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liver segmentation using deep learning

Python 100.00%

pytorch liver-segmentation deep-learning medical-image-processing fully-convolutional-networks

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miccai-lits2017's Issues

the model does not save in '/module'

Strange dice coefficient on volume-54.nii

I randomly split the train dataset (131 cases) to two no-overlap subset, which are train set (105 cases) and test (26 cases) set. When I finshed train the network and test it on the test set (26 cases). I obtained a result that dice per case is 0.932. It is lower than your result (0.957).
Most importantly, I found that the dice coefficient on volume-54.nii is very poor. (0.18). Then I visualized the segmentation result of volume-54.nii and compared it to its ground truth. And then I found there were some dislocation about them.(about 10 slices). For example, segmentation result started with 62th slice,while ground truth started with 52th slice.

Just segment liver without segment liver tumor?

How to segment liver tumor, this challenge is multi-label problem, how to process data, if I want to simultaneous segment liver and liver tumor? Looking forward to your answer.

label

你好，很高兴看到您的代码分享，有一个问题我想问一下，就是模型输出的时候为什么是[B,1,256,256,256]，这个1不应该是对应类别吗？假如说是三分类，模型输出不应该是[B,3,256,256,256]这个样子吗？希望可以解决一下我的困惑，谢谢！

Training Time

Hi, Could you pls tell me how long you take to train 1000 epoch?

loss convergence

Hi, I arranged my equipment environment follow your code. But the train loss is unchanged, then I add val loss and find the val loss is also keeping unchanged. I changed the learning rate bigger, but still not effect. Have you encountered this problem?

Train MR images

Can this network be used in training my own MR images?

set the alpha of loss function

Could you explain how is alpha valued？why is the value of alpha 0.33？

想请问一下Metirx类中输入的voxel_spacing是个什么格式的数据呀，如果我的模型就是直接输出3D矩阵的话是否在评估的时候还需要这个变量呢

Can anyone share how to organize the dataset？

what kind of data should I use？Can I use .nii.gz format data directly？
Thanks！

what will be the input image?

should we give the input the whole 3D CT images? please explain it
the folder name
'train/ct' and 'train/seg'
please also explain it.

Thanks

Does anyone know how to do data augmentation？

logger.py报错

ttributeError: 'DataFrame' object has no attribute 'append'，
how to fix it?

Project dependencies may have API risk issues

Hi, In MICCAI-LITS2017, inappropriate dependency versioning constraints can cause risks.

Below are the dependencies and version constraints that the project is using

numpy==1.14.2
torch==1.0.1.post2
visdom==0.1.8.8
pandas==0.23.3
scipy==1.0.0
tqdm==4.40.2
scikit-image==0.13.1
SimpleITK==1.0.1
pydensecrf==1.0rc3

The version constraint == will introduce the risk of dependency conflicts because the scope of dependencies is too strict.
The version constraint No Upper Bound and * will introduce the risk of the missing API Error because the latest version of the dependencies may remove some APIs.

After further analysis, in this project,
The version constraint of dependency visdom can be changed to >=0.1.8,<=0.1.8.9.
The version constraint of dependency scipy can be changed to >=0.12.0,<=1.7.3.
The version constraint of dependency tqdm can be changed to >=4.36.0,<=4.64.0.
The version constraint of dependency scikit-image can be changed to >=0.12.0,<=0.19.3.

The above modification suggestions can reduce the dependency conflicts as much as possible,
and introduce the latest version as much as possible without calling Error in the projects.

The invocation of the current project includes all the following methods.

The calling methods from the visdom

visdom.Visdom.line
visdom.Visdom

The calling methods from the scipy

scipy.ndimage.morphology.binary_erosion
scipy.ndimage.zoom
scipy.ndimage.morphology.generate_binary_structure

