black0017/medicalzoopytorch

A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

medicalzoopytorch's Issues

Data size into the model

Hi,
I would like to train using my own data. I have volume x,y,z and corresponding 1 segmentation with 99 classes. what is the data size that feeded into the model. since I don't have access into the data example, what is the size of data example used?

Hi, How to get this kind of results using highresnet?

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ValueError: low >= high

!python test_covid_ct.py --dataset_name covid_seg

2020-05-01 11:52:26.733364: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
COVID SEGMENTATION DATASET
0
Mode: train Subvolume samples to generate:  10  Volumes:  0
Traceback (most recent call last):
  File "test_covid_ct.py", line 76, in <module>
    main()
  File "test_covid_ct.py", line 27, in main
    path='.././datasets')
  File "/content/MedicalZooPytorch/lib/medloaders/__init__.py", line 79, in generate_datasets
    fold=args.fold_id, samples=samples_train, save=True)
  File "/content/MedicalZooPytorch/lib/medloaders/Covid_Segmentation_dataset.py", line 70, in __init__
    self.create_sub_volumes()
  File "/content/MedicalZooPytorch/lib/medloaders/Covid_Segmentation_dataset.py", line 96, in create_sub_volumes
    random_index = np.random.randint(total)
  File "mtrand.pyx", line 743, in numpy.random.mtrand.RandomState.randint
  File "_bounded_integers.pyx", line 1260, in numpy.random._bounded_integers._rand_int64
ValueError: low >= high

Example command lines for running COVID segmentation example?

Can you please provide the exact command lines to train and infer a segmentation model on the COVID dataset? I see you've provided data loaders, but it's not quite clear what commands to use.

Thanks.

full_volume.shape ==> "None type has no attribute shape"

Does anyone has solution to this

Data: brats2018 dataset

A bug in tests.test_vizual.py
Line: 37: visualize_3D_no_overlap_new(args, full_volume, affine, model, 10, args.dim)
This leads to lib/visual3D_temp/[viz.py]
Line 111: modalities, slices, height, width = full_volume.shape

"None type has no attribute shape"

Version 1.0

TODO:

• Restructure project
• Base classes
  • Base dataset class
  • Base augment class
• Inference with pretrained models
• Configuration systems
  • basic infrastructure
  • wire up with all medloaders/datasets and remove parse arg
• Logging
  • Create logger and inject everywhere
  • add more logging in the coder
• API
  • create a medzoo train
  • create a medzoo infer (we don't have inference yet)
• API docs
  • Sphinx setup
  • Update docstrings everywhere
• Medzoo documentation site
  • Create static site and wire up with API
  • Write docs content
  • Publish
• Package
  • setup.py
  • publish in pypi

question about train

When I used the mrbrains18 dataset for training, the following error occurred
Traceback (most recent call last):
File "./train_mrbrains_9_classes.py", line 81, in
main()
File "./train_mrbrains_9_classes.py", line 26, in main
path='/home/zwj/MedicalZooPytorch-master/datasets/mrbrains_2018/training')
File "/home/zwj/MedicalZooPytorch-master/lib/medloaders/init.py", line 50, in generate_datasets
split_id=0, samples=samples_train, load=args.loadData)
AttributeError: 'Namespace' object has no attribute 'loadData'

'Namespace' object has no attribute 'samples_train'

command:
!python test_miccai_2019.py --dataset_name MICCAI_2019_pathology_challenge --nEpochs 2 --opt adam --model UNET2D

output:

2020-05-01 07:37:33.986079: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
Traceback (most recent call last):
  File "test_miccai_2019.py", line 86, in <module>
    main()
  File "test_miccai_2019.py", line 34, in main
    path='.././datasets')
  File "/content/MedicalZooPytorch/lib/medloaders/__init__.py", line 17, in generate_datasets
    samples_train = args.samples_train
AttributeError: 'Namespace' object has no attribute 'samples_train'

updated command :
!python test_miccai_2019.py --dataset_name MICCAI_2019_pathology_challenge --nEpochs 2 --opt adam --model UNET2D --samples_train 10

ouput:

usage: test_miccai_2019.py [-h] [--batchSz BATCHSZ]
                           [--dataset_name DATASET_NAME] [--nEpochs NEPOCHS]
                           [--dim DIM [DIM ...]] [--classes CLASSES]
                           [--inChannels INCHANNELS]
                           [--inModalities INMODALITIES] [--fold_id FOLD_ID]
                           [--lr LR] [--cuda] [--resume PATH]
                           [--model {VNET,VNET2,UNET3D,DENSENET1,DENSENET2,DENSENET3,HYPERDENSENET,UNET2D}]
                           [--opt {sgd,adam,rmsprop}]
test_miccai_2019.py: error: unrecognized arguments: --samples_train 10

