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FAQ regarding the usage of the dataset #8
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I have been searching for the datasets, would you send me the datasets? Thank you very much! |
You can download the dataset from the below link: |
Thank you very much! |
But how to reorder the image sequence according to the [SliceLocation] field of the Dicom image,could u give more detailed information,cuz when i try to preprocess,the code cant detect the dicom file |
That info is in the dicom header. It can be extracted using package like pydicom. I would suggest you to review some dicom basics before proceeding. |
i downloaded this link from onedrive. but it didnt unzip correctly. can you help me? |
Can you just try to redownload? I haven't get any issue like this before. |
i tried to unzip in both windows and ubuntu. But when in windows. it failed to start unzip when in ubuntu. it failed to unzip during process.
i tried to install torch7 in ubuntu 20.04. but it failed to install and tf didnt work. can i use pytorch instead of torch7? |
I would like to use this data set. However, when I jump to the link, the image does not exist. |
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I want to get the dataset. but, the warning happened ... please help me in getting the data |
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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
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