The calling methods from the tqdm

tqdm.tqdm

The calling methods from the scikit-image

skimage.morphology.remove_small_holes

The calling methods from the all methods

SimpleITK.GetArrayFromImage.astype
sum.append
dice.torch.log.torch.pow.mean
pandas.DataFrame.mean
self.pred_mask.self.real_mask.sum
self.real2pred_nn.sum
pydensecrf.utils.create_pairwise_gaussian
target.target.pred.pow.sum.sum
copy.deepcopy
net.ResUNet.ResUNet
self.decoder_stage3
self.map3
SimpleITK.GetImageFromArray.SetSpacing
pydensecrf.densecrf.DenseCRF.stepInference
random.randint
self.up_conv3
ct_array.torch.FloatTensor.unsqueeze.astype
self.map4
target.pred.sum.sum.sum
pred.squeeze.pow
end_slice.start_slice.ct_array.torch.FloatTensor.cuda
self.pred_mask.sum
self.down_conv4
numpy.power
self.ce_loss
file.replace
pred.pow.sum.sum.sum
sys.path.append
ct.cuda.cuda
torch.nn.Upsample
torch.log
self.real2pred_nn.np.power.sum
visdom.Visdom
pydensecrf.densecrf.DenseCRF
SimpleITK.GetImageFromArray.astype
torch.utils.data.DataLoader
pandas.DataFrame.max
net.ResUNet.net.torch.nn.DataParallel.cuda.parameters
torch.save
SimpleITK.WriteImage
pydensecrf.densecrf.DenseCRF.startInference
self.bce_loss
self.down_conv1
isinstance
self.get_jaccard_index
torch.nn.Sequential
math.sqrt
pydensecrf.utils.create_pairwise_bilateral
x.replace.replace
self.voxel_sapcing.np.array.reshape
self.get_surface
utilities.calculate_metrics.Metirc.get_jaccard_index
net.ResUNet.ResUNet.torch.nn.DataParallel.cuda.eval
dict
self.pred2real_nn.sum
scipy.ndimage.morphology.binary_erosion.nonzero
time.time
loss.WBCE.WCELoss
torch.nn.init.kaiming_normal_
super.__init__
torch.nn.init.constant_
torch.long
pandas.DataFrame
numpy.zeros_like
len
scipy.ndimage.morphology.generate_binary_structure
outputs.cpu.detach
SimpleITK.GetImageFromArray
target.pow.sum.sum
print
liver_seg.astype.astype
skimage.measure.regionprops
SimpleITK.GetImageFromArray.SetOrigin
self.pred2real_nn.max
torch.nn.CrossEntropyLoss
loss_func.item
target.sum.sum
torch.pow
torch.no_grad
pred.pow.sum.sum
SimpleITK.ReadImage.GetOrigin
target.pow.sum
self.up_conv2
self.decoder_stage2
scipy.ndimage.zoom.astype
self.pred2real_nn.np.power.sum
net.ResUNet.ResUNet.torch.nn.DataParallel.cuda.load_state_dict
numpy.ones
ResUNet.parameters
os.mkdir
self.decoder_stage4
float
pandas.ExcelWriter
scipy.spatial.cKDTree
loss.Dice.DiceLoss
scipy.ndimage.morphology.binary_erosion
net
net.ResUNet.net.torch.nn.DataParallel.cuda.train
torch.FloatTensor
utilities.calculate_metrics.Metirc
list
self.real2pred_nn.max
torch.nn.PReLU
os.path.exists
self.encoder_stage2
torch.optim.Adam
pred.squeeze.squeeze
spacing_list.sort
self.map2
numpy.zeros_like.astype
Q.np.array.np.argmax.reshape
seg.cuda.cuda
utilities.calculate_metrics.Metirc.get_RMSD
int.para.lower.liver_roi.para.upper.liver_roi.astype.sum
SimpleITK.GetImageFromArray.SetDirection
utilities.calculate_metrics.Metirc.get_FNR
SimpleITK.ReadImage
target.pow.sum.sum.sum
super
s2.s1.mean
torch.optim.lr_scheduler.MultiStepLR
ct_tensor.unsqueeze.unsqueeze
loss.Hybrid.HybridLoss
SimpleITK.ReadImage.GetDirection
loss.ELDice.ELDiceLoss
self.down_conv2
spacing_list.append
loss.backward
format
int
self.encoder_stage4
numpy.prod
ct_tensor.unsqueeze.unsqueeze.unsqueeze
numpy.where
self.up_conv4
torch.optim.Adam.step
sum
self.get_real2pred_nn
torch.optim.Adam.zero_grad
utilities.calculate_metrics.Metirc.get_dice_coefficient
torch.nn.Conv3d
torch.clamp
self.encoder_stage1
dataset.dataset.Dataset
pandas.DataFrame.std
self.real_mask.sum
utilities.calculate_metrics.Metirc.get_FPR
enumerate
scipy.ndimage.zoom
x.replace
net.ResUNet.ResUNet.torch.nn.DataParallel.cuda
ct_array.torch.FloatTensor.unsqueeze
numpy.stack
file.replace.replace
torch.nn.Sigmoid
target.pred.sum.sum
pydensecrf.densecrf.DenseCRF.addPairwiseEnergy
os.path.split
Q.np.array.np.argmax.reshape.astype
pandas.ExcelWriter.save
self.map1
pandas.DataFrame.to_excel
torch.optim.lr_scheduler.MultiStepLR.step
shutil.rmtree
net.ResUNet.net.torch.nn.DataParallel.cuda
range
numpy.any
net.ResUNet.net
net.cpu
torch.nn.DataParallel
target.pred.sum
target.target.pred.pow.sum.sum.sum
param.numel
loss.SS.SSLoss
zip
ResUNet
torch.nn.functional.dropout
torch.ones_like
SimpleITK.GetArrayFromImage
visdom.Visdom.line
collections.OrderedDict
parameter.lower.liver_roi.para.upper.liver_roi.astype
self.down_conv3
SimpleITK.ReadImage.GetSpacing
loss.Tversky.TverskyLoss
torch.cat
pydensecrf.densecrf.DenseCRF.setUnaryEnergy
numpy.argmax
numpy.array
utilities.calculate_metrics.Metirc.get_MSD
os.path.join
loss.Jaccard.JaccardLoss
utilities.calculate_metrics.Metirc.get_VOE
map
self.get_pred2real_nn
utilities.calculate_metrics.Metirc.get_ASSD
os.listdir
time_pre_case.append
outputs.cpu.detach.numpy
skimage.measure.label
torch.load
target.pred.sum.sum.sum.mean
step_list.append
self.encoder_stage3
net.ResUNet.net.torch.nn.DataParallel.cuda.state_dict
int.seg_array.astype.sum
pred.pow.sum
target.pow
pydensecrf.densecrf.DenseCRF.inference
numpy.squeeze
torch.nn.BCELoss
target.sum.sum.sum
ResUNet.apply
file_name.append
pandas.DataFrame.min
collections.OrderedDict.append
numpy.zeros
tqdm.tqdm
max
target.pred.pow
torch.FloatTensor.cuda
loss.BCE.BCELoss
min
torch.nn.ConvTranspose3d
skimage.morphology.remove_small_holes
scipy.spatial.cKDTree.query
loss_func
target.target.pred.pow.sum
self.decoder_stage1
target.sum
pydensecrf.utils.unary_from_softmax

