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‎dataprocess/Augmain.py‎

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from dataprocess.Augmentation.ImageAugmentation import DataAug3D
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if __name__ == '__main__':
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aug = DataAug3D(rotation=5, width_shift=0.01, height_shift=0.01, depth_shift=0, zoom_range=0,
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aug = DataAug3D(rotation=10, width_shift=0.01, height_shift=0.01, depth_shift=0, zoom_range=0,
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vertical_flip=True, horizontal_flip=True)
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aug.DataAugmentation('data/traindata.csv', 15, aug_path='D:\challenge\data\KiPA2022\\trainstage\\augtrain/')
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aug.DataAugmentation('data/traindata.csv', 10, aug_path='D:\challenge\data\KiPA2022\\trainstage\\augtrain/')

‎dataprocess/__pycache__/utils.cpython-36.pyc‎

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‎dataprocess/data/trainaugdata.csv‎

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‎dataprocess/data/traindata.csv‎

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Image,Mask
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D:\challenge\data\KiPA2022\trainstage\train/Image/0.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/0.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/1.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/1.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/10.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/10.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/11.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/11.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/12.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/12.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/13.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/13.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/14.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/14.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/15.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/15.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/16.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/16.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/17.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/17.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/18.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/18.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/19.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/19.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/2.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/2.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/20.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/20.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/21.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/21.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/22.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/22.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/23.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/23.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/24.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/24.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/25.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/25.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/26.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/26.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/27.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/27.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/28.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/28.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/29.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/29.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/3.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/3.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/30.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/30.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/31.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/31.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/32.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/32.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/33.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/33.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/34.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/34.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/35.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/35.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/36.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/36.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/37.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/37.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/38.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/38.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/39.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/39.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/4.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/4.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/40.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/40.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/41.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/41.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/42.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/42.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/43.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/43.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/44.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/44.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/45.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/45.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/46.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/46.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/47.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/47.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/48.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/48.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/49.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/49.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/5.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/5.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/50.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/50.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/51.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/51.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/52.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/52.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/53.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/53.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/54.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/54.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/55.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/55.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/56.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/56.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/57.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/57.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/58.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/58.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/59.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/59.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/6.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/6.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/60.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/60.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/61.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/61.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/62.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/62.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/63.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/63.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/64.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/64.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/7.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/7.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/8.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/8.npy
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D:\challenge\data\KiPA2022\trainstage\train/Image/9.npy,D:\challenge\data\KiPA2022\trainstage\train/Mask/9.npy

‎dataprocess/data/validata.csv‎

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Image,Mask
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D:\challenge\data\KiPA2022\trainstage\validation/Image/0.npy,D:\challenge\data\KiPA2022\trainstage\validation/Mask/0.npy
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D:\challenge\data\KiPA2022\trainstage\validation/Image/1.npy,D:\challenge\data\KiPA2022\trainstage\validation/Mask/1.npy
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D:\challenge\data\KiPA2022\trainstage\validation/Image/2.npy,D:\challenge\data\KiPA2022\trainstage\validation/Mask/2.npy
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D:\challenge\data\KiPA2022\trainstage\validation/Image/3.npy,D:\challenge\data\KiPA2022\trainstage\validation/Mask/3.npy
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D:\challenge\data\KiPA2022\trainstage\validation/Image/4.npy,D:\challenge\data\KiPA2022\trainstage\validation/Mask/4.npy

‎dataprocess/data/validata1.csv‎

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Image,Mask
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D:\challenge\data\KiPA2022\trainstage\validation/Image/0.npy,D:\challenge\data\KiPA2022\trainstage\validation/Mask/0.npy

‎dataprocess/说明文档.txt‎

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1、统计数据平均大小mean size,mean spacing: (array([153.29230769, 153.29230769, 197.49230769]), array([0.63416487, 0.63416487, 0.63416487]))
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2、图像缩放到固定大小（112x112x128）
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3、归一化采用（5，95）归一化范围
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4、损失采用focalloss

‎inference.py‎

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import torch
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import os
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from model import *
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from dataprocess.utils import file_name_path
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import SimpleITK as sitk
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# Use CUDA
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os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
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use_cuda = torch.cuda.is_available()
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def inferencemutilunet3dtest():
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newSize = (112, 112, 128)
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Unet3d = MutilUNet3dModel(image_depth=128, image_height=112, image_width=112, image_channel=1, numclass=1,
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batch_size=1, loss_name='MutilFocalLoss', inference=True,
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model_path=r'log\MutilUNet3d\focalloss\BinaryVNet2dSegModel.pth')
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datapath = r"F:\MedicalData\(ok)2022KiPA\dataset\test\image"
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makspath = r"F:\MedicalData\(ok)2022KiPA\dataset\test\label"
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image_path_list = file_name_path(datapath, False, True)
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for i in range(len(image_path_list)):
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imagepathname = datapath + "/" + image_path_list[i]
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sitk_image = sitk.ReadImage(imagepathname)
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sitk_mask = Unet3d.inference(sitk_image, newSize)
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maskpathname = makspath + "/" + image_path_list[i]
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sitk.WriteImage(sitk_mask, maskpathname)
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if __name__ == '__main__':
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inferencemutilunet3dtest()

‎model/__pycache__/losses.cpython-36.pyc‎

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‎model/__pycache__/metric.cpython-36.pyc‎

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