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nnUNet 训练 AMOS22数据集 Task216（抽丝剥茧指令+原理篇）

news 来源：原创 2023/3/25 5:44:33

环境准备篇

安装hiddenlayer（用来生成什么网络拓扑图？管他呢，装吧） pip install --upgrade git+https://github.com/nanohanno/hiddenlayer.git@bugfix/get_trace_graph#egg=hiddenlayer
安装环境，由于服务器已经装好pytorch哈哈，现在在setup.py里面，把pytorch删掉！其他按照指令： pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -e . （清华源快点哦

dataset_conversion篇

修改python Task216_Amos2022_task1.py，需要修改一下amos_base的路径，注意，这里是数据集的初始路径
其次，需要在root路径下面vim .bashrc并输入以下内容：
```
export nnUNet_raw_data_base="/root/autodl-tmp/amos22_raw"
export nnUNet_preprocessed="/root/autodl-tmp/amos22_pre"
export RESULTS_FOLDER="/root/autodl-tmp/nnUNet_trained_models"
```
注意看！第一行是格式化命名后的数据集，第二行是预处理后的数据集，第三行是训练的模型，这里都放在数据盘！

然后 source .bashrc
运行python Task216_Amos2022_task1.py，

目的：重新格式化文件名，全都化成这样的形式：

`nnUNet_plan_and_preprocess篇`

运行指令：

nnUNet_plan_and_preprocess -t 216 -pl3d ExperimentPlanner3D_residual_v21_bfnnUNet_31 -pl2d None

输入指令后，参数将传给 nnUNet_plan_and_preprocess.py 文件，然后会调用到三个函数：

crop(task_name, False, tf) #用于crop出有器官的地方，去除边界部分exp_planner.plan_experiment() #应该是用于调用模型exp_planner.run_preprocessing(threads) #用于进行预处理

我们输入的指令是：nnUNet_plan_and_preprocess -t 216 -pl3d ExperimentPlanner3D_residual_v21_bfnnUNet_31 -pl2d None

说明我们采用的planner是ExperimentPlanner3D_residual_v21_bfnnUNet_31

继承关系是ExperimentPlanner->ExperimentPlanner3D_v21->ExperimentPlanner3D_residual_v21_bfnnUNet->ExperimentPlanner3D_residual_v21_bfnnUNet_31,

这个预处理看起来很复杂，不要慌，我们一个一个往前看：

ExperimentPlanner3D_residual_v21_bfnnUNet_31

初始化：鉴别器，预处理器：Preprocessor3DBetterResampling

def __init__(self, folder_with_cropped_data, preprocessed_output_folder):super().__init__(folder_with_cropped_data, preprocessed_output_folder)self.data_identifier = "nnUNetData_bfnnUNet_31"self.plans_fname = join(self.preprocessed_output_folder,"nnUNetPlans_bfnnUNet_fabresnet_31_plans_3D.pkl")self.preprocessor_name = 'Preprocessor3DBetterResampling'

plan_experiments：继承了父类

run_preprocessing：

def run_preprocessing(self, num_threads):if os.path.isdir(join(self.preprocessed_output_folder, "gt_segmentations")):shutil.rmtree(join(self.preprocessed_output_folder, "gt_segmentations"))shutil.copytree(join(self.folder_with_cropped_data, "gt_segmentations"), join(self.preprocessed_output_folder,"gt_segmentations"))normalization_schemes = self.plans['normalization_schemes'] #设置模态，单/多模态use_nonzero_mask_for_normalization = self.plans['use_mask_for_norm'] #是否采取掩膜，是没必要的，因为task216 全是CTintensityproperties = self.plans['dataset_properties']['intensityproperties']preprocessor_class = recursive_find_python_class([join(nnunet.__path__[0], "preprocessing")],self.preprocessor_name, current_module="nnunet.preprocessing")assert preprocessor_class is not Nonepreprocessor = preprocessor_class(normalization_schemes, use_nonzero_mask_for_normalization,self.transpose_forward,intensityproperties) #创建预处理器target_spacings = [i["current_spacing"] for i in self.plans_per_stage.values()] #获取每阶段的spacing,stage0大概是[2,1.3,1.3],stage1大概是[1.5,1,1]if self.plans['num_stages'] > 1 and not isinstance(num_threads, (list, tuple)):num_threads = (default_num_threads, num_threads)elif self.plans['num_stages'] == 1 and isinstance(num_threads, (list, tuple)):num_threads = num_threads[-1]preprocessor.run(target_spacings, self.folder_with_cropped_data, self.preprocessed_output_folder,self.plans['data_identifier'], num_threads, force_separate_z=False)# 进行预处理