@developer
Could please help me check this issue?
May I pull a request to fix it?
Thank you very much.

Can you share specific implementation steps?

I want to use your code reimplement liver and tumor segmentation in my dataset,so can you share specfic implement steps?

how can I sovle the problem of CUDA out of memory

hybrid dilated convolution?

Thanks for your share.
In this code, there is no code about "hybrid dilated convolution"
Can you share it ?
Thanks

3DIRCADb dataset is a subset of LiTS dataset with case number from 27 to 48.

Dear author:

My question is how to extract the 3DIRCADb dataset from LiTS dataset? case numver from 27 to 48, then there should be 22 volumes.

Looking forward to your reply. Thank you.

测试

请问这个代码没有测试部分吗

How to cite your work?

Is there a paper related to this work?

Could you please provide any pretrained models?

Could you please provide any pre-trained models?
It would be helpful.

about dateset

能分享一下数据集吗？登录到谷歌driver下载不下来。　[email protected]　分享个网盘连接也好

A bug when test

I found a bug in 'val.py', which caused abnormal segmentation result ( a poor dice) in some case (eg. volume-54.nii). The number of its slices was divisible by 48 (the 3d patch's depth size),when it was split into test dataset, the value of variable 'count' will be equal 0,but such code
{ pred_seg = np.concatenate([pred_seg, outputs_list[-1][-count:]], axis=0) }
will concentrate the last block (entire 3d 256×256×48 patches) on the segmentation result,which should been only used the last several slices (the number of slices which should be used = count)
--------------------original code -----------------------------

当无法整除的时候反向取最后一个block

if end_slice is not ct_array.shape[0] - 1:
    flag = True
    count = ct_array.shape[0] - start_slice
    ct_array_list.append(ct_array[-size:, :, :]

--------------------revised code--------------------------------
# 当无法整除的时候反向取最后一个block
if end_slice is not ct_array.shape[0] - 1:
count = ct_array.shape[0] - start_slice
if count != 0:
flag = True
ct_array_list.append(ct_array[-size:, :, :])

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miccai-lits2017's Issues

当无法整除的时候反向取最后一个block

--------------------revised code-------------------------------- # 当无法整除的时候反向取最后一个block if end_slice is not ct_array.shape[0] - 1: count = ct_array.shape[0] - start_slice if count != 0: flag = True ct_array_list.append(ct_array[-size:, :, :])

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--------------------revised code--------------------------------
# 当无法整除的时候反向取最后一个block
if end_slice is not ct_array.shape[0] - 1:
count = ct_array.shape[0] - start_slice
if count != 0:
flag = True
ct_array_list.append(ct_array[-size:, :, :])