nnUnet肾脏肿瘤分割实战(KiTS19)

nnUnet肾脏肿瘤分割实战

1.数据获取

3D Slicer查看图片(casse_00023)

KiTS19是肾脏肿瘤分割挑战赛，包括300例病人。

其中有标签的210例作为训练样本（训练集），无标签的90例作为客观模型评估（测试集）。

原始数据集下载方法：

github官网下载 – kits19: The official repository of the 2019 Kidney and Kidney Tumor Segmentation Challenge
百度飞桨的公共数据集 – Kits19肾脏肿瘤分割 - 飞桨AI Studio

找数据集的时候我校验过，百度飞桨和github上的数据集是一样的。

github官网下载比较慢，可使用wget命令直接从百度飞桨的数据集地址下载，网速非常快。

原始数据如下图所示，使用nnunet要求结构化的数据集，使用前进行一个简单处理

root@worker04:~/data# tree data/KiTS19/origin
data/KiTS19/origin
|-- case_00000
|   |-- imaging.nii.gz
|   `-- segmentation.nii.gz
|-- case_00001
|   |-- imaging.nii.gz
|   `-- segmentation.nii.gz
|-- case_00002
|   |-- imaging.nii.gz
|   `-- segmentation.nii.gz
|-- case_00003
|   |-- imaging.nii.gz
|   `-- segmentation.nii.gz
......

下面是我根据nnunet中的dataset_conversion/Task040_KiTS.py修改的代码

import os
import json
import shutildef save_json(obj, file, indent=4, sort_keys=True):with open(file, 'w') as f:json.dump(obj, f, sort_keys=sort_keys, indent=indent)def maybe_mkdir_p(directory):directory = os.path.abspath(directory)splits = directory.split("/")[1:]for i in range(0, len(splits)):if not os.path.isdir(os.path.join("/", *splits[:i+1])):try:os.mkdir(os.path.join("/", *splits[:i+1]))except FileExistsError:# this can sometimes happen when two jobs try to create the same directory at the same time,# especially on network drives.print("WARNING: Folder %s already existed and does not need to be created" % directory)def subdirs(folder, join=True, prefix=None, suffix=None, sort=True):if join:l = os.path.joinelse:l = lambda x, y: yres = [l(folder, i) for i in os.listdir(folder) if os.path.isdir(os.path.join(folder, i))and (prefix is None or i.startswith(prefix))and (suffix is None or i.endswith(suffix))]if sort:res.sort()return resbase = "/root/data/data/KiTS19/origin"
out = "/root/data/nnUNet_raw_data_base/nnUNet_raw_data/Task040_KiTS"
cases = subdirs(base, join=False)maybe_mkdir_p(out)
maybe_mkdir_p(os.path.join(out, "imagesTr"))
maybe_mkdir_p(os.path.join(out, "imagesTs"))
maybe_mkdir_p(os.path.join(out, "labelsTr"))for c in cases:case_id = int(c.split("_")[-1])if case_id < 210:shutil.copy(os.path.join(base, c, "imaging.nii.gz"), os.path.join(out, "imagesTr", c + "_0000.nii.gz"))shutil.copy(os.path.join(base, c, "segmentation.nii.gz"), os.path.join(out, "labelsTr", c + ".nii.gz"))else:shutil.copy(os.path.join(base, c, "imaging.nii.gz"), os.path.join(out, "imagesTs", c + "_0000.nii.gz"))print(case_id,' done!')json_dict = {}
json_dict['name'] = "KiTS"
json_dict['description'] = "kidney and kidney tumor segmentation"
json_dict['tensorImageSize'] = "4D"
json_dict['reference'] = "KiTS data for nnunet"
json_dict['licence'] = ""
json_dict['release'] = "0.0"
json_dict['modality'] = {"0": "CT",
}
json_dict['labels'] = {"0": "background","1": "Kidney","2": "Tumor"
}
json_dict['numTraining'] = 210
json_dict['numTest'] = 90
json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i, "label": "./labelsTr/%s.nii.gz" % i} for i incases[:210]]
json_dict['test'] = ["./imagesTs/%s.nii.gz" % i for i incases[210:]]save_json(json_dict, os.path.join(out, "dataset.json"))

