monailabel.scribbles.infer module

class monailabel.scribbles.infer.HistogramBasedGraphCut(dimension=3, description='A post processing step with histogram-based GraphCut for Generic segmentation', intensity_range=(- 300, 200, 0.0, 1.0), pix_dim=(2.5, 2.5, 5.0))

Bases: monailabel.interfaces.tasks.infer.InferTask

Defines histogram-based GraphCut task for Generic segmentation from the following paper:

Wang, Guotai, et al. “Interactive medical image segmentation using deep learning with image-specific fine tuning.” IEEE transactions on medical imaging 37.7 (2018): 1562-1573. (preprint: https://arxiv.org/pdf/1710.04043.pdf)

This task takes as input 1) original image volume and 2) scribbles from user indicating foreground and background regions. A likelihood volume is generated using histogram method. User-scribbles are incorporated using Equation 7 on page 4 of the paper.

SimpleCRF’s GraphCut layer is used to optimise Equation 5 from the paper, where unaries come from Equation 7 and pairwise is the original input volume.

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.scribbles package monailabel.scribbles.transforms module