArray
antFemurMask : int
Label anterior femur should be labeled in final segmentation.
medWbFemurMask : int
Label medial weight bearing femur should be labeled in final segmentation.
latWbFemurMask : int
Label lateral weight bearing femur should be labeled in final segmentation.
medPostFemurMask : int
Label medial posterior femur should be labeled in final segmentation.
latPostFemurMask : int
Label lateral posterior femur should be labeled in final segmentation.
Returns
-------
final_segmentation : array
3D array with the updated segmentations - including weightbearing, medial/latera, anterior, and posterior.
Notes
-----
'''
#array to store final segmentation
final_segmentation = np.zeros_like(segArray)
#create masks for ant/wb/posterior femur
anterior_femur_mask = np.zeros_like(segArray)
anterior_femur_mask[:,:,:anteriorWBslice] = 1
wb_femur_mask = np.zeros_like(segArray)
wb_femur_mask[:,:,anteriorWBslice:posteriorWBslice] = 1
posterior_femur_mask = np.zeros_like(segArray)
posterior_femur_mask[:,:,posteriorWBslice:] = 1
#create seg of just femur - and then break it into the sub-regions
femurSegArray = np.zeros_like(segArray)
femurSegArray[segArray==femurLabel] = 1
#find the center of the medial/lateral tibia - use to distinguish M/L femur ROIs
locationMedialTibia = np.asarray(np.where(segArray==medTibiaLabel))
locationLateralTibia = np.asarray(np.where(segArray==latTibiaLabel))
centerMedialTibia = locationMedialTibia.mean(axis=1)
centerLateralTibia = locationLateralTibia.mean(axis=1)
med_femur_mask = np.zeros_like(segArray)
lat_femur_mask = np.zeros_like(segArray)
if centerMedialTibia[0] > trochY:
med_femur_mask[trochY:,:,:] = 1
lat_femur_mask[:trochY,:,:] = 1
else:
med_femur_mask[:trochY,:,:] = 1
lat_femur_mask[trochY:,:,:] = 1
final_segmentation[segArray!=femurLabel] = segArray[segArray!=femurLabel]
final_segmentation += (femurSegArray * anterior_femur_mask) * antFemurMask
final_segmentation += (femurSegArray * wb_femur_mask * med_femur_mask) * medWbFemurMask
final_segmentation += (femurSegArray * wb_femur_mask * lat_femur_mask) * latWbFemurMask
final_segmentation += (femurSegArray * posterior_femur_mask * med_femur_mask) * medPostFemurMask
final_segmentation += (femurSegArray * posterior_femur_mask * lat_femur_mask) * latPostFemurMask
return(final_segmentation)</code></pre>
</details>
</dd>
<dt id="pymskt.image.cartilage_processing.get_knee_segmentation_with_femur_subregions"><code class="name flex">
<span>def <span class="ident">get_knee_segmentation_with_femur_subregions</span></span>(<span>seg_image, fem_cart_label_idx=1, wb_region_percent_dist=0.6, med_tibia_label=2, lat_tibia_label=3, ant_femur_mask=11, med_wb_femur_mask=12, lat_wb_femur_mask=13, med_post_femur_mask=14, lat_post_femur_mask=15, verify_med_lat_tib_cart=True, tibia_label=6, ml_axis=0)</span>
</code></dt>
<dd>
<div class="desc"><p>Give seg image of knee. Return seg image with all sub-regions of femur included. </p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>seg_image</code></strong> : <code>SimpleITK.Image</code></dt>
<dd>SimpleITK image of the segmentation to be processed.</dd>
<dt><strong><code>fem_cart_label_idx</code></strong> : <code>int</code>, optional</dt>
<dd>Label of femoral cartilage, by default 1</dd>
<dt><strong><code>wb_region_percent_dist</code></strong> : <code>float</code>, optional</dt>
<dd>How large weightbearing region is (from not to posterior of condyles), by default 0.6</dd>
<dt><strong><code>femur_label</code></strong> : <code>int</code>, optional</dt>
<dd>Seg label for the femur cartilage, by default 1</dd>
<dt><strong><code>med_tibia_label</code></strong> : <code>int</code>, optional</dt>
<dd>Seg label for the medial tibia cartilage, by default 2</dd>
<dt><strong><code>lat_tibia_label</code></strong> : <code>int</code>, optional</dt>
<dd>Seg label for the lateral tibia cartilage, by default 3</dd>
<dt><strong><code>ant_femur_mask</code></strong> : <code>int</code>, optional</dt>
<dd>Seg label for the anterior femur region, by default 11</dd>
<dt><strong><code>med_wb_femur_mask</code></strong> : <code>int</code>, optional</dt>
<dd>Seg label for medial weight-bearing femur, by default 12</dd>
<dt><strong><code>lat_wb_femur_mask</code></strong> : <code>int</code>, optional</dt>
