nnunet项目官方地址

MIC-DKFZ/nnUNet (github.com)

使用nnunet之前，建议先阅读两篇论文

nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

本文使用vscode-remote ssh远程配置nnunet包，然后使用KiTS19肾脏肿瘤分割数据集进行实现。

1.服务器环境

import torch
print(torch.__version__)  # torch版本查询
print(torch.version.cuda)  # cuda版本查询
print(torch.backends.cudnn.version())  # cudnn版本查询
print(torch.cuda.get_device_name(0))  #设备名

1.8.0 11.1 8805

NVIDIA A100-SXM4-40GB

2.nnunet安装

一步到位

pip install nnunet

3.数据准备

以KiTS19为例

原始数据集下载方法：

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

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 shutil


def 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.join
    else:
        l = lambda x, y: y
    res = [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 res


base = "../../../data/KiTS19/origin"  # 原始文件路径
out = "../../../nnUNet_raw_data_base/nnUNet_raw_data/Task040_KiTS"  # 结构化数据集目录
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"))

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'] = len(cases)  # 应该是210例
json_dict['numTest'] = 0
json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i, "label": "./labelsTr/%s.nii.gz" % i} for i in cases]
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、测试集图像信息

{
    "description": "kidney and kidney tumor segmentation",
    "labels": {
        "0": "background",
        "1": "Kidney",
        "2": "Tumor"
    },
    "licence": "",
    "modality": {
        "0": "CT"
    },
    "name": "KiTS",
    "numTest": 0,
    "numTraining": 210,
    "reference": "KiTS data for nnunet",
    "release": "0.0",
    "tensorImageSize": "4D",
    "test": [],
    "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"
        },
        ......
    ]
}

4.添加路径

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

  • nnUNet_raw_data_base
    • nnUNet_raw_data
      • Task005_Prostate
      • Task040_KiTS
  • nnUNet_preprocessed
  • nnUNet_trained_models

提前准备好数据集，放在nnUNet_raw_data_base/nnUNet_raw_data下面。 三个文件夹的路径一定要记清楚，nnunet的数据都在三个文件夹里面了
在这里插入图片描述

5.设置环境变量

以下两种方式都可

1.修改主目录中的 .bashrc 文件，通过文本编辑器或者vim修改

  • vim ~/.bashrc

  • 将以下代码添加到 .bashrc 文件的底部

修改成上一步建立的三个文件夹的路径

export nnUNet_raw_data_base="/media/fabian/nnUNet_raw_data_base"
export nnUNet_preprocessed="/media/fabian/nnUNet_preprocessed"
export RESULTS_FOLDER="/media/fabian/nnUNet_trained_models"

在这里插入图片描述

2.临时设置环境变量

直接在终端输入上面的三行代码，但每次关闭终端，变量会丢失，需要重新设置

成功设置后，键入echo $RESULTS_FOLDER 等验证路径
在这里插入图片描述

6.数据预处理

nnUNet_plan_and_preprocess -t XXX --verify_dataset_integrity

XXX是任务ID，verify_dataset_integrity是对结构化数据集做一个校验，第一次进行预处理的时候最好还是加上。

nnUNet_preprocessed文件夹下
|-- Task005_Prostate
|   |-- 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
`-- 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Task040_KiTS的原始数据在预处理之前，我按照冠军论文里的方法做了一个重采样，因此没有stage1。另外，我丢掉了两个错误病例，15号和37号，这样一共有210-2=208个病例。

我一开始也没有做重采样，直接扔到nnunet里面，结果发现nnunet的自动重采样不是那么靠谱，采样后的尺寸太大了。

这里生成的文件都可以打开来看看，对预处理方法和训练过程有一个了解

  • dataset.json就是第3步数据准备生成的

  • daset_properties为数据的 size, spacing, origin, classes, size_after_cropping 等属性

  • gt_segmentations为标签

  • 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208个病人，42为一折

  • nnUNetPlansv2.1_plans_3D.pkl

    • plan_path = "../../../nnUNet_preprocessed/Task040_KiTS/nnUNetPlansv2.1_plans_3D.pkl"
with open(plan_path,'rb') as f:
    plans = pickle.load(f)
print(plans['plans_per_stage'][0])

