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from numbers import Real |
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from typing import Union, Optional, Callable |
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import torch |
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from torchio.data.image import ScalarImage |
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from ....data.subject import Subject |
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from ...intensity_transform import IntensityTransform |
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class SlabProjection(IntensityTransform): |
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"""Project intensities along a given axis, possibly with sliding slabs. |
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Args: |
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axis: Index for the spatial dimension to project across. |
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See :class:`~.torchio.RandomFlip` for information on the accepted |
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types. |
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slab_thickness: Thickness of slab projections. In other words, the |
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number of voxels in the ``axis`` dimension to project across. |
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If ``None``, the projection will be done across the entire span of |
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the ``axis`` dimension (i.e. ``axis`` dimension will be reduced to |
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1). |
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stride: Number of voxels to stride along the ``axis`` dimension between |
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slab projections. Default is 1. |
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projection_type: Type of intensity projection. Possible inputs are |
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``'max'`` (the default), ``'min'``, ``'mean'``, ``'median'``, or |
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``'percentile'``. If ``'percentile'`` is used, the ``percentile`` |
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argument must also be supplied. |
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percentile: Percetile to use for intensity projections. This argument |
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is required if ``projection_type`` is ``'percentile'`` and is |
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silently ignored otherwise. |
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full_slabs_only: Boolean. Should projections be done only for slabs |
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that are ``slab_thickness`` thick? Default is ``True``. |
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If ``False``, some slabs may not be ``slab_thickness`` thick |
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depending on the size of the image, slab thickness, and stride. |
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Example: |
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>>> import torchio as tio |
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>>> ct = tio.datasets.Slicer('CTChest').CT_chest |
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>>> axial_mip = tio.SlabProjection("S", slab_thickness=20) |
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>>> ct_t = axial_mip(ct) |
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>>> ct_t.plot() |
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.. plot:: |
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import torchio as tio |
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sub = tio.datasets.Slicer('CTChest') |
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ct = sub.CT_chest |
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axial_mip = tio.SlabProjection("S", slab_thickness=20) |
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ct_mip = axial_mip(ct) |
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sub.add_image(ct_mip, 'CT_MIP') |
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sub = tio.Clamp(-1000, 1000)(sub) |
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sub.plot() |
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""" |
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def __init__( |
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self, |
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axis: Union[int, str], |
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slab_thickness: Optional[int] = None, |
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stride: int = 1, |
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projection_type: str = 'max', |
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percentile: Optional[float] = None, |
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full_slabs_only: bool = True, |
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**kwargs |
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): |
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super().__init__(**kwargs) |
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self.args_names = ( |
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'axis', 'slab_thickness', 'stride', |
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'projection_type', 'percentile', 'full_slabs_only' |
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) |
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self.axis = self.parse_axes(axis)[0] |
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self.slab_thickness = slab_thickness |
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self.stride = stride |
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self.projection_fun = self.get_projection_function(projection_type) |
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self.projection_type = projection_type |
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self.percentile = self.validate_percentile(percentile, projection_type) |
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self.full_slabs_only = full_slabs_only |
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@staticmethod |
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def validate_percentile(percentile, projection_type): |
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if not projection_type == 'percentile': |
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return percentile |
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message = ( |
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"For projection_type='percentile', `percentile` must be a scalar" |
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f' value in the range [0, 1], but "{percentile}" was passed' |
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) |
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if not isinstance(percentile, Real): |
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raise TypeError(message) |
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elif 0 <= percentile <= 100: |
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return percentile / 100 |
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else: |
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raise ValueError(message) |
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@staticmethod |
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def get_projection_function(projection_type: str) -> Callable: |
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arg_to_function = { |
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'max': 'amax', |
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'min': 'amin', |
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'mean': 'mean', |
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'median': 'median', |
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'percentile': 'quantile', |
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} |
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try: |
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function_name = arg_to_function[projection_type] |
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except KeyError: |
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message = ( |
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f'The projection type must be in {arg_to_function.keys()}, ' |
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f' but {projection_type} was passed' |
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) |
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raise ValueError(message) |
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projection_function = getattr(torch, function_name) |
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return projection_function |
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def get_num_slabs(self, axis_span: int) -> int: |
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if self.full_slabs_only: |
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start_index = 0 |
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num_slabs = 0 |
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while start_index + self.slab_thickness <= axis_span: |
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num_slabs += 1 |
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start_index += self.stride |
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else: |
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num_slabs = torch.ceil(torch.tensor(axis_span) / self.stride) |
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num_slabs = int(num_slabs.item()) |
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return num_slabs |
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def apply_transform(self, subject: Subject) -> Subject: |
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axis_index = self.ensure_axes_indices(subject, [self.axis])[0] |
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for image in self.get_images(subject): |
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self.apply_projection(image, axis_index) |
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return subject |
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def apply_projection(self, image: ScalarImage, axis_index: int) -> None: |
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axis_span = image.shape[axis_index] |
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if self.slab_thickness is None: |
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self.slab_thickness = axis_span |
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elif self.slab_thickness > axis_span: |
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self.slab_thickness = axis_span |
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image.set_data(self.projection(image.data, axis_index, axis_span)) |
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def projection( |
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self, |
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tensor: torch.Tensor, |
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axis_index: int, |
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axis_span: int, |
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) -> torch.Tensor: |
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if self.projection_type in ['mean', 'percentile']: |
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tensor = tensor.to(torch.float) |
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num_slabs = self.get_num_slabs(axis_span) |
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slabs = [] |
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start_index = 0 |
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end_index = start_index + self.slab_thickness |
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for _ in range(num_slabs): |
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slab_indices = torch.arange(start_index, end_index) |
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slab = tensor.index_select(axis_index, slab_indices) |
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if self.projection_type == 'median': |
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projected, _ = self.projection_fun( |
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slab, dim=axis_index, keepdim=True) |
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elif self.projection_type == 'percentile': |
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projected = self.projection_fun( |
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slab, q=self.percentile, dim=axis_index, |
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keepdim=True) |
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else: |
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projected = self.projection_fun( |
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slab, dim=axis_index, keepdim=True) |
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slabs.append(projected) |
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start_index += self.stride |
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end_index = start_index + self.slab_thickness |
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if end_index > axis_span: |
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end_index = axis_span |
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return torch.cat(slabs, dim=axis_index) |
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fepegar /
torchio
