在這之前，要安裝一些常用的包：

         pip install nibabel----安裝用於讀寫影像資料檔案的程式包

         pip install --trusted-host pypi.python.org SimpleITK-----安裝ITK包

• 影象讀取

法1：

import SimpleITK as sitk #匯入itk
import numpy as np
import torch

.... 

imagenames = [os.path.join(datapath, 'patient%02d_C0.nii.gz' % i) for i in indcs] #影象路徑

....     
   
itkimage = sitk.ReadImage(imagenames[subjectid]) #讀取資料
numpyimage = sitk.GetArrayFromImage(itkimage) #轉為numpy
tensorimage = torch.from_numpy(numpyimage ).type(torch.FloatTensor) #轉為tensor

....
 

法2：

import numpy as np
import nibabel as nib

nibimage = nib.load(imagenames[subjectid])
imagedata = nibimage.get_data() 
header = nibimage.get_header() #讀取影象資訊，後面save result in NIFITY format要用到
ref_affine = nibimage.get_affine()
numpyimage = np.array(imagedata)

•   影象常用基本操作

tensor_label = tensor.squeeze(tensor_label) #對tensor_label所有維度為1的維度壓縮

tensor_label = tensor_label.squeeze(0) #可以在指定維度對numpy進行壓縮

torch.cat((torchimage1, torchimage2), dim=0) #可以在指定維度上對tensor進行拼接


out = np.squeeze(out.numpy(), axis=0) #可以在指定維度上對numpy進行壓縮
numpylabel = np.concatenate((numpylabel, pre), axis=2) #可以在指定維度上對numpy進行拼接


 

•   影象儲存

predictlabel = nib.Nifti1Image(predictnumpylabel, ref_affine, header)
savefold = os.path.join(Result_DIR_PATH, 'patient%02d_C0_predict.nii.gz' % (subjectid + 31)) #儲存路徑
nib.save(predictlabel, savefold)

•   如何輸出多個label?

#--------------training data的label處理--------------
numpy2Dlabel = (numpy2Dlabel == 200) * 1 + (numpy2Dlabel == 500) * 2 + (numpy2Dlabel == 600) * 3  #輸入label處理

#--------------loss function的定義--------------
loss_func = nn.CrossEntropyLoss() # the target label is NOT an one-hotted



#--------------網路結構的最後輸出--------------
outmax, a = torch.max(c10, dim=1, keepdim=True) #c10是網路的最後輸出一層
outmax = outmax.repeat(1, 4, 1, 1) #一共輸出4個label
c10 = c10 - outmax
out = torch.exp(c10)
outsum = torch.sum(out, dim=1, keepdim=True)
outsum = outsum.repeat(1, 4, 1, 1)
out = torch.div(out, outsum)
return out


#--------------test的時候對output處理--------------
out = test_net(image)

loss_output = loss(out, label) #計算predict error
loss_sum += loss_output.item()

out = out.data.cpu()
out = np.squeeze(out.numpy(), axis=0)

pre = np.argmax(out, axis=0) #最終predict的結果
 

•    如何自定義loss？

參考知乎上一個回答，做如下筆記: