tbuikr / 3d-skipdenseseg Goto Github PK

At segmentor_v1.py line 100 i think you have a potential bug:

Code:

up_block = nn.ConvTranspose3d(num_features, num_classes, kernel_size=2 ** (i + 1) + 2, stride=2 ** (i + 1), padding=1, groups=num_classes, bias=False)

Bug:

while iterating with your settings: config[0] = 6, growth rate = 16 and num_init_features = 32 you will get the following out channels of the Dense Layers:

• 48
• 64
• 80
• 96
• 112
• 128

Let's say, i want to segment into 9 classes, and you set up_block = nn.ConvTranspose3d(... groups=num_classes ...) then in your code: num_features = 32 + 6 * 16 = 128 is correct for the first loop but 128 is not divisible by 9 and thus creating the following error: ValueError: in_channels must be divisible by groups

• Solution 1: remove the parameter num_groups and have more trainable parameters or
• Solution 2: calculate the variables based on the num_classes so that the division works

Did i miss something?

If this is intentional, then you might not let the user have to find the correct parameters. You could calculate them based on the num_classes 🙂

However, thanks for sharing 👍

Hi!
First of all, thank you very much for sharing your project. It's a great job, congratulations!

I would like to train your network with my own dataset. I've .nii MRI files, Sagital T2 and STIR. Each file (patient) has got several slices, between 12 and 16 (shape=[nslices, 256, 256]).

Could you tell me if would be possible to train it with your network?
And, in the event that it is possible, how must I organize the data for training, i.e., all the slices in one array, each file in one array...?
For example, if I use 60 patients (.nii files) to train, "mri_data_train" should be of size [60xnslices,256,256]?

Thank you very much in advance!

All the best :)