Other way around by altering the test_miccai_2019.py code:

medical_loaders.generate_datasets(args, path='.././datasets',samples_train=samples_train,
samples_val=samples_val)

Adding samples_train and Samples_val ,rises error in generate_dataset()

The error message "Expected 5-dimensional input for 5-dimensional weight [8, 3, 3, 3, 3], but got 4-dimensional input of size [4, 384, 128, 48] instead" indicates that there is a mismatch between the expected input dimensions (5-dimensional) and the actual input dimensions provided (4-dimensional).

After replacing the getitem method in line 77 of the file lib->medloaders>mrbrains2018.py with the following code, the error disappears and the training can proceed normally.

def __getitem__(self, index):
    t1_path, ir_path, flair_path, seg_path = self.list[index]
    t1_data, ir_data, flair_data, seg_data = np.load(t1_path), np.load(ir_path), np.load(flair_path), np.load(
        seg_path)

    return torch.FloatTensor(t1_data.copy()).unsqueeze(0), torch.FloatTensor(ir_data.copy()).unsqueeze(0), \
        torch.FloatTensor(flair_data.copy()).unsqueeze(0), torch.FloatTensor(seg_data.copy())

Low validation DSC when training on Brats2018

Hello, First of all, thanks for the amazing work.
Question: I tried to train the Unet3D model on bratS 2018 dataset using the 'train_brats_2018_new.py'. but I got lower validation DSC and higher validation loss, I think the model is facing some trouble on validation set:

The OutputTransition of the VNet model exists with one more than Conv3D

According to the structure diagram of the paper, the final OutputTransition should look like the following：

class OutputTransition(nn.Module):
    def __init__(self, in_channels, classes, elu):
        super(OutputTransition, self).__init__()
        self.classes = classes
        # self.conv1 = nn.Conv3d(in_channels, classes, kernel_size=5, padding=2)    # 修改
        self.conv1 = nn.Conv3d(in_channels, classes, kernel_size=1)
        self.bn1 = torch.nn.BatchNorm3d(classes)

        self.conv2 = nn.Conv3d(classes, classes, kernel_size=1)
        self.relu1 = ELUCons(elu, classes)

    def forward(self, x):
        out = self.relu1(self.bn1(self.conv1(x)))
        # out = self.conv2(out) # 修改
        return out

We simply use the only 111 convolutional layer to make the number of channels the same as the classes.

MICCAI_BraTS_2018_Data_Training/brats2018-list-' + mode + '-samples-' + str(samples) + '.txt'

When I run "brats2018.py", I do not know what should be in this file

self.save_name = self.root + '/MICCAI_BraTS_2018_Data_Training/brats2018-list-' + mode + '-samples-' + str(
samples) + '.txt'

Please help me.

RuntimeError: Expected 5-dimensional input for 5-dimensional weight 8 4 3 3, but got 4-dimensional input of size [4, 256, 64, 64] instead

Can you tell me why cone to it and how to solve it?
Traceback (most recent call last):
File "train_brats2018_new.py", line 75, in
main()
File "train_brats2018_new.py", line 33, in main
trainer.training()
File "/data/Disk_A/guoyang/mqp/MedicalZooPytorch-master/lib/train/trainer.py", line 38, in training
self.validate_epoch(epoch)
File "/data/Disk_A/guoyang/mqp/MedicalZooPytorch-master/lib/train/trainer.py", line 83, in validate_epoch
output = self.model(input_tensor)
File "/data/Disk_A/guoyang/.conda/envs/pt041/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in call
result = self.forward(*input, **kwargs)
File "/data/Disk_A/guoyang/mqp/MedicalZooPytorch-master/lib/medzoo/Unet3D.py", line 114, in forward
out = self.conv3d_c1_1(x)
File "/data/Disk_A/guoyang/.conda/envs/pt041/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in call
result = self.forward(*input, **kwargs)
File "/data/Disk_A/guoyang/.conda/envs/pt041/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 478, in forward
self.padding, self.dilation, self.groups)
RuntimeError: Expected 5-dimensional input for 5-dimensional weight 8 4 3 3, but got 4-dimensional input of size [4, 256, 64, 64] instead

About the format of using my own data

I would like to ask about the format of using your own data set, including how many bits of data it is and what data must be included in the folder

