Vision inference

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save_series_pred

def save_series_pred(
    series_obj, save_dir, val:str='1234'
):

Saves series prediction with updated DICOM UIDs.

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load_system_resources

def load_system_resources(
    models_path, learner_fn, variables_fn
):

Load necessary resources like learner and variables.

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inference

def inference(
    learn_inf, apply_reorder, target_spacing, fn:(<class 'str'>, <class 'pathlib.Path'>)='',
    save_path:(<class 'str'>, <class 'pathlib.Path'>)=None, org_img:NoneType=None, input_img:NoneType=None,
    org_size:NoneType=None
):

Predict on new data using exported model.

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compute_binary_tumor_volume

def compute_binary_tumor_volume(
    mask_data:Image
):

Compute the volume of the tumor in milliliters (ml).

Post-processing

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refine_binary_pred_mask

def refine_binary_pred_mask(
    pred_mask, remove_size:(<class 'int'>, <class 'float'>), percentage:float=0.2, verbose:bool=False
)->Tensor:

Removes small objects from the predicted binary mask.

Args: pred_mask: The (already binary) predicted mask to clean up. remove_size: Absolute reference object size in voxels (required, > 0). Objects smaller than remove_size * percentage are removed. percentage: Fraction of remove_size used as the cutoff. Defaults to 0.2. verbose: If True, print the number of components. Defaults to False.

Returns: The processed mask with small objects removed.

from fastcore.test import test_eq, test_fail

# large 4x4x4 (64 vox) + small 2x2x2 (8 vox) disjoint cubes
_m = np.zeros((10, 10, 10), dtype=np.uint8)
_m[1:5, 1:5, 1:5] = 1
_m[7:9, 7:9, 7:9] = 1

# cutoff 64*0.2=12.8 removes the small cube, keeps the large
_out = refine_binary_pred_mask(_m, remove_size=64, percentage=0.2)
test_eq(float(_out.sum()), 64.)
test_eq(float(_out[7:9, 7:9, 7:9].sum()), 0.)

# cutoff 8*0.2=1.6 keeps both cubes
_out2 = refine_binary_pred_mask(_m, remove_size=8, percentage=0.2)
test_eq(float(_out2.sum()), 72.)

# empty mask returns zeros
_empty = np.zeros((4, 4, 4), dtype=np.uint8)
test_eq(float(refine_binary_pred_mask(_empty, remove_size=8).sum()), 0.)

# remove_size is required and must be positive
test_fail(lambda: refine_binary_pred_mask(_m))
test_fail(lambda: refine_binary_pred_mask(_m, remove_size=None))
test_fail(lambda: refine_binary_pred_mask(_m, remove_size=0))

Parameter `min_size` is deprecated since version 0.26.0 and will be removed in 2.0.0 (or later). To avoid this warning, please use the parameter `max_size` instead. For more details, see the documentation of `remove_small_objects`. Note that the new threshold removes objects smaller than **or equal to** its value, while the previous parameter only removed smaller ones.

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keep_largest

def keep_largest(
    pred_mask:Tensor
)->Tensor:

Keep only the largest connected component in a binary mask.

Args: pred_mask: Binary prediction mask tensor.

Returns: Binary mask with only the largest connected component.

Gradio

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gradio_image_classifier

def gradio_image_classifier(
    file_obj, learn, apply_reorder, target_spacing
):

Predict on images using exported learner and return the result as a dictionary.