Using Default_pad_value If Image Is Of Type LABEL

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Introduction

When working with medical imaging data, it's common to use data augmentation techniques to increase the size of the dataset and improve the robustness of machine learning models. One such technique is the use of random affine transformations, which can be applied to images to simulate different perspectives and orientations. However, when working with label maps, which are used to represent the presence or absence of specific features in an image, the default pad value may not be used as expected. In this article, we'll explore this issue and provide a solution.

Is There an Existing Issue for This?

Before diving into the issue, it's essential to check if there's an existing issue that addresses this problem. After searching the existing issues, we found that this is a known issue in the TorchIO project.

Bug Summary

The issue arises when using a LabelMap as part of the subject and applying a RandomAffine transformation with a default_pad_value. The code of the RandomAffine class forces the default_pad_value to be 0 when the image type is not INTENSITY. This means that when working with label maps, the default_pad_value is not used as expected.

Code for Reproduction

To reproduce this issue, we can use the following code:

import numpy as np
import torch
import torchio as tio

label_data = torch.from_numpy(
  np.full((1, 2, 2, 2), 1)
)
s = tio.Subject(
  label=tio.LabelMap(tensor=label_data)
)
aff = tio.RandomAffine(
  p=1,
  translation=(-10, 10, -10, 10, -10, 10),
  default_pad_value=250
)
s_aug = aff.apply_transform(s)
s_aug['label'].tensor

Actual Outcome

When running this code, we get the following output:

tensor([[[[0., 0.],
          [0., 0.]],

          [[0., 0.],
          [0., 0.]]]])

As we can see, the default_pad_value is not used, and the output is a tensor filled with zeros.

Error Messages

There are no error messages in this case, as the code runs without any issues. However, the output is not as expected.

Expected Outcome

We would expect the default_pad_value to be used in this case, resulting in a tensor filled with the specified value.

System Info

Here's the system info:

Platform:   Linux-6.8.0-59-generic-x86_64-with-glibc2.35
TorchIO:    0.20.8
PyTorch:    2.6.0+cu124
SimpleITK:  2.5.0 (ITK 5.4)
NumPy:      2.0.2
Python:     3.9.22 | packaged by conda-forge | (main, Apr 14 2025, 23:35:59)
[GCC 13.3.0]

Solution

To solve this issue, we can create a RandomAffine transformation that allows the default_pad_value to be used for label maps. We can do this by creating a new class that inherits from the RandomAffine class and overrides the apply_transform method.

Here's an example implementation:

class CustomRandomAffine(tio.RandomAffine):
  def apply_transform(self, subject):
    if subject.label.type == 'LABEL':
      # Use the default pad value for label maps
      pad_value = self.default_pad_value
    else:
      # Use the default pad value for intensity images
      pad_value = 0

    # Apply the random affine transformation
    transformed_subject = super().apply_transform(subject)

    # Set the pad value for the label map
    transformed_subject.label.tensor = transformed_subject.label.tensor.fill_(pad_value)

    return transformed_subject

We can then use this custom RandomAffine transformation in our code:

aff = CustomRandomAffine(
  p=1,
  translation=(-10, 10, -10, 10, -10, 10),
  default_pad_value=250
)
s_aug = aff.apply_transform(s)
s_aug['label'].tensor

This should result in the expected output, with the default_pad_value used for the label map.

Conclusion

Q: What is the issue with using default_pad_value with LabelMap?

A: The issue arises when using a LabelMap as part of the subject and applying a RandomAffine transformation with a default_pad_value. The code of the RandomAffine class forces the default_pad_value to be 0 when the image type is not INTENSITY. This means that when working with label maps, the default_pad_value is not used as expected.

Q: Why is this a problem?

A: This is a problem because it can lead to unexpected behavior when working with label maps. For example, if you're using a LabelMap to represent the presence or absence of specific features in an image, and you apply a RandomAffine transformation with a default_pad_value, the resulting image may not accurately represent the original features.

Q: How can I fix this issue?

A: To fix this issue, you can create a custom RandomAffine transformation that allows the default_pad_value to be used for label maps. This can be done by creating a new class that inherits from the RandomAffine class and overrides the apply_transform method.

Q: What is the custom RandomAffine transformation?

A: The custom RandomAffine transformation is a new class that inherits from the RandomAffine class and overrides the apply_transform method. This method checks if the image type is LABEL, and if so, uses the default_pad_value instead of 0.

Q: How do I use the custom RandomAffine transformation?

A: To use the custom RandomAffine transformation, you can create an instance of the class and pass it to the apply_transform method. For example:

aff = CustomRandomAffine(
  p=1,
  translation=(-10, 10, -10, 10, -10, 10),
  default_pad_value=250
)
s_aug = aff.apply_transform(s)
s_aug['label'].tensor

Q: What are the benefits of using the custom RandomAffine transformation?

A: The benefits of using the custom RandomAffine transformation are:

  • It allows the default_pad_value to be used for label maps, which can lead to more accurate results.
  • It provides more flexibility when working with label maps.
  • It can be used in conjunction with other data augmentation techniques to create more diverse and realistic datasets.

Q: Are there any limitations to using the custom RandomAffine transformation?

A: Yes, there are some limitations to using the custom RandomAffine transformation:

  • It requires creating a new class that inherits from the RandomAffine class, which can be more complex than using the original RandomAffine class.
  • It may require additional code to handle specific use cases, such as when working with multiple label maps.

Q: Can I use the custom RandomAffine transformation with other data augmentation techniques?

A: Yes, you can use the custom Affine transformation with other data augmentation techniques. In fact, using multiple data augmentation techniques can help create more diverse and realistic datasets.

Q: How do I know if I need to use the custom RandomAffine transformation?

A: You may need to use the custom RandomAffine transformation if you're working with label maps and want to use the default_pad_value to create more accurate results. You can also use the custom transformation if you want to provide more flexibility when working with label maps.

Q: Can I contribute to the development of the custom RandomAffine transformation?

A: Yes, you can contribute to the development of the custom RandomAffine transformation by submitting a pull request to the TorchIO project. This can help ensure that the transformation is maintained and updated in the future.