这里只是对数据集进行一个拷贝和重命名，不对原始数据进行修改。

运行代码后，整理好的数据集结构如下：

nnUNet_raw_data_base/nnUNet_raw_data/Task040_KiTS
├── dataset.json
├── imagesTr
│   ├── case_00000_0000.nii.gz
│   ├── case_00001_0000.nii.gz
│   ├── ...├── imagesTs
│   ├── case_00210_0000.nii.gz
│   ├── case_00211_0000.nii.gz
│   ├── ...├── labelsTr
│   ├── case_00000.nii.gz
│   ├── case_00001.nii.gz
│   ├── ...

dataset.json文件保存了训练集图像、训练集标签、测试集图像等信息。

预处理阶段会根据dataset.json读取图像，如果想要剔除某个病例，直接在dataset.json修改就好。

{"description": "kidney and kidney tumor segmentation","labels": {"0": "background","1": "Kidney","2": "Tumor"},"licence": "","modality": {"0": "CT"},"name": "KiTS","numTest": 90,"numTraining": 210,"reference": "KiTS data for nnunet","release": "0.0","tensorImageSize": "4D","test": ["./imagesTs/case_00210.nii.gz","./imagesTs/case_00211.nii.gz",.....],"training": [{"image": "./imagesTr/case_00000.nii.gz","label": "./labelsTr/case_00000.nii.gz"},{"image": "./imagesTr/case_00001.nii.gz","label": "./labelsTr/case_00001.nii.gz"},......]
}

提前准备三个文件夹，分别存放数据集、预处理数据和训练结果，配置好环境变量，具体细节可以参考我的第一篇博文。

2.数据预处理

nnUnet可以读取CT图像的模态信息、体素间距、灰度分布，自动进行重采样、裁剪以及归一化。

nnUnet图像分割的自动方法配置(https://www.nature.com/articles/s41592-020-01008-z)

重采样

不同时期，不同仪器的CT扫描仪，采样得到的CT图像具有不同的空间分辨率，重采样的目的是将所有的病例采样到相同的空间分辨率（体素间距）。

nnUnet的数据预处理preprocess自带重采样，但我试过两次之后效果并不好，重采样之后的图像尺寸太大了，于是我按照冠军论文里的方法自己写了个重采样，将所有病例的体素间距重采样为 3.22 x 1.62 x 1.62.

另外，论文中有提到case15和case37标签的错误，本来打算去掉，不过后来我去KiTS19的github官网看了一下，官方已经作了修正。

import numpy as np
import SimpleITK as sitkdef transform(image,newSpacing, resamplemethod=sitk.sitkNearestNeighbor):# 设置一个Filterresample = sitk.ResampleImageFilter()# 初始的体素块尺寸originSize = image.GetSize()# 初始的体素间距originSpacing = image.GetSpacing()newSize = [int(np.round(originSize[0] * originSpacing[0] / newSpacing[0])),int(np.round(originSize[1] * originSpacing[1] / newSpacing[1])),int(np.round(originSize[2] * originSpacing[2] / newSpacing[2]))]print('current size:',newSize)# 沿着x,y,z,的spacing（3）# The sampling grid of the output space is specified with the spacing along each dimension and the origin.resample.SetOutputSpacing(newSpacing)# 设置originalresample.SetOutputOrigin(image.GetOrigin())# 设置方向resample.SetOutputDirection(image.GetDirection())resample.SetSize(newSize)# 设置插值方式resample.SetInterpolator(resamplemethod)# 设置transformresample.SetTransform(sitk.Euler3DTransform())# 默认像素值   resample.SetDefaultPixelValue(image.GetPixelIDValue())return resample.Execute(image)