数据格式转换

每一个病人的数据都会被转换成一个四维numpy数组和一个pickle文件。

四维数组array(CXYZ)中，第一个维度的最后array[-1..]存储的是分割标注结果。而第一个维度的前面存储不同模态的数据，如果仅单模态，则四维数组第一维度长度仅为2，分别表示影像数据以及标注数据。四维数组array的后三个维度代表x,;.z三个坐标表示的三维数据，对于原始影像数据，值大小代表强度，而对于标注结果，后三个维度的三维数据值分别为0，1，…..表示不同的标注类别。

Preprocessor3DBetterResampling

该类继承GenericPreprocessor，所以直接看他的run函数：

可以看到首先初始化了input和output的路径，然后加载了一些config文件，调用了p.starmap(self._run_internal, all_args) ，这个_run_internal才是预处理的关键。

先对data和金标准进行transpose
根据target_spacing重采样和归一化
将data,seg组成all_data
期望每个类sample 10000 locations或样本数的0.01
保存class_locations
保存all_data

def _run_internal(self, target_spacing, case_identifier, output_folder_stage, cropped_output_dir, force_separate_z,all_classes):data, seg, properties = self.load_cropped(cropped_output_dir, case_identifier)data = data.transpose((0, *[i + 1 for i in self.transpose_forward]))seg = seg.transpose((0, *[i + 1 for i in self.transpose_forward]))# 首先进行transpose# 然后根据target_spacing进行重采样和归一化data, seg, properties = self.resample_and_normalize(data, target_spacing,properties, seg, force_separate_z)all_data = np.vstack((data, seg)).astype(np.float32)# we need to find out where the classes are and sample some random locations# let's do 10.000 samples per class# seed this for reproducibility!num_samples = 10000min_percent_coverage = 0.01 # at least 1% of the class voxels need to be selected, otherwise it may be too sparserndst = np.random.RandomState(1234)class_locs = {}for c in all_classes:all_locs = np.argwhere(all_data[-1] == c)if len(all_locs) == 0:class_locs[c] = []continuetarget_num_samples = min(num_samples, len(all_locs))target_num_samples = max(target_num_samples, int(np.ceil(len(all_locs) * min_percent_coverage)))selected = all_locs[rndst.choice(len(all_locs), target_num_samples, replace=False)]class_locs[c] = selectedprint(c, target_num_samples)properties['class_locations'] = class_locsprint("saving: ", os.path.join(output_folder_stage, "%s.npz" % case_identifier))np.savez_compressed(os.path.join(output_folder_stage, "%s.npz" % case_identifier),data=all_data.astype(np.float32))with open(os.path.join(output_folder_stage, "%s.pkl" % case_identifier), 'wb') as f:pickle.dump(properties, f)

归一化部分详解：resample and nomalized

回到Preprocessor3DBetterResampling，对于CT而言：调用numpy中的函数统计整个训练集的均值，标准差，0.5%分位HU值，99.5%分位HU值，并利用这些统计信息对每张图像进行clip以及z-scoring。代码中用use_nonzero_mask表示是否只在nonzero区域进行normalization.

  if scheme == "CT":# clip to lb and ub from train data foreground and use foreground mn and sd from training dataassert self.intensityproperties is not None, "ERROR: if there is a CT then we need intensity properties"mean_intensity = self.intensityproperties[c]['mean']std_intensity = self.intensityproperties[c]['sd']lower_bound = self.intensityproperties[c]['percentile_00_5']upper_bound = self.intensityproperties[c]['percentile_99_5']data[c] = np.clip(data[c], lower_bound, upper_bound)data[c] = (data[c] - mean_intensity) / std_intensityif use_nonzero_mask[c]:data[c][seg[-1] < 0] = 0

