monailabel.tasks.infer.deepgrow_3d module

class monailabel.tasks.infer.deepgrow_3d.InferDeepgrow3D(path, network=None, type='deepgrow', labels=None, dimension=3, description='A pre-trained 2D DeepGrow model based on UNET', spatial_size=(256, 256), model_size=(128, 192, 192))

Bases: monailabel.interfaces.tasks.infer.InferTask

This provides Inference Engine for Deepgrow-3D pre-trained model. For More Details, Refer https://github.com/Project-MONAI/tutorials/tree/master/deepgrow/ignite

Parameters

• path – Model File Path. Supports multiple paths to support versions (Last item will be picked as latest)

• network – Model Network (e.g. monai.networks.xyz). None in case if you use TorchScript (torch.jit).

• type – Type of Infer (segmentation, deepgrow etc..)

• dimension – Input dimension

• description – Description

• model_state_dict – Key for loading the model state from checkpoint

• input_key – Input key for running inference

• output_label_key – Output key for storing result/label of inference

• output_json_key – Output key for storing result/label of inference

• config – K,V pairs to be part of user config

inferer()

Provide Inferer Class

For Example:

return monai.inferers.SlidingWindowInferer(roi_size=[160, 160, 160])

post_transforms()

Provide List of post-transforms

For Example:

return [
    monai.transforms.AddChanneld(keys='pred'),
    monai.transforms.Activationsd(keys='pred', softmax=True),
    monai.transforms.AsDiscreted(keys='pred', argmax=True),
    monai.transforms.SqueezeDimd(keys='pred', dim=0),
    monai.transforms.ToNumpyd(keys='pred'),
    monailabel.interface.utils.Restored(keys='pred', ref_image='image'),
    monailabel.interface.utils.ExtremePointsd(keys='pred', result='result', points='points'),
    monailabel.interface.utils.BoundingBoxd(keys='pred', result='result', bbox='bbox'),
]

pre_transforms()

Provide List of pre-transforms

For Example:

return [
    monai.transforms.LoadImaged(keys='image'),
    monai.transforms.AddChanneld(keys='image'),
    monai.transforms.Spacingd(keys='image', pixdim=[1.0, 1.0, 1.0]),
    monai.transforms.ScaleIntensityRanged(keys='image',
        a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True),
]

monailabel.tasks.infer.deepgrow_2d module monailabel.tasks.infer.deepgrow_pipeline module