注意重采样的插值方法，我试过SimpleITK自带的多种插值方法，线性插值,三次插值以及B样条，比较发现B样条的效果是最好的。

因此，image采用sitk.sitkBSpline插值，segment采用sitk.sitkNearestNeighbor插值。

如果感兴趣可以自己尝试一下不同的插值方法，或者使用scipy等其他工具包进行重采样。

data_path = "/root/data/nnUNet_raw_data_base/nnUNet_raw_data/Task040_KiTS/imagesTr"for path in sorted(os.listdir(data_path)):print(path)img_path = os.path.join(data_path,path)img_itk = sitk.ReadImage(img_path)print('origin size:', img_itk.GetSize())new_itk = transform(img_itk, [3.22, 1.62, 1.62], sitk.sitkBSpline) # sitk.sitkLinearsitk.WriteImage(new_itk, img_path)print('images is resampled!')
print('-'*20)label_path = "/root/data/nnUNet_raw_data_base/nnUNet_raw_data/Task040_KiTS/labelsTr"for path in sorted(os.listdir(label_path)):print(path)img_path = os.path.join(label_path,path)img_itk = sitk.ReadImage(img_path)print('origin size:', img_itk.GetSize())new_itk = transform(img_itk, [3.22, 1.62, 1.62])sitk.WriteImage(new_itk, img_path)print('labels is resampled!')

下面开始介绍nnUnet的数据预处理方法：

输入指令：

 python  nnunet/experiment_planning/nnUNet_plan_and_preprocess.py -t 40 --verify_dataset_integrity

verify_dataset_integrity这里不再赘述，主要是根据验证数据集结构，第一次运行的时候最好还是加上。

裁剪

裁剪的目的是裁去黑边，减少像素值为0的边缘区域，裁剪的时候保持空间分辨率等信息不变。

def crop(task_string, override=False, num_threads=default_num_threads):# 输出目录：'/root/data/nnUNet_raw_data_base/nnUNet_cropped_data/Task040_KiTS'cropped_out_dir = join(nnUNet_cropped_data, task_string)maybe_mkdir_p(cropped_out_dir)if override and isdir(cropped_out_dir):shutil.rmtree(cropped_out_dir)maybe_mkdir_p(cropped_out_dir)splitted_4d_output_dir_task = join(nnUNet_raw_data, task_string)lists, _ = create_lists_from_splitted_dataset(splitted_4d_output_dir_task)  # 创建裁剪列表imgcrop = ImageCropper(num_threads, cropped_out_dir)imgcrop.run_cropping(lists, overwrite_existing=override)shutil.copy(join(nnUNet_raw_data, task_string, "dataset.json"), cropped_out_dir)

create_lists_from_splitted_dataset加载所有的训练集的图像地址，lists一共有210个元素，每个元素包含图像和标签。

def create_lists_from_splitted_dataset(base_folder_splitted):lists = []json_file = join(base_folder_splitted, "dataset.json")with open(json_file) as jsn:d = json.load(jsn)training_files = d['training']num_modalities = len(d['modality'].keys())for tr in training_files:cur_pat = []for mod in range(num_modalities):cur_pat.append(join(base_folder_splitted, "imagesTr", tr['image'].split("/")[-1][:-7] +"_%04.0d.nii.gz" % mod))cur_pat.append(join(base_folder_splitted, "labelsTr", tr['label'].split("/")[-1]))lists.append(cur_pat)return lists, {int(i): d['modality'][str(i)] for i in d['modality'].keys()}

重点是这两个函数：

    imgcrop = ImageCropper(num_threads, cropped_out_dir)imgcrop.run_cropping(lists, overwrite_existing=override)

ImageCropper是一个类，包含10个方法。

重点是crop和run_cropping两个方法:

crop：裁剪到非零区域，返回data, seg, properties
run_cropping：执行裁剪操作，并且将结果保存为.npz文件(包含data和seg)，将size, spacing, origin, classes, size_after_cropping 等属性保存在.pkl文件。

但是执行代码时，发现裁剪前后尺寸没有变化，可能是因为图像没有什么黑边

    # 裁剪的时候seg!=Nonedef crop(data, properties, seg=None):shape_before = data.shape  # 原始尺寸data, seg, bbox = crop_to_nonzero(data, seg, nonzero_label=-1)  # 裁剪结果shape_after = data.shape  # 裁剪尺寸print("before crop:", shape_before, "after crop:", shape_after, "spacing:",np.array(properties["original_spacing"]), "\n")properties["crop_bbox"] = bboxproperties['classes'] = np.unique(seg)seg[seg < -1] = 0properties["size_after_cropping"] = data[0].shapereturn data, seg, properties

数据分析

收集上一步裁剪得到的图像信息（尺寸、体素间距、灰度分布），为当前任务制定合适的训练计划(plan)