About

AttributeError: 'MRIDatasetMRBRAINS2018' object has no attribute 'affine'

when load processed data, there is a bug

        if load:
            ## load pre-generated data
            self.list = utils.load_list(self.save_name)
            return
     ...
        self.affine = img_loader.load_affine_matrix(list_reg_t1[0]) # it is a bug

AttributeError: 'Namespace' object has no attribute 'loadData'

Hi,

When trying to do inference using iseg2017 model.
I am getting below error

File "inference.py", line 80, in <module> main() File "inference.py", line 24, in main training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(args, File "/mnt/nas02/Sampledata_BrainVolumetry/brain_try/MedicalZoo/MedicalZooPytorch/lib/medloaders/__init__.py", line 28, in generate_datasets split_id=split_idx, samples=samples_train, load=args.loadData) AttributeError: 'Namespace' object has no attribute 'loadData'

When I check inference.py file its not taking any input argument for loadData

Something trouble with inference.py

Hello！
I have trained a segmentation model using my own datasets，there is nothing wrong with my valuation dataset and the dice is high.But when I use the 'non_overlap_padding' fuction in the inference.py ,the dice score is very low.I have tried to output the prediction image (image bellow),there might something wrong with the patches index.Would you mind help me to fix this problem?Thank you very much !

IO format

Hello,

How do I perform multi class semantic (instance) segmentation over my set of 2D PNG/TIF grayscale images and their corresponding masks file (NPY numpy array, TIF or any other) matrix representation with # channels corresponding to the classes?
Do I need to use NIFTI_interface? If so, how do I convert my PNG/TIF/NPY files to this format?

Thank you

pretrained models for COVID-19 CT segmentation

Hi @black0017 ,

Thanks for your great work.

I notice that you are also working on pre-trained models on public datasets.

Here is a benchmark for lung and infection segmentation in COVID-19 CT scans.

Are you interested in including this COVID-19 segmentation dataset for pre-trained models?

Best regards,
Jun

About img_nii shape in method 'load_medical_image'

When I read MRbrainS18 data and use 'load_medical_image' to get volume, I find that the shape of the volume is (240, 240, 48) -> (width, height, slice). However, the shape of the crop is (slice, width, height), Is it a bug？

 def load_medical_image(path, type=None, resample=None,
                       viz3d=False, to_canonical=False, rescale=None, normalization='full_volume_mean',
                       clip_intenisty=True, crop_size=(0, 0, 0), crop=(0, 0, 0), ):
    img_nii = nib.load(path)

    if to_canonical:
        img_nii = nib.as_closest_canonical(img_nii)

    if resample is not None:
        img_nii = resample_to_output(img_nii, voxel_sizes=resample)

    img_np = np.squeeze(img_nii.get_fdata(dtype=np.float32))

    if viz3d:
 >       return torch.from_numpy(img_np) # shape:(240,240,48), whether it is a bug?

Data directory structure

Hi,

How to perform 3D segmentation to the custom dataset with 3D volume and corresponding segmentation.
A dataset file structure might be helpful for iseg_2017 example.

Thanks

没看懂数据的结构

图像文件和标签文件的结构具体是什么样子的？？？？？

RuntimeError: Expected 5-dimensional input for 5-dimensional weight 8 4 3 3 3, but got 4-dimensional input of size [4, 256, 64, 64] instead

Hi,
When I want to train on BraTS2018, I use this command line
"python train_brats2018_new.py"

It shows this problem

Summary train Epoch 1: Loss:0.7025 DSC:29.7528 Background : 0.8263 NCR/NET : 0.0612 ED : 0.2336 ET : 0.0689
Traceback (most recent call last):
File "train_brats2018_new.py", line 75, in
main()
File "train_brats2018_new.py", line 33, in main
trainer.training()
File "/content/drive/My Drive/MedicalZooPytorch/lib/train/trainer.py", line 38, in training
self.validate_epoch(epoch)
File "/content/drive/My Drive/MedicalZooPytorch/lib/train/trainer.py", line 83, in validate_epoch
output = self.model(input_tensor)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "/content/drive/My Drive/MedicalZooPytorch/lib/medzoo/Vnet.py", line 150, in forward
out16 = self.in_tr(x)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "/content/drive/My Drive/MedicalZooPytorch/lib/medzoo/Vnet.py", line 58, in forward
out = self.conv1(x)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py", line 480, in forward
self.padding, self.dilation, self.groups)
RuntimeError: Expected 5-dimensional input for 5-dimensional weight 16 4 5 5 5, but got 4-dimensional input of size [4, 256, 64, 64] instead

Could you help me solve that?

Sharing pre-trained model weights.

Would you please share your pre-trained model weights (on any datasets)? Might be greatly helpful.