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evaluation.py
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evaluation.py
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# -*- coding: utf-8 -*-
import difflib
import numpy as np
import os
import SimpleITK as sitk
import scipy.spatial
# Set the path to the source data (e.g. the training data for self-testing)
# and the output directory of that subject
testDir = 'evaluation' # For example: '/input/2'
participantDir = 'evaluation' # For example: '/output/2'
labels = {1: 'Cortical gray matter',
2: 'Basal ganglia',
3: 'White matter',
4: 'White matter lesions',
5: 'Cerebrospinal fluid in the extracerebral space',
6: 'Ventricles',
7: 'Cerebellum',
8: 'Brain stem',
# The two labels below are ignored:
#9: 'Infarction',
#10: 'Other',
}
def do():
"""Main function"""
resultFilename = getResultFilename(participantDir)
testImage, resultImage = getImages(os.path.join(testDir, 'segm.nii.gz'), resultFilename)
dsc = getDSC(testImage, resultImage)
h95 = getHausdorff(testImage, resultImage)
vs = getVS(testImage, resultImage)
print('Dice', dsc, '(higher is better, max=1)')
print('HD', h95, 'mm', '(lower is better, min=0)')
print('VS', vs, '(higher is better, max=1)')
def getResultFilename(participantDir):
"""Find the filename of the result image.
This should be result.nii.gz or result.nii. If these files are not present,
it tries to find the closest filename."""
files = os.listdir(participantDir)
if not files:
raise Exception("No results in "+ participantDir)
resultFilename = None
if 'result.nii.gz' in files:
resultFilename = os.path.join(participantDir, 'result.nii.gz')
elif 'result.nii' in files:
resultFilename = os.path.join(participantDir, 'result.nii')
else:
# Find the filename that is closest to 'result.nii.gz'
maxRatio = -1
for f in files:
currentRatio = difflib.SequenceMatcher(a = f, b = 'result.nii.gz').ratio()
if currentRatio > maxRatio:
resultFilename = os.path.join(participantDir, f)
maxRatio = currentRatio
return resultFilename
def getImages(testFilename, resultFilename):
"""Return the test and result images, thresholded and pathology masked."""
testImage = sitk.ReadImage(testFilename)
resultImage = sitk.ReadImage(resultFilename)
# Check for equality
assert testImage.GetSize() == resultImage.GetSize()
# Get meta data from the test-image, needed for some sitk methods that check this
resultImage.CopyInformation(testImage)
# Remove pathology from the test and result images, since we don't evaluate on that
pathologyImage = sitk.BinaryThreshold(testImage, 9, 11, 0, 1) # pathology == 9 or 10
maskedTestImage = sitk.Mask(testImage, pathologyImage) # tissue == 1 -- 8
maskedResultImage = sitk.Mask(resultImage, pathologyImage)
# Force integer
if not 'integer' in maskedResultImage.GetPixelIDTypeAsString():
maskedResultImage = sitk.Cast(maskedResultImage, sitk.sitkUInt8)
return maskedTestImage, maskedResultImage
def getDSC(testImage, resultImage):
"""Compute the Dice Similarity Coefficient."""
dsc = dict()
for k in labels.keys():
testArray = sitk.GetArrayFromImage(sitk.BinaryThreshold( testImage, k, k, 1, 0)).flatten()
resultArray = sitk.GetArrayFromImage(sitk.BinaryThreshold(resultImage, k, k, 1, 0)).flatten()
# similarity = 1.0 - dissimilarity
# scipy.spatial.distance.dice raises a ZeroDivisionError if both arrays contain only zeros.
try:
dsc[k] = 1.0 - scipy.spatial.distance.dice(testArray, resultArray)
except ZeroDivisionError:
dsc[k] = None
return dsc
def getHausdorff(testImage, resultImage):
"""Compute the 95% Hausdorff distance."""
hd = dict()
for k in labels.keys():
lTestImage = sitk.BinaryThreshold( testImage, k, k, 1, 0)
lResultImage = sitk.BinaryThreshold(resultImage, k, k, 1, 0)
# Hausdorff distance is only defined when something is detected
statistics = sitk.StatisticsImageFilter()
statistics.Execute(lTestImage)
lTestSum = statistics.GetSum()
statistics.Execute(lResultImage)
lResultSum = statistics.GetSum()
if lTestSum == 0 or lResultSum == 0:
hd[k] = None
continue
# Edge detection is done by ORIGINAL - ERODED, keeping the outer boundaries of lesions. Erosion is performed in 2D
eTestImage = sitk.BinaryErode(lTestImage, (1,1,0))
eResultImage = sitk.BinaryErode(lResultImage, (1,1,0))
hTestImage = sitk.Subtract(lTestImage, eTestImage)
hResultImage = sitk.Subtract(lResultImage, eResultImage)
hTestArray = sitk.GetArrayFromImage(hTestImage)
hResultArray = sitk.GetArrayFromImage(hResultImage)
# Convert voxel location to world coordinates. Use the coordinate system of the test image
# np.nonzero = elements of the boundary in numpy order (zyx)
# np.flipud = elements in xyz order
# np.transpose = create tuples (x,y,z)
# testImage.TransformIndexToPhysicalPoint converts (xyz) to world coordinates (in mm)
# (Simple)ITK does not accept all Numpy arrays; therefore we need to convert the coordinate tuples into a Python list before passing them to TransformIndexToPhysicalPoint().
testCoordinates = [testImage.TransformIndexToPhysicalPoint(x.tolist()) for x in np.transpose( np.flipud( np.nonzero(hTestArray) ))]
resultCoordinates = [testImage.TransformIndexToPhysicalPoint(x.tolist()) for x in np.transpose( np.flipud( np.nonzero(hResultArray) ))]
# Use a kd-tree for fast spatial search
def getDistancesFromAtoB(a, b):
kdTree = scipy.spatial.KDTree(a, leafsize=100)
return kdTree.query(b, k=1, eps=0, p=2)[0]
# Compute distances from test to result and vice versa.
dTestToResult = getDistancesFromAtoB(testCoordinates, resultCoordinates)
dResultToTest = getDistancesFromAtoB(resultCoordinates, testCoordinates)
hd[k] = max(np.percentile(dTestToResult, 95), np.percentile(dResultToTest, 95))
return hd
def getVS(testImage, resultImage):
"""Volume similarity.
VS = 1 - abs(A - B) / (A + B)
A = ground truth in ML
B = participant segmentation in ML
"""
# Compute statistics of both images
testStatistics = sitk.StatisticsImageFilter()
resultStatistics = sitk.StatisticsImageFilter()
vs = dict()
for k in labels.keys():
testStatistics.Execute(sitk.BinaryThreshold(testImage, k, k, 1, 0))
resultStatistics.Execute(sitk.BinaryThreshold(resultImage, k, k, 1, 0))
numerator = abs(testStatistics.GetSum() - resultStatistics.GetSum())
denominator = testStatistics.GetSum() + resultStatistics.GetSum()
if denominator > 0:
vs[k] = 1 - float(numerator) / denominator
else:
vs[k] = None
return vs
if __name__ == "__main__":
do()