        # '/root/data/nnUNet_raw_data_base/nnUNet_cropped_data/Task040_KiTS'cropped_out_dir = os.path.join(nnUNet_cropped_data, t)# '/root/data/nnUNet_preprocessed/Task040_KiTS'preprocessing_output_dir_this_task = os.path.join(preprocessing_output_dir, t)# we need to figure out if we need the intensity propoerties. We collect them only if one of the modalities is CTdataset_json = load_json(join(cropped_out_dir, 'dataset.json'))modalities = list(dataset_json["modality"].values())collect_intensityproperties = True if (("CT" in modalities) or ("ct" in modalities)) else Falsedataset_analyzer = DatasetAnalyzer(cropped_out_dir, overwrite=False, num_processes=tf)  # this class creates the fingerprint_ = dataset_analyzer.analyze_dataset(collect_intensityproperties)  # this will write output files that will be used by the ExperimentPlannermaybe_mkdir_p(preprocessing_output_dir_this_task)shutil.copy(join(cropped_out_dir, "dataset_properties.pkl"), preprocessing_output_dir_this_task)shutil.copy(join(nnUNet_raw_data, t, "dataset.json"), preprocessing_output_dir_this_task)

分析得到的dataset_properties.pkl结果如下：

创建数据指纹

根据上一步得到的数据集信息，针对不同的训练任务，制定合适的训练计划(plan)

         if planner_3d is not None:if args.overwrite_plans is not None:assert args.overwrite_plans_identifier is not None, "You need to specify -overwrite_plans_identifier"exp_planner = planner_3d(cropped_out_dir, preprocessing_output_dir_this_task, args.overwrite_plans,args.overwrite_plans_identifier)else:exp_planner = planner_3d(cropped_out_dir, preprocessing_output_dir_this_task)exp_planner.plan_experiment()if not dont_run_preprocessing:  # double negative, yoooexp_planner.run_preprocessing(threads)if planner_2d is not None:exp_planner = planner_2d(cropped_out_dir, preprocessing_output_dir_this_task)exp_planner.plan_experiment()if not dont_run_preprocessing:  # double negative, yoooexp_planner.run_preprocessing(threads)

预处理执行完毕，得到如下处理结果：

nnUNet_preprocessed文件夹下
|-- Task040_KiTS|-- dataset.json|-- dataset_properties.pkl|-- gt_segmentations|-- nnUNetData_plans_v2.1_2D_stage0|-- nnUNetData_plans_v2.1_stage0|-- nnUNetPlansv2.1_plans_2D.pkl|-- nnUNetPlansv2.1_plans_3D.pkl`-- splits_final.pkl

这里生成的文件都可以打开来看看，对预处理方法和数据指纹有一个了解

dataset.json在数据获取阶段产生
daset_properties为数据的 size, spacing, origin, classes, size_after_cropping 等属性
gt_segmentations为图像分割标签
nnUNetData_plans_v2.1_2D_stage0和nnUNetData_plans_v2.1_stage0是预处理后的数据集
splits_final.pkl是五折交叉验证划分的结果，一共210个病人，42为一折
nnUNetPlansv2.1_plans*.pkl为训练计划，参考官方文档中的edit_plans_files.md可进行编辑

以nnUNetPlansv2.1_plans_3D.pkl为例，

3.模型训练

一行代码开始训练，执行过程以及调参可以参考我的博客nnUnet代码解读–模型训练

python nnunet/run/run_training.py CONFIGURATION TRAINER_CLASS_NAME TASK_NAME_OR_ID FOLD  # 格式
python nnunet/run/run_training.py 3d_fullres nnUNetTrainerV2 40 1

训练开始后，训练日志和训练结果记录在nnUNet_trained_models/nnUNet/3d_fullres/Task040_KiTS文件夹下

UNetTrainer__nnUNetPlansv2.1├── fold_1│   ├── debug.json│   ├── model_best.model│   ├── model_best.model.pkl│   ├── model_final_checkpoint.model│   ├── model_final_checkpoint.model.pkl│   ├── postprocessing.json│   ├── progress.png│   ├── training_log_2022_5_4_12_06_14.txt│   ├── training_log_2022_5_5_10_30_05.txt│   ├── validation_raw│   └── validation_raw_postprocessed

