Tensorflow 2.3.1中的Keras ImageDataGenerator无法识别的DICOM文件

问题描述

我正在尝试读取文件夹中的DICOM（.dcm）文件：

/ rsna-肺炎检测挑战/ stage_2_train_images /

文件可读，可以使用pidicom库显示。但是，当我使用Keras ImageDataGenerator读取用于训练管道的文件时，会出现以下错误：

“ UnidentifiedImageError：无法识别图像文件<_io.bytesio>”

据我从详细的错误中了解到，Python PIL（枕头）无法识别文件格式“ .dcm”。

我需要两个见解：

是否可以使用Keras ImageDataGenerator读取DICOM文件以进行训练，而无需先将这些文件转换为其他格式，例如“ .png”？我在“ https://medium.com/@rragundez/medical-images-Now-supported-by-keras-imagedatagenerator-e67d1c2a1103”上阅读了以下博客，但令人惊讶地使用“ .PNG”作为示例。
是否有可能编写一个自定义生成器，该生成器首先从DICOM文件中提取像素信息，然后将其提取到训练管道中？

任何帮助将不胜感激。

下面给出了我的代码和错误消息。

import pandas as pd
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Dense,Activation,Flatten,Dropout,Batchnormalization
from tensorflow.keras.layers import Conv2D,MaxPooling2D
from tensorflow.keras import regularizers,optimizers

traindf=pd.read_csv(‘/rsna-pneumonia-detection-challenge/stage_2_train_labels.csv',dtype=str)
classdf=pd.read_csv('/rsna-pneumonia-detection-challenge/stage_2_detailed_class_info.csv',dtype=str)

tr100df = traindf[0:100] # take first 100 samples
tr100df.loc[:,'path'] = tr100df.patientId + '.dcm'

datagen=ImageDataGenerator(rescale=1./255.,validation_split=0.25)

train_generator=datagen.flow_from_dataframe(
dataframe=tr100df,directory="/rsna-pneumonia-detection-challenge/stage_2_train_images",x_col="path",y_col="Target",subset="training",batch_size=32,seed=42,shuffle=True,class_mode="binary",target_size=(32,32),mode='grayscale',validate_filenames=False)

for image_batch,labels_batch in train_generator:
  print(image_batch.shape)
  print(labels_batch.shape)
  image_np = image_batch.numpy()
  label_np = labels_batch.numpy()
  break

错误：

UnidentifiedImageError                    Traceback (most recent call last)
<ipython-input-66-9af954b10f7c> in <module>
----> 1 for image_batch,labels_batch in train_generator:
      2   print(image_batch.shape)
      3   print(labels_batch.shape)
      4   image_np = image_batch.numpy()
      5   label_np = labels_batch.numpy()

~/opt/anaconda3/lib/python3.8/site-packages/keras_preprocessing/image/iterator.py in __next__(self,*args,**kwargs)
    102 
    103     def __next__(self,**kwargs):
--> 104         return self.next(*args,**kwargs)
    105 
    106     def next(self):

~/opt/anaconda3/lib/python3.8/site-packages/keras_preprocessing/image/iterator.py in next(self)
    114         # The transformation of images is not under thread lock
    115         # so it can be done in parallel
--> 116         return self._get_batches_of_transformed_samples(index_array)
    117 
    118     def _get_batches_of_transformed_samples(self,index_array):

~/opt/anaconda3/lib/python3.8/site-packages/keras_preprocessing/image/iterator.py in _get_batches_of_transformed_samples(self,index_array)
    225         filepaths = self.filepaths
    226         for i,j in enumerate(index_array):
--> 227             img = load_img(filepaths[j],228                            color_mode=self.color_mode,229                            target_size=self.target_size,~/opt/anaconda3/lib/python3.8/site-packages/keras_preprocessing/image/utils.py in load_img(path,grayscale,color_mode,target_size,interpolation)
    112                           'The use of `load_img` requires PIL.')
    113     with open(path,'rb') as f:
--> 114         img = pil_image.open(io.BytesIO(f.read()))
    115         if color_mode == 'grayscale':
    116             # if image is not already an 8-bit,16-bit or 32-bit grayscale image

~/opt/anaconda3/lib/python3.8/site-packages/PIL/Image.py in open(fp,mode)
   2928     for message in accept_warnings:
   2929         warnings.warn(message)
-> 2930     raise UnidentifiedImageError(
   2931         "cannot identify image file %r" % (filename if filename else fp)
   2932     )

UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7f90a47e3b80>

解决方法

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medical-imaging python tensorflow tensorflow tensorflow tf.keras