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Sharpness-aware Low Dose CT Denoising Using Conditional Generative Adversarial Network
I enjoyed reading your high-quality paper. I think it will be very helpful for my task.
But in the mean time, I have a question about "sharpness detection network".
There seems to be a reason for additionally training the sharpness network instead of using the LBP-based sharpness metric directly for loss calculation.
May I know the reason for that? Or, if you have any experiments, please let me know, I would be very grateful.
Thanks
if I want to train the model by my dataset ,what should I do?
The link you provide on your personal webpage could't be opened, so how can I get the dataset?
您好,我在复现您的工作的时候,按照您GitHub上的步骤来进行,install torch和其他packages,然后在clone项目时是直接下载下来放到服务器上(一个sagan-master的文件夹),接着我不能Download the pretrained denoising model ,因为链接在国内失效了,我猜测;接着Prepare test set成功,显示:318 files in the folder;but 当我 test the model时,显示: DATA_ROOT: command not found,我在想是不是预训练模型的问题,不知道您有没有解决办法,谢谢!
RT.
I wonder why training an independent network for sharpness detection? Why not calculate the sharpness difference between synthesized images and real images and then add the sharpness loss to objective function?
Which are the 850 images mentioned in your paper?
Inside each experiment folder (SE0, SE1....SE28), there are 906 images. So to get the exact 850 images, you need to, first, reordering the image sequence according to the [SliceLocation] field of the Dicom image (sort in ascending order) and you will get images arranged from pelvis to head. Then just keeping slice 21 to 870 and discard the rest which has almost no content.
What is the train/test split?
After obtaining the 850 ordered images, test images were selected at an interval of 6, i.e. slice 1, 7, 13 … 847.
How do you compute the PSNR and SSIM for the simulated and real dataset?
For the simulated data, the generated noisy image was converted to uint8 using the abdomen window (center: 40, width: 400). The training and evaluation were all conducted using these narrow range 8 bit images.
For the piglet data, the original 16 bit value was used. The training and evaluation was on 16 bit images.
What should I do when my test dicom data is not uint16?
In this case, the correct way to convert the data in preprocess.py is ([Hounsfield units]+1024)*22
torch/install/share/lua/5.1/hdf5/ffi.lua:42: Error: unable to locate HDF5 header file at hdf5.h
stack traceback:
[C]: in function 'error'
/home/hejian/torch/install/share/lua/5.1/trepl/init.lua:389: in function 'require'
test.lua:10: in main chunk
[C]: in function 'dofile'
...jian/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:150: in main chunk
[C]: at 0x00405d50
请问,你有遇到吗?是怎样处理的,我在Ubuntu下尝试运行的?网上的方法试了不少,还是报错...
DATA_ROOT=./datasets/experiment name=SAGAN which_direction=AtoB phase=test th test.lua
{
input_nc : 3
results_dir : "./results/"
Size : 512
batchSize : 1
phase : "test"
fineSize : 512
aspect_ratio : 1
how_many : "all"
gpu : 1
nThreads : 1
DATA_ROOT : "./datasets/experiment"
serial_batch_iter : 1
preprocess : "regular"
norm : "batch"
which_epoch : "latest"
name : "SAGAN"
cudnn : 1
serial_batches : 1
flip : 0
output_nc : 3
which_direction : "AtoB"
checkpoints_dir : "/home/angus/Documents/project/Medical_image/SAGAN/checkpoints"
display_id : 200
display : 0
}
Random Seed: 7774
#threads...1
Starting donkey with id: 1 seed: 7775
table: 0x41c20428
./datasets/experiment
trainCache /home/angus/Documents/rfid-project/Medical_image/SAGAN/cache/_home_angus_Documents_project_Medical_image_SAGAN_datasets_experiment_test_trainCache.t7
Creating train metadata
serial batch:, 1
table: 0x41dc8138
running "find" on each class directory, and concatenate all those filenames into a single file containing all image paths for a given class
now combine all the files to a single large file
load the large concatenated list of sample paths to self.imagePath
cmd..wc -L '/tmp/lua_RsZajh' |cut -f1 -d' '
64 samples found..... 0/64 .....................] ETA: 0ms | Step: 0ms
Updating classList and imageClass appropriately
[=================== 1/1 =====================>] Tot: 0ms | Step: 0ms
Cleaning up temporary files
Dataset Size: 64
checkpoints_dir /home/angus/Documents/project/Medical_image/SAGAN/checkpoints
/home/angus/torch/install/bin/luajit: /home/angus/torch/install/share/lua/5.1/threads/threads.lua:315: /home/angus/torch/install/share/lua/5.1/threads/threads.lua:183: [thread 1 callback] /home/angus/torch/install/share/lua/5.1/dok/inline.lua:738: <image.scale> could not find valid dest size
stack traceback:
[C]: in function 'error'
/home/angus/torch/install/share/lua/5.1/dok/inline.lua:738: in function 'error'
/home/angus/torch/install/share/lua/5.1/image/init.lua:718: in function 'scale'
.../project/Medical_image/SAGAN/data/donkey_folder.lua:40: in function 'preprocessAandB'
.../project/Medical_image/SAGAN/data/donkey_folder.lua:256: in function 'sampleHookTrain'
...uments/project/Medical_image/SAGAN/data/dataset.lua:342: in function 'getByClass'
...uments/project/Medical_image/SAGAN/data/dataset.lua:375: in function <...uments/project/Medical_image/SAGAN/data/dataset.lua:367>
[C]: in function 'xpcall'
/home/angus/torch/install/share/lua/5.1/threads/threads.lua:234: in function 'callback'
/home/angus/torch/install/share/lua/5.1/threads/queue.lua:65: in function </home/angus/torch/install/share/lua/5.1/threads/queue.lua:41>
[C]: in function 'pcall'
/home/angus/torch/install/share/lua/5.1/threads/queue.lua:40: in function 'dojob'
[string " local Queue = require 'threads.queue'..."]:15: in main chunk
Hello! I am trying to process my own dicom file using your Matlab code. However, I find the dicom file I get doesn't have the attribute PixelPaddingValue. I want to ask that is there another attribute to replace PixelPaddingValue? is this attribute necessary if I want to add poisson noise to the dicom file? Thanks!
Hi,
I have an issue when i tap this command "python pre_process.py -s 1 -i ./dicoms -o ./datasets/experiment/test" , prompt respond " warnings.warn(msg)
0 files in the folder" while i've lot of dicoms format images., and the root is good. Do you ve any idea of the problem ?
My laptop have only a AMD GPU...
Is it possible this made the error ?
There is a solution or a modification to run it with CPU ?
Thanks a lot
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