<dd>Seg label for lateral weight-bearing femur, by default 13</dd>
<dt><strong><code>med_post_femur_mask</code></strong> : <code>int</code>, optional</dt>
<dd>Seg label for medial posterior femur, by default 14</dd>
<dt><strong><code>lat_post_femur_mask</code></strong> : <code>int</code>, optional</dt>
<dd>Seg label for lateral posterior femur, by default 15</dd>
<dt><strong><code>verify_med_lat_tib_cart</code></strong> : <code>bool</code>, optional</dt>
<dd>Whether to verify that medial and lateral tibial cartilage is on same side of centerline, by default True</dd>
<dt><strong><code>tibia_label</code></strong> : <code>int</code>, optional</dt>
<dd>Seg label for the tibia, by default 6</dd>
<dt><strong><code>ml_axis</code></strong> : <code>int</code>, optional</dt>
<dd>Medial/lateral axis of the acquired knee MRI, by default 0</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>SimpleITK.Image</code></dt>
<dd>Image of the new/updated segmentation</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_knee_segmentation_with_femur_subregions(seg_image,
fem_cart_label_idx=1,
wb_region_percent_dist=0.6,
# femur_label=1,
med_tibia_label=2,
lat_tibia_label=3,
ant_femur_mask=11,
med_wb_femur_mask=12,
lat_wb_femur_mask=13,
med_post_femur_mask=14,
lat_post_femur_mask=15,
verify_med_lat_tib_cart=True,
tibia_label=6,
ml_axis=0
):
"""
Give seg image of knee. Return seg image with all sub-regions of femur included.
Parameters
----------
seg_image : SimpleITK.Image
SimpleITK image of the segmentation to be processed.
fem_cart_label_idx : int, optional
Label of femoral cartilage, by default 1
wb_region_percent_dist : float, optional
How large weightbearing region is (from not to posterior of condyles), by default 0.6
femur_label : int, optional
Seg label for the femur cartilage, by default 1
med_tibia_label : int, optional
Seg label for the medial tibia cartilage, by default 2
lat_tibia_label : int, optional
Seg label for the lateral tibia cartilage, by default 3
ant_femur_mask : int, optional
Seg label for the anterior femur region, by default 11
med_wb_femur_mask : int, optional
Seg label for medial weight-bearing femur, by default 12
lat_wb_femur_mask : int, optional
Seg label for lateral weight-bearing femur, by default 13
med_post_femur_mask : int, optional
Seg label for medial posterior femur, by default 14
lat_post_femur_mask : int, optional
Seg label for lateral posterior femur, by default 15
verify_med_lat_tib_cart : bool, optional
Whether to verify that medial and lateral tibial cartilage is on same side of centerline, by default True
tibia_label : int, optional
Seg label for the tibia, by default 6
ml_axis : int, optional
Medial/lateral axis of the acquired knee MRI, by default 0
Returns
-------
SimpleITK.Image
Image of the new/updated segmentation
"""
troch_notch_y, troch_notch_x = getAnteriorOfWeightBearing(sitk.GetArrayViewFromImage(seg_image),
femurIndex=fem_cart_label_idx)
loc_fem_z, loc_fem_y, loc_fem_x = np.where(sitk.GetArrayViewFromImage(seg_image) == fem_cart_label_idx)
post_femur_slice = np.max(loc_fem_x)
posterior_wb_slice = np.round((post_femur_slice - troch_notch_x) * wb_region_percent_dist + troch_notch_x).astype(int)
new_seg_array = getCartilageSubRegions(sitk.GetArrayViewFromImage(seg_image),
anteriorWBslice=troch_notch_x,
posteriorWBslice=posterior_wb_slice,
trochY=troch_notch_y,
femurLabel=fem_cart_label_idx,
medTibiaLabel=med_tibia_label,
latTibiaLabel=lat_tibia_label,
antFemurMask=ant_femur_mask,
medWbFemurMask=med_wb_femur_mask,
latWbFemurMask=lat_wb_femur_mask,
medPostFemurMask=med_post_femur_mask,
latPostFemurMask=lat_post_femur_mask
)
if verify_med_lat_tib_cart:
new_seg_array = verify_and_correct_med_lat_tib_cart(new_seg_array,
tib_label=tibia_label,
med_tib_cart_label=med_tibia_label,
lat_tib_cart_label=lat_tibia_label,
ml_axis=ml_axis)
seg_label_image = sitk.GetImageFromArray(new_seg_array)
seg_label_image.CopyInformation(seg_image)
return seg_label_image</code></pre>
</details>
</dd>
<dt id="pymskt.image.cartilage_processing.get_y_CofM"><code class="name flex">
<span>def <span class="ident">get_y_CofM</span></span>(<span>flattenedSeg)</span>
</code></dt>
<dd>
<div class="desc"><p>Get CofM of femoral cartilage for each row of the flattened segmentation.