重采样部分详解:get_target_spacing+resample_patient+resample_data_or_seg

第一步是确定重采样的目标空间大小。在之前数据格式转换的时候，每个数据的spacing信息存储在对应的pickle文件中，需要依次进行读取，然后一起存放在一个列表spacings当中。之后调用numpy中函数统计每个维度spacing的中值即可。
接下来根据中值spacing进行判断，数据集是否存在各向异性的问题。nnUNet设定的判断标准是，中值spacing中三个维度，是否有一个维度spacing大于另一个维度spacing的3倍，并且，该维度的中值size小于另一个维度中值size的1/3。如果存在各向异性，对spacing特别大的维度，取数据集中该维度spacing值的10%分位点作为该维度的目标空间大小。

get_target_spacing中：

anisotropy_threshold = 3worst_spacing_axis = np.argmax(target)
other_axes = [i for i in range(len(target)) if i != worst_spacing_axis]
other_spacings = [target[i] for i in other_axes]has_aniso_spacing = target[worst_spacing_axis] > (anisotropy_threshold * min(other_spacings))
has_aniso_voxels = target_size[worst_spacing_axis] * self.anisotropy_threshold < min(other_sizes)
if has_aniso_spacing and has_aniso_voxels:spacings_of_that_axis = np.vstack(spacings)[:, worst_spacing_axis]target_spacing_of_that_axis = np.percentile(spacings_of_that_axis, 10)# don't let the spacing of that axis get higher than the other axesif target_spacing_of_that_axis < min(other_spacings):target_spacing_of_that_axis = max(min(other_spacings), target_spacing_of_that_axis) + 1e-5target[worst_spacing_axis] = target_spacing_of_that_axis

第二步：resample_patient中，根据target_spacing重新确定目标尺寸。

new_shape = np.round(((np.array(original_spacing) / np.array(target_spacing)).astype(float) * shape)).astype(int)

第三步：调用resample_data_or_seg，skimage库中的reisze函数对每张图像进行resize即可,但在nnUNet中会根据图像是否存在各向异性进行不同的resize策略。如果不存在各向异性，对整个三维图像进行3阶spline插值即可。如果图像存在各向异性，设spacing大的维度为z轴，则仅在图像的xy平面进行3阶spline插值，而在z轴采用最近邻插值。而对于分割的标注图像，无论各向异性与否,在三个维度上都采用最近邻插值。下面代码中，用do_seperate_z表示是否存在各向异性，用axis表示各向异性图像中spacing最大的轴。

resample_data_or_seg中：

from skimage.transform import resize
if do_separate_z:if axis == 0:new_shape_2d = new_shape[1:]elif axis == 1:new_shape_2d = new_shape[[0, 2]]else:new_shape_2d = new_shape[:-1]reshaped_final_data = []for c in range(data.shape[0]):reshaped_data = []for slice_id in range(shape[axis]):if axis == 0:reshaped_data.append(resize(data[c, slice_id], new_shape_2d, order=3))elif axis == 1:reshaped_data.append(resize(data[c, :, slice_id], new_shape_2d, order=3))else:reshaped_data.append(resize(data[c, :, :, slice_id], new_shape_2d, order=3))if shape[axis] != new_shape[axis]:# The following few lines are blatantly copied and modified from sklearn's resize()rows, cols, dim = new_shape[0], new_shape[1], new_shape[2]orig_rows, orig_cols, orig_dim = reshaped_data.shaperow_scale = float(orig_rows) / rowscol_scale = float(orig_cols) / colsdim_scale = float(orig_dim) / dimmap_rows, map_cols, map_dims = np.mgrid[:rows, :cols, :dim]map_rows = row_scale * (map_rows + 0.5) - 0.5map_cols = col_scale * (map_cols + 0.5) - 0.5map_dims = dim_scale * (map_dims + 0.5) - 0.5coord_map = np.array([map_rows, map_cols, map_dims])reshaped_final_data.append(map_coordinates(reshaped_data, coord_map, order=0, cval=cval,mode='nearest')[None])
else:reshaped = []for c in range(data.shape[0]):reshaped.append(resize(data[c], new_shape, order=3)[None])reshaped_final_data = np.vstack(reshaped)reshaped_seg = resize(seg, new_shape, order=0)

实验结果：

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