clinicadl.infer.SlicesToImageInferer¶

class clinicadl.infer.SlicesToImageInferer(slice_direction: str | SliceDirection, batch_size: int = 1, postprocessing: Sequence[Callable[[DataPointT], DataPointT] | TransformConfig] | None = None, postprocessing_on_cpu: bool = False, output_name: str = 'output', output_type: str | OutputType = OutputType.TENSOR)[source]¶

Splits a 3D volume into 2D slices, passes them in a 2D neural network, and merges the outputs in a 3D output volume.

Adapted from monai.inferers.SliceInferer.

Parameters:

slice_direction (str | SliceDirection, default=0) –
The slicing direction. Can be 0, 1 or 2.

Warning

Be careful with the orientation of your image. If your image is in RAS+ (e.g. you used ToCanonicalConfig), 0 refers to the sagittal direction, 1 to the coronal direction, and 2 to the axial direction.
batch_size (int, default=1) –
The size of the batch passed to the neural network. If you pass a batch of images to the inferer, this batch will be rearranged to match batch_size.

E.g., if a batch of 2` images is passed, with 3 slices in each image, and batch_size=4, then the first batch passed to the neural network will contain the three slices of the first image and the first slice of the second.
postprocessing (Optional[Sequence[TransformOrConfig]], default=None) – To apply postprocessing transformations (e.g. activations) after the pass forward in the neural network. Accepted transforms are functions that take as input a DataPoint and return a DataPoint, or configuration classes.
postprocessing_on_cpu (bool, default=False) – Whether to necessarily apply postprocessing on CPU. If False, postprocessing will be applied on the device where are the data and the neural network.
output_name (str, default="output") –
The name the give to the output in the DataPoint.

Important

If you postprocessing transform comes from clinicadl.transforms.config, do not forget to specify include=["<output_name>"] to apply the postprocessing to the output of the neural network.
output_type (str | OutputType, default="tensor") –
Determines the data type of the output:
- "image": the output will be converted to a torchio.ScalarImage;
- "mask": the output will be converted to a torchio.LabelMap;
- "tensor": the output will remain a torch.Tensor.

Examples

import torch
from clinicadl.infer import SlicesToImageInferer
from clinicadl.data.structures.examples import Colin27DataPoint
from clinicadl.networks.nn import ConvEncoder

net = ConvEncoder(spatial_dims=2, in_channels=1, channels=[2, 4], kernel_size=7)
datapoint = Colin27DataPoint()
inferer = SlicesToImageInferer(slice_direction=1, batch_size=16)

>>> datapoint.image.shape
(1, 181, 217, 181)
>>> with torch.no_grad(): out = inferer(datapoint, net)
>>> out["output"].shape
(4, 169, 217, 169)  # 2D neural network applies to the 217 coronal slices