Parameters</p>
<hr>
<dl>
<dt><strong><code>flattenedSeg</code></strong> : <code>2D array</code></dt>
<dd>Axial flattened, and filled in femoral cartilage segmentation.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>yCofM</code></strong></dt>
<dd>Find the CofM for each row of the image.</dd>
</dl>
<h2 id="notes">Notes</h2>
<p>Get the x/y coordinates for the CofM for each row of the flattened segmentation.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_y_CofM(flattenedSeg):
'''
Get CofM of femoral cartilage for each row of the flattened segmentation.
Parameters
----------
flattenedSeg : 2D array
Axial flattened, and filled in femoral cartilage segmentation.
Returns
-------
yCofM :
Find the CofM for each row of the image.
Notes
-----
Get the x/y coordinates for the CofM for each row of the flattened segmentation.
'''
locationFemur = np.where(flattenedSeg==1)
yCofM = np.zeros((flattenedSeg.shape[0], 2), dtype=int)
# only calculate for rows with cartilage.
minRow = np.min(locationFemur[0])
maxRow = np.max(locationFemur[0])
# iterate over rows of image, get CofM, store CofM for row.
for x in range(minRow, maxRow):
yCofM[x, 0] = x #store the x-coordinate (row) we calcualted CofM for.
yCofM[x, 1] = int(CofM(flattenedSeg[x, :])) # store the CofM value (make it an integer for indexing)
yCofM = yCofM[minRow+10:maxRow-10,:] # remove 10 most medial and most lateral pixels of femoral cartilage.
return(yCofM) </code></pre>
</details>
</dd>
<dt id="pymskt.image.cartilage_processing.verify_and_correct_med_lat_tib_cart"><code class="name flex">
<span>def <span class="ident">verify_and_correct_med_lat_tib_cart</span></span>(<span>seg_array, tib_label=6, med_tib_cart_label=2, lat_tib_cart_label=3, ml_axis=0)</span>
</code></dt>
<dd>
<div class="desc"><p>Verify that the medial and lateral tibial cartilage are correctly labeled.
Parameters</p>
<hr>
<dl>
<dt><strong><code>seg_array</code></strong> : <code>array</code></dt>
<dd>3D array with segmentation for the cartilage/bone regions.</dd>
<dt><strong><code>tib_label</code></strong> : <code>int</code></dt>
<dd>Label that tibial cartilage is in the seg_array</dd>
<dt><strong><code>med_tib_cart_label</code></strong> : <code>int</code></dt>
<dd>Label that medial tibial cartilage is in the seg_array</dd>
<dt><strong><code>lat_tib_cart_label</code></strong> : <code>int</code></dt>
<dd>Label that lateral tibial cartilage is in the seg_array</dd>
<dt><strong><code>ml_axis</code></strong> : <code>int</code></dt>
<dd>Medial/lateral axis of the acquired knee MRI.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>seg_array</code></strong> : <code>array</code></dt>
<dd>3D array with segmentation for the cartilage/bone regions.
The tibial cartilage regions will have been updated to ensure
all tib cart on med/lat sides are correctly classified.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def verify_and_correct_med_lat_tib_cart(
seg_array, #sitk.GetArrayViewFromImage(seg)
tib_label=6,
med_tib_cart_label=2,
lat_tib_cart_label=3,
ml_axis=0
):
'''
Verify that the medial and lateral tibial cartilage are correctly labeled.
