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我正在尝试读取位于文件夹中的 DICOM (.dcm) 文件：

/rsna-肺炎检测-挑战/stage_2_train_images/

这些文件是可读的，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 object at 0x7f90a47e3b80>”

正如我从详细错误中了解到的那样，Python PIL（Pillow）无法识别文件格式“.dcm”。

我需要两个见解：

  1. 是否可以使用 Keras ImageDataGenerator 读取用于训练管道的 DICOM 文件，而无需先将这些文件转换为其他格式，如“.png”？我在“https://medium.com/@rragundez/medical-images-now-supported-by-keras-imagedatagenerator-e67d1c2a1103”阅读了以下博客，但令人惊讶的是它使用“.PNG”作为示例。

  2. 是否可以编写一个自定义生成器，首先从 DICOM 文件中提取像素信息并将批次提取到训练管道？

任何帮助将不胜感激。

我的代码和错误信息如下。

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, *args, **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>
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