训练过程Loss曲线以及在线计算的Dice曲线

这里我想补充一下nnUnet的评价指标

在线评价

下面这段代码是nnUnet计算dice值的方法

先对每张图像中的每个类别分别计算tp, fp, fn，再对一个batch内的所有图像的tp, fp, fn求和，同时对一个batch求dice

import numpy as np
import torchdef sum_tensor(inp, axes, keepdim=False):axes = np.unique(axes).astype(int)if keepdim:for ax in axes:inp = inp.sum(int(ax), keepdim=True)else:for ax in sorted(axes, reverse=True):inp = inp.sum(int(ax))return inpdef run_online_evaluation(output, target):# torch.Size([b,num_classes, 80, 160, 160]) torch.Size([b,1, 80, 160, 160])with torch.no_grad():num_classes = output.shape[1]output_softmax = torch.softmax(output,dim=1)output_seg = output_softmax.argmax(1)  # [b,80,160,160]target = target[:, 0]  # [b,80,160,160]axes = tuple(range(1, len(target.shape)))  # (1,2,..,num_classes)tp_hard = torch.zeros((target.shape[0], num_classes - 1)).to(output_seg.device.index)  # [b,num_classes-1]fp_hard = torch.zeros((target.shape[0], num_classes - 1)).to(output_seg.device.index)  # [b,num_classes-1]fn_hard = torch.zeros((target.shape[0], num_classes - 1)).to(output_seg.device.index)  # [b,num_classes-1]for c in range(1, num_classes):tp_hard[:, c - 1] = sum_tensor((output_seg == c).float() * (target == c).float(), axes=axes)fp_hard[:, c - 1] = sum_tensor((output_seg == c).float() * (target != c).float(), axes=axes)fn_hard[:, c - 1] = sum_tensor((output_seg != c).float() * (target == c).float(), axes=axes)# [b,num_classes-1] -> [num_classes-1,]tp_hard = tp_hard.sum(0, keepdim=False).detach().cpu().numpy()fp_hard = fp_hard.sum(0, keepdim=False).detach().cpu().numpy()fn_hard = fn_hard.sum(0, keepdim=False).detach().cpu().numpy()print(list((2 * tp_hard) / (2 * tp_hard + fp_hard + fn_hard + 1e-8)))print(list(tp_hard))print(list(fp_hard))print(list(fn_hard))if __name__ == '__main__':outputs = torch.randn(4,3,80,160)targets = torch.randint(0, 3, (4,1,80,160))run_online_evaluation(outputs,targets)

但是我觉得直接对一个batch累加求dice不够准确，因为不同图像的目标区域大小不同，目标区域大的图像对目标区域小的图像影响太大了。

比较好的评价方法是应该对batch内的每张图像分别求dice，然后求平均。

下面这段代码中，作者也提到：

训练过程中的在线评价，只是对Dice值的一个估计，并不能代表最终的dice.

整体思路就是把每个batch当做一张图像去求的dice，迭代一个epoch之后，再对每个batch的dice求平均。

验证时，每个epoch中batch的数量取决于num_val_batches_per_epoch

    def finish_online_evaluation(self):self.online_eval_tp = np.sum(self.online_eval_tp, 0)self.online_eval_fp = np.sum(self.online_eval_fp, 0)self.online_eval_fn = np.sum(self.online_eval_fn, 0)global_dc_per_class = [i for i in [2 * i / (2 * i + j + k) for i, j, k inzip(self.online_eval_tp, self.online_eval_fp, self.online_eval_fn)]if not np.isnan(i)]self.all_val_eval_metrics.append(np.mean(global_dc_per_class))self.print_to_log_file("Average global foreground Dice:", [np.round(i, 4) for i in global_dc_per_class])self.print_to_log_file("(interpret this as an estimate for the Dice of the different classes. This is not ""exact.)")self.online_eval_foreground_dc = []self.online_eval_tp = []self.online_eval_fp = []self.online_eval_fn = []