Parameters
----------
seg_array : array
3D array with segmentation for the cartilage/bone regions.
tib_label : int
Label that tibial cartilage is in the seg_array
med_tib_cart_label : int
Label that medial tibial cartilage is in the seg_array
lat_tib_cart_label : int
Label that lateral tibial cartilage is in the seg_array
ml_axis : int
Medial/lateral axis of the acquired knee MRI.
Returns
-------
seg_array : array
3D array with segmentation for the cartilage/bone regions.
The tibial cartilage regions will have been updated to ensure
all tib cart on med/lat sides are correctly classified.
'''
#get binary array for tibia
array_tib = np.zeros_like(seg_array)
array_tib[seg_array == tib_label] = 1
#get binary array for tib cart
array_tib_cart = np.zeros_like(seg_array)
array_tib_cart[(seg_array == lat_tib_cart_label) + (seg_array == med_tib_cart_label)] = 1
#get the locatons of med/lat cartilage & get their centroids
med_cart_locs = np.asarray(np.where(seg_array == med_tib_cart_label))
lat_cart_locs = np.asarray(np.where(seg_array == lat_tib_cart_label))
middle_med_cart = med_cart_locs[ml_axis,:].mean()
middle_lat_cart = lat_cart_locs[ml_axis,:].mean()
#get location of tibia to get centroid of tibial plateau
tib_locs = np.asarray(np.where(seg_array == tib_label))
middle_tib = tib_locs[ml_axis, :].mean()
center_tibia_slice = int(middle_tib)
# infer the direction(s) for medial/lateral
med_direction = np.sign(middle_med_cart - middle_tib)
lat_direction = np.sign(middle_lat_cart - middle_tib)
if med_direction == lat_direction:
raise Exception('Middle of med and lat tibial cartilage on same side of centerline!')
#create med/lat cartilage masks - binary for updating seg masks
med_tib_cart_mask = np.zeros_like(seg_array)
lat_tib_cart_mask = np.zeros_like(seg_array)
if med_direction > 0:
med_tib_cart_mask[center_tibia_slice:,...] = 1
lat_tib_cart_mask[:center_tibia_slice,...] = 1
elif med_direction < 0:
med_tib_cart_mask[:center_tibia_slice,...] = 1
lat_tib_cart_mask[center_tibia_slice:,...] = 1
# create new med/lat cartilage arrays
new_med_cart_array = array_tib_cart * med_tib_cart_mask
new_lat_cart_array = array_tib_cart * lat_tib_cart_mask
#make copy of original segmentation array & update
# med/lat tibial cartilage labels
new_seg_array = seg_array.copy()
new_seg_array[new_med_cart_array == 1] = med_tib_cart_label
new_seg_array[new_lat_cart_array == 1] = lat_tib_cart_label
return new_seg_array</code></pre>
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<li><h3><a href="#header-functions">Functions</a></h3>
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<li><code><a title="pymskt.image.cartilage_processing.CofM" href="#pymskt.image.cartilage_processing.CofM">CofM</a></code></li>
<li><code><a title="pymskt.image.cartilage_processing.absolute_CofM" href="#pymskt.image.cartilage_processing.absolute_CofM">absolute_CofM</a></code></li>
<li><code><a title="pymskt.image.cartilage_processing.findNotch" href="#pymskt.image.cartilage_processing.findNotch">findNotch</a></code></li>
<li><code><a title="pymskt.image.cartilage_processing.getAnteriorOfWeightBearing" href="#pymskt.image.cartilage_processing.getAnteriorOfWeightBearing">getAnteriorOfWeightBearing</a></code></li>
<li><code><a title="pymskt.image.cartilage_processing.getCartilageSubRegions" href="#pymskt.image.cartilage_processing.getCartilageSubRegions">getCartilageSubRegions</a></code></li>
<li><code><a title="pymskt.image.cartilage_processing.get_knee_segmentation_with_femur_subregions" href="#pymskt.image.cartilage_processing.get_knee_segmentation_with_femur_subregions">get_knee_segmentation_with_femur_subregions</a></code></li>
<li><code><a title="pymskt.image.cartilage_processing.get_y_CofM" href="#pymskt.image.cartilage_processing.get_y_CofM">get_y_CofM</a></code></li>
<li><code><a title="pymskt.image.cartilage_processing.verify_and_correct_med_lat_tib_cart" href="#pymskt.image.cartilage_processing.verify_and_correct_med_lat_tib_cart">verify_and_correct_med_lat_tib_cart</a></code></li>
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