      {'batch_size': 12, 
'num_pool_per_axis': [4, 5, 5], 
'patch_size': array([ 80, 160, 160]), 
'median_patient_size_in_voxels': array([128, 247, 247]), 
'current_spacing': array([3.22000003, 1.62      , 1.62      ]), 
'original_spacing': array([3.22000003, 1.62      , 1.62      ]), 
'do_dummy_2D_data_aug': False, 
'pool_op_kernel_sizes': [[2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 
'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]]}
-------------------------------------------------------------------------------------------
batch_size默认为2，如果GPU内存足够，可以设置大一点
patch_size默认为[96,160,160]

      参考官方教程中的edit_plans_files.md文件

准备工作做好之后就可以开始训练了

7.模型训练

nnUNet_train CONFIGURATION TRAINER_CLASS_NAME TASK_NAME_OR_ID FOLD  # 格式
nnUNet_train 3d_fullres nnUNetTrainerV2 40 2

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

Task040_KiTS/
|-- nnUNetTrainerV2__nnUNetPlansv2.1
|   |-- fold_2
|   |   |-- debug.json
|   |   |-- model_best.model
|   |   |-- model_best.model.pkl
|   |   |-- model_latest.model
|   |   |-- model_latest.model.pkl
|   |   |-- progress.png
|   |   `-- training_log_2022_4_27_09_06_59.txt
|   `-- plans.pkl

training_log_2022_4_27_09_06_59.txt

在这里插入图片描述

batch_size=12，patch_size=（80,160,160）时，GPU使用情况
在这里插入图片描述

progress.png
在这里插入图片描述

部分训练结果

2022-04-28 00:40:45.481503: 
epoch:  59 
2022-04-28 00:55:54.086779: train loss : -0.7443 
2022-04-28 00:56:32.347733: validation loss: -0.7275 
2022-04-28 00:56:32.349458: Average global foreground Dice: [0.9517, 0.9154] 
2022-04-28 00:56:32.349963: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
2022-04-28 00:56:33.177943: lr: 0.009458 
2022-04-28 00:56:33.213631: saving checkpoint... 
2022-04-28 00:56:33.665359: done, saving took 0.49 seconds 
2022-04-28 00:56:33.680583: This epoch took 948.198634 s
 
2022-04-28 00:56:33.681018: 
epoch:  60 
2022-04-28 01:11:42.852178: train loss : -0.7372 
2022-04-28 01:12:21.286995: validation loss: -0.7232 
2022-04-28 01:12:21.288569: Average global foreground Dice: [0.9528, 0.8968] 
2022-04-28 01:12:21.289036: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
2022-04-28 01:12:22.120225: lr: 0.009449 
2022-04-28 01:12:22.120799: This epoch took 948.439400 s
 
2022-04-28 01:12:22.121174: 
epoch:  61 
2022-04-28 01:27:31.129683: train loss : -0.7454 
2022-04-28 01:28:09.405324: validation loss: -0.7357 
2022-04-28 01:28:09.406955: Average global foreground Dice: [0.9532, 0.9338] 
2022-04-28 01:28:09.407326: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
2022-04-28 01:28:10.347231: lr: 0.00944 
2022-04-28 01:28:10.373067: saving checkpoint... 
2022-04-28 01:28:10.841225: done, saving took 0.49 seconds 
2022-04-28 01:28:10.855461: This epoch took 948.733870 s
......

训练结果出乎意料的好，没想到我把数据集重采样之后效果提高了这么多

nnunet包容易上手，但调参不方便，也不能修改网络结构导入自己的模型，接下来想在实验室的服务器上使用nnunet代码，方便以后阅读源码、调参以及导入自己的模型。

生物医学工程专业，研一小硕一枚，接触深度学习和图像分割半年。人生第一次写博客，记录一下自己的学习过程，若有错误，请多指教，与君共勉~~

# 深度学习 # 图像处理 # pytorch
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