计算PET脑图像分割白质和灰质的Dice系数代码
时间: 2023-05-30 15:02:30 浏览: 102
以下是使用Python计算PET脑图像分割白质和灰质的Dice系数的代码:
```python
import numpy as np
# 读取PET图像分割结果和真实标签
pred = np.load('pred.npy') # 分割结果
label = np.load('label.npy') # 真实标签
# 计算白质和灰质的Dice系数
wm_pred = (pred == 1)
wm_label = (label == 1)
gm_pred = (pred == 2)
gm_label = (label == 2)
dice_wm = 2 * np.sum(wm_pred * wm_label) / (np.sum(wm_pred) + np.sum(wm_label))
dice_gm = 2 * np.sum(gm_pred * gm_label) / (np.sum(gm_pred) + np.sum(gm_label))
print("白质的Dice系数为:%f" % dice_wm)
print("灰质的Dice系数为:%f" % dice_gm)
```
其中,`pred.npy`为PET图像的分割结果,`label.npy`为真实标签。首先读取这两个数组,然后根据分割结果和真实标签计算白质和灰质的Dice系数。最后输出结果。
相关问题
计算PET脑nii图像分割白质和灰质的Dice系数代码
以下是一个Python代码示例,用于计算PET脑nii图像分割白质和灰质的Dice系数。
```
import numpy as np
import nibabel as nib
def dice_coef(y_true, y_pred, smooth=1):
intersection = np.sum(np.abs(y_true * y_pred))
return (2. * intersection + smooth) / (np.sum(np.square(y_true)) + np.sum(np.square(y_pred)) + smooth)
# Load the PET brain nii image and the ground truth segmentation mask
pet_nii = nib.load('pet_brain.nii')
seg_nii = nib.load('seg_mask.nii')
# Get the image data and the segmentation mask data as numpy arrays
pet_data = pet_nii.get_fdata()
seg_data = seg_nii.get_fdata()
# Normalize the PET data to have values between 0 and 1
pet_data = (pet_data - np.min(pet_data)) / (np.max(pet_data) - np.min(pet_data))
# Threshold the segmentation mask data to get binary masks for white matter and gray matter
white_matter_seg = np.where(seg_data == 1, 1, 0)
gray_matter_seg = np.where(seg_data == 2, 1, 0)
# Threshold the PET data to get binary masks for white matter and gray matter
white_matter_pet = np.where(pet_data >= 0.5, 1, 0)
gray_matter_pet = np.where(pet_data < 0.5, 1, 0)
# Calculate the Dice coefficients for white matter and gray matter segmentation
white_matter_dice = dice_coef(white_matter_seg, white_matter_pet)
gray_matter_dice = dice_coef(gray_matter_seg, gray_matter_pet)
# Print the results
print('White Matter Dice Coefficient:', white_matter_dice)
print('Gray Matter Dice Coefficient:', gray_matter_dice)
```
该代码首先加载PET脑nii图像和地面真实分割掩码,然后将它们转换为numpy数组。然后,PET数据被归一化到0到1之间的值,并使用阈值将分割掩码数据转换为二进制掩码,以获取白质和灰质掩码。接下来,使用定义的Dice系数函数计算白质和灰质的分割Dice系数。最后,代码将打印结果。
计算PET脑nii图像分割白质的Dice系数代码
以下是计算PET脑nii图像分割白质的Dice系数的Python代码示例:
```python
import numpy as np
import nibabel as nib
def dice_coef(im1, im2):
"""
计算Dice系数
:param im1: 分割结果
:param im2: 参考标准
:return: Dice系数
"""
intersection = np.sum(im1 * im2)
return (2. * intersection) / (np.sum(im1) + np.sum(im2))
# 读取PET脑nii图像和分割结果
img = nib.load('pet_brain.nii')
seg = nib.load('pet_brain_seg.nii')
# 获取图像数据
img_data = img.get_fdata()
seg_data = seg.get_fdata()
# 提取白质部分
white_matter = (seg_data == 2).astype(int)
# 计算Dice系数
dice = dice_coef(white_matter, img_data)
print('Dice系数为:', dice)
```
注:以上代码仅为示例,具体实现可能需要根据实际数据格式和需求进行调整。