最终评价

模型训练完成后，对五折交叉验证的验证集进行评价

dataset_val存储了验证集的信息，包含data, seg, properties

for k in self.dataset_val.keys():properties = load_pickle(self.dataset[k]['properties_file'])fname = properties['list_of_data_files'][0].split("/")[-1][:-12]if overwrite or (not isfile(join(output_folder, fname + ".nii.gz"))) or \(save_softmax and not isfile(join(output_folder, fname + ".npz"))):data = np.load(self.dataset[k]['data_file'])['data']print(k, data.shape)data[-1][data[-1] == -1] = 0softmax_pred = self.predict_preprocessed_data_return_seg_and_softmax(data[:-1],do_mirroring=do_mirroring,  # Truemirror_axes=mirror_axes,  # 0,1,2use_sliding_window=use_sliding_window,  # Truestep_size=step_size,  # 0.5use_gaussian=use_gaussian,  # Trueall_in_gpu=all_in_gpu,  # Falsemixed_precision=self.fp16)[1]  # fp16=True

在线评价时，每个epoch从训练集中取一定数量的batch，取样的patch_size为(80,160,160)，计算dice以及tp,fp,fn

最终评价时，对划分的验证集的每个图像用patch_size大小的滑动窗口进行评价，每个图像是经过充分评价的。

核心是predict_preprocessed_data_return_seg_and_softmax函数，输出验证集的分割结果以及summary.json文件。

这里的dice等评价指标才是验证集的真实评价指标

 "mean": {"0": {......},"1": {"Accuracy": 0.9993829712065982,"Dice": 0.9577956529884739,"False Discovery Rate": 0.049338979474340974,"False Negative Rate": 0.03426020473989496,"False Omission Rate": 0.000264596006662038,"False Positive Rate": 0.0003583155624936977,"Jaccard": 0.9195569582759517,"Negative Predictive Value": 0.9997354039933379,"Precision": 0.950661020525659,"Recall": 0.9657397952601052,"Total Positives Reference": 50782.54761904762,"Total Positives Test": 51469.26190476191,"True Negative Rate": 0.9996416844375062},"2": {"Accuracy": 0.9997035394427145,"Dice": 0.818755367440307,"False Discovery Rate": 0.1575802546022549,"False Negative Rate": 0.1761964196424669,"False Omission Rate": 0.00018421007671236777,"False Positive Rate": 0.00011484654734425636,"Jaccard": 0.7197449105231752,"Negative Predictive Value": 0.9998157899232878,"Precision": 0.8424197453977451,"Recall": 0.823803580357533,"Total Positives Reference": 19027.85714285714,"Total Positives Test": 18542.309523809523,"True Negative Rate": 0.9998851534526555}}

4.预测结果

nnUNet_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -t TASK_NAME_OR_ID -m CONFIGURATION --save_npz  # 格式

若以第二折交叉验证的结果进行预测，需要两个文件

fold_2/model_final_checkpoint.model为模型参数和权重
fold_2/model_final_checkpoint.model.pkl包含模型类别，训练计划(plan)

nnunet包

nnUNet_predict -i $nnUNet_raw_data_base/nnUNet_raw_data/Task040_KiTS/imagesTs/ -o OUTPUT_DIRECTORY -t 40 -m 3d_fullres -f 2

使用代码

python /nnunet/inference/predict_simple.py  -i $nnUNet_raw_data_base/nnUNet_raw_data/Task040_KiTS/imagesTs/ -o OUTPUT_DIRECTORY -t 40 -m 3d_fullres -f 2

INPUT_FOLDER：测试集所在目录
OUTPUT_FOLDER：输出目录，可自己指定

预测结果

KiTS19_predict/OUTPUT_DIRECTORY/
|-- plans.pkl
|-- case_00210.nii.gz
|-- case_00211.nii.gz
|-- ......

5.比赛提交

提交结果是有格式要求的，需要提交一个prediction.zip压缩包，其中的文件名为prediction_*.nii.gz

重命名

使用rename命令：

 rename "s/case/prediction/" *

执行完毕，文件名中的case被批量替换为prediction

KiTS19_predict/OUTPUT_DIRECTORY/
|-- plans.pkl
|-- prediction_00210.nii.gz
|-- prediction_00211.nii.gz
|-- ......

打包文件

进入OUTPUT_DIRECTORY目录，命令行输入：

zip predictions.zip prediction_*.nii.gz

得到predictions.zip压缩包就可以提交了

我用nnUNetTrainerV2在第二折训练了90个epoch，当做最终训练结果预测测试集图像，提交了一次，排名200+，大家感兴趣的话也可以试试。

最近学会使用nnUnet训练自己的模型了，直接改网络确实比较麻烦，但还没想好怎么讲，后处理部分也还没看。