Exemplo n.º 1
0
    def _extract_patches(self, image, label, patch_size, step):
        transforms_ = transforms.Compose(
            [PadToPatchShape(patch_size=patch_size, step=step)])
        transformed_image = transforms_(image)
        transformed_label = transforms_(label)

        return ABIDEPreprocessingPipeline.get_filtered_patches(
            transformed_image, transformed_label, patch_size, step)
Exemplo n.º 2
0
    def _extract_patches(self, image, label, patch_size, step):
        transforms_ = transforms.Compose(
            [PadToPatchShape(patch_size=patch_size, step=step)])
        transformed_image = transforms_(image)
        transformed_label = transforms_(label)

        return MultipleDatasetPipeline.get_filtered_patches(
            transformed_image, transformed_label, patch_size, step)
Exemplo n.º 3
0
 def setUp(self) -> None:
     paths = extract_file_paths(self.PATH)
     self._dataset = MRBrainSSegmentationFactory.create(
         natural_sort(paths), None, modalities=Modality.T1, dataset_id=0)
     self._reconstructor = ImageReconstructor([256, 256, 192],
                                              [1, 32, 32, 32], [1, 8, 8, 8])
     transforms = Compose(
         [ToNumpyArray(),
          PadToPatchShape([1, 32, 32, 32], [1, 8, 8, 8])])
     self._full_image = transforms(self.FULL_IMAGE_PATH)
Exemplo n.º 4
0
    def _extract_patches(self, image, subject, modality, patch_size, step):
        transforms_ = transforms.Compose(
            [PadToPatchShape(patch_size=patch_size, step=step)])
        transformed_image = transforms_(image)

        patches = ABIDEPreprocessingPipeline.get_patches(
            transformed_image, patch_size, step)

        for i, patch in enumerate(patches):
            x = transformed_image.x[tuple(patch.slice)]
            transform_ = transforms.Compose([
                ToNifti1Image(),
                NiftiToDisk(
                    os.path.join(
                        os.path.join(self._output_dir, subject, "mri",
                                     "patches", modality),
                        str(i) + ".nii.gz"))
            ])
            transform_(x)
Exemplo n.º 5
0
    def _extract_patches(self, image, subject, modality, patch_size, step):
        transforms_ = transforms.Compose(
            [PadToPatchShape(patch_size=patch_size, step=step)])
        transformed_image = transforms_(image)

        patches = iSEGPipeline.get_patches(transformed_image, patch_size, step)

        if not os.path.exists(os.path.join(self._output_dir, subject,
                                           modality)):
            os.makedirs(os.path.join(self._output_dir, subject, modality))

        for i, patch in enumerate(patches):
            x = transformed_image[tuple(patch.slice)]
            transforms_ = transforms.Compose([
                ToNifti1Image(),
                NiftiToDisk(
                    os.path.join(self._output_dir, subject, modality,
                                 str(i) + ".nii.gz"))
            ])
            transforms_(x)
Exemplo n.º 6
0
 def setUp(self) -> None:
     transforms = Compose(
         [ToNumpyArray(),
          PadToPatchShape((1, 32, 32, 32), (1, 8, 8, 8))])
     self._image = transforms(self.FULL_IMAGE_PATH)
     self._target = transforms(self.TARGET_PATH)
     patches = iSEGSliceDatasetFactory.get_patches([self._image],
                                                   [self._target],
                                                   (1, 32, 32, 32),
                                                   (1, 16, 16, 16))
     self._dataset = iSEGSliceDatasetFactory.create(
         [self._image], [self._target],
         patches,
         Modality.T1,
         0,
         transforms=[ToNDTensor()])
     self._reconstructor = ImageReconstructor([256, 192, 160],
                                              [1, 32, 32, 32],
                                              [1, 16, 16, 16],
                                              models=None,
                                              test_image=self._image)
Exemplo n.º 7
0
    def pad_to_shape(image, patch_size, step):
        transforms_ = transforms.Compose(
            [PadToPatchShape(patch_size=patch_size, step=step)])

        return transforms_(image)