Resample one key to the spacing of another key #3245

mattwarkentin · 2021-11-02T21:56:02Z

mattwarkentin
Nov 2, 2021

Hi,

I am trying to write a custom transform that will resample the spacing of the one image/key to match the spacing of another key within a subject. This is my attempt so far, but it errors and I can't figure out why...

from monai.transforms import (
    Compose,
    LoadImaged,
    AddChanneld,
    ToTensord,
    Orientationd,
    Spacing,
    MapTransform
)

class DynamicSpacingd(MapTransform):
    def __init__(self, keys, template_key):
        super().__init__(keys)
        self.template_key = template_key

    def __call__(self, data):
        new_spacing = data[f'{self.template_key}_meta_dict']['spacing']
        spacer = Spacing(new_spacing.tolist())
        for key in self.keys:
            data[key] = spacer(data[key])
        return data

keys = ['img', 'mask']

trans = Compose([
  LoadImaged(keys),
  AddChanneld(keys),
  Orientationd(keys, axcodes='RAS'),
  DynamicSpacingd('mask', 'img'),
  ToTensord(keys)
])

sub = {'img': 'image.nrrd', 'mask': 'mask.nrrd'}

trans(sub)


=== Transform input info -- ToTensord ===

=== Transform input info -- ToTensord ===

=== Transform input info -- ToTensord ===

=== Transform input info -- ToTensord ===
img statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 512, 512, 134)
Value range: (-3024.0, 2007.0)
img statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 512, 512, 134)
Value range: (-3024.0, 2007.0)
img statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 512, 512, 134)
Value range: (-3024.0, 2007.0)
img statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 512, 512, 134)
Value range: (-3024.0, 2007.0)
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mask statistics:
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img_meta_dict statistics:
Type: <class 'dict'>
Value: {'NRRD_kinds[0]': 'domain', 'NRRD_kinds[1]': 'domain', 'NRRD_kinds[2]': 'domain', 'NRRD_space': 'left-posterior-superior', 'spacing': array([0.74218798, 0.74218798, 2.49999952]), 'original_affine': array([[  -0.74218798,    0.        ,    0.        ,  191.6000061 ],
       [   0.        ,   -0.74218798,    0.        ,  208.19999695],
       [   0.        ,    0.        ,    2.49999952, -324.51998901],
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       [   0.        ,    0.        ,    2.49999952, -324.51998901],
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img_meta_dict statistics:
Type: <class 'dict'>
Value: {'NRRD_kinds[0]': 'domain', 'NRRD_kinds[1]': 'domain', 'NRRD_kinds[2]': 'domain', 'NRRD_space': 'left-posterior-superior', 'spacing': array([0.74218798, 0.74218798, 2.49999952]), 'original_affine': array([[  -0.74218798,    0.        ,    0.        ,  191.6000061 ],
       [   0.        ,   -0.74218798,    0.        ,  208.19999695],
       [   0.        ,    0.        ,    2.49999952, -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'affine': array([[   0.74218798,    0.        ,    0.        , -187.65805006],
       [   0.        ,    0.74218798,    0.        , -171.05805922],
       [   0.        ,    0.        ,    2.49999952, -324.51998901],
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img_meta_dict statistics:
Type: <class 'dict'>
Value: {'NRRD_kinds[0]': 'domain', 'NRRD_kinds[1]': 'domain', 'NRRD_kinds[2]': 'domain', 'NRRD_space': 'left-posterior-superior', 'spacing': array([0.74218798, 0.74218798, 2.49999952]), 'original_affine': array([[  -0.74218798,    0.        ,    0.        ,  191.6000061 ],
       [   0.        ,   -0.74218798,    0.        ,  208.19999695],
       [   0.        ,    0.        ,    2.49999952, -324.51998901],
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       [   0.        ,    0.        ,    2.49999952, -324.51998901],
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img_meta_dict statistics:
Type: <class 'dict'>
Value: {'NRRD_kinds[0]': 'domain', 'NRRD_kinds[1]': 'domain', 'NRRD_kinds[2]': 'domain', 'NRRD_space': 'left-posterior-superior', 'spacing': array([0.74218798, 0.74218798, 2.49999952]), 'original_affine': array([[  -0.74218798,    0.        ,    0.        ,  191.6000061 ],
       [   0.        ,   -0.74218798,    0.        ,  208.19999695],
       [   0.        ,    0.        ,    2.49999952, -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'affine': array([[   0.74218798,    0.        ,    0.        , -187.65805006],
       [   0.        ,    0.74218798,    0.        , -171.05805922],
       [   0.        ,    0.        ,    2.49999952, -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'spatial_shape': array([512, 512, 134]), 'original_channel_dim': 'no_channel', 'filename_or_obj': imagenrrd'}
mask_meta_dict statistics:
Type: <class 'dict'>
Value: {'NRRD_kinds[0]': 'domain', 'NRRD_kinds[1]': 'domain', 'NRRD_kinds[2]': 'domain', 'NRRD_space': 'left-posterior-superior', 'spacing': array([0.742188, 0.742188, 2.5     ]), 'original_affine': array([[  -0.742188  ,    0.        ,    0.        ,  191.6000061 ],
       [   0.        ,   -0.742188  ,    0.        ,  208.19999695],
       [   0.        ,    0.        ,    2.5       , -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'affine': array([[   0.742188  ,    0.        ,    0.        , -187.6580619 ],
       [   0.        ,    0.742188  ,    0.        , -171.05807105],
       [   0.        ,    0.        ,    2.5       , -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'spatial_shape': array([512, 512, 134]), 'original_channel_dim': 'no_channel', 'filename_or_obj': 'mask.nrrd'}
mask_meta_dict statistics:
Type: <class 'dict'>
Value: {'NRRD_kinds[0]': 'domain', 'NRRD_kinds[1]': 'domain', 'NRRD_kinds[2]': 'domain', 'NRRD_space': 'left-posterior-superior', 'spacing': array([0.742188, 0.742188, 2.5     ]), 'original_affine': array([[  -0.742188  ,    0.        ,    0.        ,  191.6000061 ],
       [   0.        ,   -0.742188  ,    0.        ,  208.19999695],
       [   0.        ,    0.        ,    2.5       , -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'affine': array([[   0.742188  ,    0.        ,    0.        , -187.6580619 ],
       [   0.        ,    0.742188  ,    0.        , -171.05807105],
       [   0.        ,    0.        ,    2.5       , -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'spatial_shape': array([512, 512, 134]), 'original_channel_dim': 'no_channel', 'filename_or_obj': 'mask.nrrd'}
mask_meta_dict statistics:
Type: <class 'dict'>
Value: {'NRRD_kinds[0]': 'domain', 'NRRD_kinds[1]': 'domain', 'NRRD_kinds[2]': 'domain', 'NRRD_space': 'left-posterior-superior', 'spacing': array([0.742188, 0.742188, 2.5     ]), 'original_affine': array([[  -0.742188  ,    0.        ,    0.        ,  191.6000061 ],
       [   0.        ,   -0.742188  ,    0.        ,  208.19999695],
       [   0.        ,    0.        ,    2.5       , -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'affine': array([[   0.742188  ,    0.        ,    0.        , -187.6580619 ],
       [   0.        ,    0.742188  ,    0.        , -171.05807105],
       [   0.        ,    0.        ,    2.5       , -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'spatial_shape': array([512, 512, 134]), 'original_channel_dim': 'no_channel', 'filename_or_obj': 'mask.nrrd'}
mask_meta_dict statistics:
Type: <class 'dict'>
Value: {'NRRD_kinds[0]': 'domain', 'NRRD_kinds[1]': 'domain', 'NRRD_kinds[2]': 'domain', 'NRRD_space': 'left-posterior-superior', 'spacing': array([0.742188, 0.742188, 2.5     ]), 'original_affine': array([[  -0.742188  ,    0.        ,    0.        ,  191.6000061 ],
       [   0.        ,   -0.742188  ,    0.        ,  208.19999695],
       [   0.        ,    0.        ,    2.5       , -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'affine': array([[   0.742188  ,    0.        ,    0.        , -187.6580619 ],
       [   0.        ,    0.742188  ,    0.        , -171.05807105],
       [   0.        ,    0.        ,    2.5       , -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'spatial_shape': array([512, 512, 134]), 'original_channel_dim': 'no_channel', 'filename_or_obj': mask.nrrd'}
img_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Orientationd', 'id': 5688239248, 'orig_size': (512, 512, 134), 'extra_info': {'meta_key': 'img_meta_dict', 'old_affine': array([[  -0.74218798,    0.        ,    0.        ,  191.6000061 ],
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       [   0.        ,    0.        ,    2.49999952, -324.51998901],
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img_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Orientationd', 'id': 5688239248, 'orig_size': (512, 512, 134), 'extra_info': {'meta_key': 'img_meta_dict', 'old_affine': array([[  -0.74218798,    0.        ,    0.        ,  191.6000061 ],
       [   0.        ,   -0.74218798,    0.        ,  208.19999695],
       [   0.        ,    0.        ,    2.49999952, -324.51998901],
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img_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Orientationd', 'id': 5688239248, 'orig_size': (512, 512, 134), 'extra_info': {'meta_key': 'img_meta_dict', 'old_affine': array([[  -0.74218798,    0.        ,    0.        ,  191.6000061 ],
       [   0.        ,   -0.74218798,    0.        ,  208.19999695],
       [   0.        ,    0.        ,    2.49999952, -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]])}}, {'class': 'ToTensord', 'id': 5688241296, 'orig_size': (512, 512, 134)}]
img_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Orientationd', 'id': 5688239248, 'orig_size': (512, 512, 134), 'extra_info': {'meta_key': 'img_meta_dict', 'old_affine': array([[  -0.74218798,    0.        ,    0.        ,  191.6000061 ],
       [   0.        ,   -0.74218798,    0.        ,  208.19999695],
       [   0.        ,    0.        ,    2.49999952, -324.51998901],
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mask_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Orientationd', 'id': 5688239248, 'orig_size': (512, 512, 134), 'extra_info': {'meta_key': 'mask_meta_dict', 'old_affine': array([[  -0.742188  ,    0.        ,    0.        ,  191.6000061 ],
       [   0.        ,   -0.742188  ,    0.        ,  208.19999695],
       [   0.        ,    0.        ,    2.5       , -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]])}}, {'class': 'ToTensord', 'id': 5688241296}]
mask_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Orientationd', 'id': 5688239248, 'orig_size': (512, 512, 134), 'extra_info': {'meta_key': 'mask_meta_dict', 'old_affine': array([[  -0.742188  ,    0.        ,    0.        ,  191.6000061 ],
       [   0.        ,   -0.742188  ,    0.        ,  208.19999695],
       [   0.        ,    0.        ,    2.5       , -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]])}}, {'class': 'ToTensord', 'id': 5688241296}]
mask_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Orientationd', 'id': 5688239248, 'orig_size': (512, 512, 134), 'extra_info': {'meta_key': 'mask_meta_dict', 'old_affine': array([[  -0.742188  ,    0.        ,    0.        ,  191.6000061 ],
       [   0.        ,   -0.742188  ,    0.        ,  208.19999695],
       [   0.        ,    0.        ,    2.5       , -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]])}}, {'class': 'ToTensord', 'id': 5688241296}]
mask_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Orientationd', 'id': 5688239248, 'orig_size': (512, 512, 134), 'extra_info': {'meta_key': 'mask_meta_dict', 'old_affine': array([[  -0.742188  ,    0.        ,    0.        ,  191.6000061 ],
       [   0.        ,   -0.742188  ,    0.        ,  208.19999695],
       [   0.        ,    0.        ,    2.5       , -324.51998901],
       [   0.        ,    0.        ,    0.        ,    1.        ]])}}, {'class': 'ToTensord', 'id': 5688241296}]
Traceback (most recent call last):
  File "/Users/matt/.virtualenvs/torch/lib/python3.7/site-packages/monai/transforms/transform.py", line 92, in apply_transform
    return _apply_transform(transform, data, unpack_items)
  File "/Users/matt/.virtualenvs/torch/lib/python3.7/site-packages/monai/transforms/transform.py", line 60, in _apply_transform
    return transform(parameters)
  File "/Users/matt/.virtualenvs/torch/lib/python3.7/site-packages/monai/transforms/utility/dictionary.py", line 474, in __call__
    d[key] = self.converter(d[key])
  File "/Users/matt/.virtualenvs/torch/lib/python3.7/site-packages/monai/transforms/utility/array.py", line 353, in __call__
    return convert_to_tensor(img, dtype=self.dtype, device=self.device, wrap_sequence=True)  # type: ignore
  File "/Users/matt/.virtualenvs/torch/lib/python3.7/site-packages/monai/utils/type_conversion.py", line 128, in convert_to_tensor
    return torch.as_tensor(data, dtype=dtype, device=device)  # type: ignore
ValueError: expected sequence of length 1 at dim 1 (got 4)

