利用 python/keras/tensorflow 计算 DICE系数，评估 3D 分割结果，只挑选特定层面
之前在文章：https://blog.csdn.net/Sweety_Lin/article/details/104199580 中写的DICE计算程序，是将Nii的整个volume计算在内，现想提取T2中有lesion的特定层面来计算DICE：
import nibabel as nib
import scipy.io as io
import os
import numpy as np
import tensorflow as tf
from keras import backend as K
def dice_coefficient(y_true, y_pred, smooth=0.00001):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
pred_dir = '/home/share/20200207_precess_lactate/DICE_NEW/SELECT/Lac_mask'
true_dir = '/home/share/20200207_precess_lactate/DICE_NEW/SELECT/T2_mask'
pred_filenames = os.listdir(pred_dir)
pred_filenames.sort(key=lambda x:x[:-2])
true_filenames = os.listdir(true_dir)
true_filenames.sort(key=lambda x:x[:-8])
dice_value = np.zeros(18)
temp = []
for f in range(18):
pred_path = os.path.join(pred_dir, pred_filenames[f])
img_pred = nib.load(pred_path)
y_pred = img_pred.get_fdata()
true_path = os.path.join(true_dir, true_filenames[f])
img_true = nib.load(true_path)
y_true = img_true.get_fdata()
select = []
for slices in range(121):
if np.sum(y_true[:, :, slices]) > 0:
select.append(slices)
pass
pass
temp.append(dice_coefficient(y_true[:, :, select], y_pred[:, :, select]))
print(select)
pass
with tf.Session() as sess:
dice_value = sess.run(temp)
pass
mat_path = '/home/share/20200207_precess_lactate/DICE_NEW/SELECT/DICE_Lac.mat'
io.savemat(mat_path, {'DICE_value': dice_value})
作者：要奋斗的人儿~
