dice computation

test_SLANT27_OASIS.py

import numpy as np
import nibabel as nii
import os
import glob
import time

os.environ["CUDA_VISIBLE_DEVICES"]='0'


def mean_dice_withoutbg(y_pred, y_true):
		acu=0
		n=0
		list=np.unique(y_true)
		for i in list:
			if(i==0):
				continue #avoid background
			a=(y_true==i)*1.0
			b=(y_pred==i)*1.0
			y_int = a[:]*b[:]
			acu=acu+(2*np.sum(y_int[:]) / (np.sum(a[:]) + np.sum(b[:])))
			n=n+1
		acu=acu/n
		return acu



def Dice_Native_Space_1mm(img_path, ref_path, result_path):

	labels_SLANT = [0, 4,11,23,30,31,32,35,36,37,38,39,40,41,44,45,47,48,49,50,51,52,55,56,57,58,59,60,61,62,71,72,73,75,76,100,101,102,103,104,105,106,107,108,109,112,113,114,115,116,117,118,119,120,121,122,123,124,125,128,129,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207]

	D=[]
	listT1 = sorted(glob.glob(img_path+"native_*.nii*"))
	listLAB = sorted(glob.glob(ref_path+"native_*seg.nii*"))
	listSEG = sorted(glob.glob(result_path+"native_*seg.nii*"))

	assert(len(listT1) == len(listLAB))
	assert(len(listT1) == len(listSEG))
        
	numfiles=len(listLAB)

	for i in range(0,numfiles):

		print(" ")
		print("Image", str(i+1))
		T1_filename=listLAB[i].replace("_seg.nii", ".nii")
		LAB_filename=listLAB[i]
		SEG_filename=listSEG[i]
		print(T1_filename)
		print(LAB_filename)
		print(SEG_filename)

		nii.Nifti1Header.quaternion_threshold = -8e-07
		LAB_img = nii.load(os.path.join(ref_path, LAB_filename))
		LAB=LAB_img.get_data()
		LAB=LAB.astype('int')
		#remove partial labels
		LABb = np.zeros(LAB.shape)
		for indexlab, lab in enumerate(labels_SLANT):
			ind=np.where(LAB==lab)
			LABb[ind] = LAB[ind]
		print('Removed inconsistent labels : ', list(set(np.unique(LAB)) - set(np.unique(LABb))))
		LAB = LABb


		SEG_img = nii.load(os.path.join(result_path, SEG_filename))
		SEG=SEG_img.get_data()
		SEG=SEG.astype('int')

		result=mean_dice_withoutbg(SEG, LAB)
		print("result=",result)
		D.append(result)


	# results
	print("")
	print("mean_dice=", np.mean(D))
	print("median_dice=", np.median(D))

	return D




img_path = "" #<path to OASIS T1 test images>
ref_path = "" #<path ot OASIS test images groundtruth segmentations>
result_path = "" #<path to OASIS test images SLANT27 segmentations>

natDOASIS = Dice_Native_Space_1mm(img_path, ref_path, result_path)