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/matt/.virtualenvs/torch/lib/python3.7/site-packages/monai/transforms/compose.py", line 160, in __call__
    input_ = apply_transform(_transform, input_, self.map_items, self.unpack_items)
  File "/Users/matt/.virtualenvs/torch/lib/python3.7/site-packages/monai/transforms/transform.py", line 116, in apply_transform
    raise RuntimeError(f"applying transform {transform}") from e
RuntimeError: applying transform <monai.transforms.utility.dictionary.ToTensord object at 0x1530bac90>

Any idea what I'm doing wrong?

Answered by rijobro

Nov 2, 2021

Spacing returns a tuple of outputs: return output_data, affine, new_affine. You could do data[key] = spacer(data[key])[0], or more simply, you could probably do:

    def __call__(self, data):
        new_spacing = data[f'{self.template_key}_meta_dict']['spacing']
        return Spacingd(self.keys, new_spacing.tolist())(data)

i.e., pass the spacing on to Spacingd rather than Spacing.

View full answer

rijobro · 2021-11-02T22:20:16Z

rijobro
Nov 2, 2021
Collaborator

Spacing returns a tuple of outputs: return output_data, affine, new_affine. You could do data[key] = spacer(data[key])[0], or more simply, you could probably do:

    def __call__(self, data):
        new_spacing = data[f'{self.template_key}_meta_dict']['spacing']
        return Spacingd(self.keys, new_spacing.tolist())(data)

i.e., pass the spacing on to Spacingd rather than Spacing.

1 reply

mattwarkentin Nov 3, 2021
Author

Oops! Thanks @rijobro.

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Resample one key to the spacing of another key #3245

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