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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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from typing import Optional |
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from math import ceil |
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class Projection(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: Possible inputs are ``'Left'``, ``'Right'``, ``'Anterior'``, |
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``'Posterior'``, ``'Inferior'``, ``'Superior'``. Lower-case |
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versions and first letters are also valid, as only the first |
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letter will be used. |
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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. |
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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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``'quantile'``. If ``'quantile'`` is used, ``q`` must also be |
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supplied. |
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q: Quantile to use for intensity projections. This argument is required |
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if ``projection_type`` is ``'quantile'`` and is silently ignored |
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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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>>> sub = tio.datasets.Colin27() |
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>>> axial_mips = tio.Projection("S", slab_thickness=20) |
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>>> sub_t = axial_mips(sub) |
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>>> sub_t.t1.plot() |
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""" |
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def __init__( |
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self, |
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axis: str, |
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slab_thickness: Optional[int] = None, |
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stride: Optional[int] = 1, |
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projection_type: Optional[str] = 'max', |
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q: Optional[float] = None, |
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full_slabs_only: Optional[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', 'q', 'full_slabs_only' |
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) |
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self.axis = axis |
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self.slab_thickness = slab_thickness |
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self.stride = stride |
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self.projection_type = projection_type |
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self.q = q |
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self.full_slabs_only = full_slabs_only |
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self.projection_fun = self.get_projection_function() |
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def get_projection_function(self): |
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if self.projection_type == 'max': |
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projection_fun = torch.amax |
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elif self.projection_type == 'min': |
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projection_fun = torch.amin |
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elif self.projection_type == 'mean': |
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projection_fun = torch.mean |
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elif self.projection_type == 'median': |
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projection_fun = torch.median |
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elif self.projection_type == 'quantile': |
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projection_fun = torch.quantile |
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self.validate_quantile() |
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else: |
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message = ( |
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'`projection_type` must be one of "max", "min", "mean",' |
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' "median", or "quantile".' |
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) |
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raise ValueError(message) |
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return projection_fun |
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def validate_quantile(self): |
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message = ( |
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'For `projection_type="quantile"`, `q` must be a scalar value' |
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f'in the range [0, 1], not {self.q}.' |
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) |
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if self.q is None: |
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raise ValueError(message) |
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elif 0 <= self.q <= 1: |
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pass |
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else: |
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raise ValueError(message) |
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def apply_transform(self, subject: Subject) -> Subject: |
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for image in self.get_images(subject): |
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self.apply_projection(image) |
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return subject |
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def apply_projection(self, image: ScalarImage) -> None: |
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self.axis_index = image.axis_name_to_index(self.axis) |
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self.axis_span = image.shape[self.axis_index] |
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if self.slab_thickness is None: |
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self.slab_thickness = self.axis_span |
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elif self.slab_thickness > self.axis_span: |
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self.slab_thickness = self.axis_span |
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image.set_data(self.projection(image.data)) |
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def projection(self, tensor: torch.Tensor) -> torch.Tensor: |
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if self.projection_type in ['mean', 'quantile']: |
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tensor = tensor.to(torch.float) |
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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 <= self.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 = ceil(self.axis_span / self.stride) |
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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.tensor(list(range(start_index, end_index))) |
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slab = tensor.index_select(self.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=self.axis_index, keepdim=True) |
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elif self.projection_type == 'quantile': |
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projected = self.projection_fun( |
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slab, q=self.q, dim=self.axis_index, keepdim=True) |
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else: |
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projected = self.projection_fun( |
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slab, dim=self.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 > self.axis_span: |
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end_index = self.axis_span |
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return torch.cat(slabs, dim=self.axis_index) |
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fepegar /
torchio
