3d图像dice系数 pytorch代码
时间: 2023-11-22 09:50:04 浏览: 58
根据提供的引用内容,以下是使用PyTorch计算3D图像Dice系数的代码实现:
```python
import torch
import numpy as np
class DiceLoss(torch.nn.Module):
def __init__(self, include_background=True, to_onehot_y=True, softmax=True, reduction="mean"):
super(DiceLoss, self).__init__()
self.include_background = include_background
self.to_onehot_y = to_onehot_y
self.softmax = softmax
self.reduction = reduction
def forward(self, input, target):
if self.to_onehot_y:
target = torch.nn.functional.one_hot(target.long(), num_classes=input.shape[1]).permute(0, 4, 1, 2, 3).float()
if self.softmax:
input = torch.nn.functional.softmax(input, dim=1)
if not self.include_background:
input = input[:, 1:]
target = target[:, 1:]
numerator = 2 * torch.sum(input * target, dim=(0, 2, 3, 4))
denominator = torch.sum(input + target, dim=(0, 2, 3, 4))
dice = numerator / denominator
if self.reduction == "mean":
dice = torch.mean(dice)
elif self.reduction == "sum":
dice = torch.sum(dice)
return 1 - dice
# 生成随机数据
in_data = torch.randint(low=0, high=2, size=(1, 3, 50, 50, 50))
label = torch.randint(low=0, high=2, size=(1, 3, 50, 50, 50))
# 计算Dice系数
dice_loss = DiceLoss(include_background=False, to_onehot_y=False, softmax=False, reduction="mean")
print(in_data.shape)
print(dice_loss(in_data, label))
```