Replies: 2 comments 3 replies
-
Hi, |
Beta Was this translation helpful? Give feedback.
-
Hi @Moron9645 ,
Thanks. |
Beta Was this translation helpful? Give feedback.
-
Hi, |
Beta Was this translation helpful? Give feedback.
-
Hi @Moron9645 ,
Thanks. |
Beta Was this translation helpful? Give feedback.
Uh oh!
There was an error while loading. Please reload this page.
Uh oh!
There was an error while loading. Please reload this page.
-
Hi everyone,
I am currently trying to use the 3D U-net implemented by MONAI to segment liver out of a series of CT images.
After several trials I have got some result from the model. However, I am still confused about how to set the parameters properly in RandCropByPosNegLabeld (spatial_size, num_samples) and sliding_window_inference (roi_size, sw_batch_size).
As far as I am concerned, to make inputs possess the same shape, RandCropByPosNegLabeld randomly cuts the image into spatial_size size crops, num_samples would be the numbers of the crops. Should I set spatial_size according to the size of liver ?
For example, if the size of the liver is (250,200,150), should I set the spatial_size like (256,256,256) or (128,128,128) ? Of course I have tried directly with (250,200,150), but I got an error about tensor size dismatch then, why should I get this error ?
Also, as far as I am concerned, the sliding_window_inference make predictions with a sliding window going through all the images and predict the results, roi_size being the size of the windows, and sw_batch_size being the number of the windows (when I set sw_batch_size to 4, will there be 4 windows slidng at the same time ?). Then also the same problem, should I set spatial_size according to the size of liver and should all the three axis lengths be the same ?
Here I have listed the shapes, spacings, and liver sizes of all the images, which size should be proper to adapt to the spatial_size and roi_size ?
Thanks a lot in advance !!
Beta Was this translation helpful? Give feedback.
All reactions