PreprocessForNNUNetInference

MacroModule

genre	ML_Inference
author	Jan Hendrik Moltz
package	FMEstable/ReleaseMeVis
definition	PreprocessForNNUNetInference.def
keywords	deep, learning, nnUNet, convert, create, generate, input, RedLeaf

Purpose

This module applies a similar preprocessing as nnU-Net and was developed as part of the NNUNetInference macro that allows to run inference on models trained with nnU-Net in MeVisLab. If you use it outside NNUNetInference, you should be familiar with nnU-Net and know what you are doing, but that is typically not necessary.

Usage

The parameters need to be set up according to the plans.pkl or plans.json file that nnU-Net created when training your model. Connect your image and press Update.

Details

The module performs the preprocessing steps that nnU-Net’s own inference would also perform (see http://doi.org/10.1038/s41592-020-01008-z for details):

1. Non-zero cropping: This is always done by nnU-Net but can be disabled by setting Crop input to non\-zero regions because it typically has no effect other than changing the output size.

2. Resampling: nnU-Net expects a fixed input voxel size which is set by Voxel Size. By default nnU-Net uses third-order spline resampling which is here replaced by Lanczos3 resampling for ease of implementation. Like nnU-Net, we use nearest neighbor resampling for the out-of-plane dimension on anisotropic images (maxVoxelSize / minVoxelSize > 3).

3. Intensity normalization: nnU-Net has different normalization schemes depending on the image modality. Normalization Scheme “none” does not normalize at all, “nonCT” subtracts mean and standard deviation of inImage (aka z-score normalization), “CT” uses fixed Mean and Standard Deviation (assuming Hounsfield units) and additionally clips at Lower Clipping Threshold and Upper Clipping Threshold, and “CT2” uses only clipping and no z-scoring. It is also possible to Use only non\-zero regions for nonCT and CT2 z\-score, which happens in nnU-Net if non-zero cropping reduces the image size by more than 25%.

4. Padding: If after cropping and resampling the image is smaller than the desired patch size (Minimum Output Image Size), it is zero-padded.

Windows

Default Panel

Input Fields

inImage

name: inImage, type: Image, deprecated name: input0

Image to be preprocessed (and then to be fed into a trained nnU-Net model)

Output Fields

outImage

name: outImage, type: Image, deprecated name: output0

Preprocessed image that can be fed into a trained nnU-Net model.

outImageWithoutPadding

name: outImageWithoutPadding, type: Image, deprecated name: output0WithoutPadding

Same image as outImage but without padding, i.e. potentially smaller than desired patch size.

Parameter Fields

Field Index

[]: Trigger	Normalization Scheme: Enum	Voxel Size: Vector3
Clear: Trigger	On Input Change Behavior: Enum
Crop input to non-zero regions: Bool	Standard Deviation: Double
doNotClearOnFailedUpdate: Bool	Status Code: Enum
Has Valid Output: Bool	Status Message: String
Lower Clipping Threshold: Double	Update: Trigger
Mean: Double	Upper Clipping Threshold: Double
Minimum Output Image Size: IntVector3	Use only non-zero regions for nonCT and CT2 z-score: Bool

Visible Fields

Update

name: update, type: Trigger

Initiates update of all output field values.

Clear

name: clear, type: Trigger

Clears all output field values to a clean initial state.

On Input Change Behavior

name: onInputChangeBehavior, type: Enum, default: Clear, deprecated name: shouldAutoUpdate,shouldUpdateAutomatically

Declares how the module should react if a value of an input field changes.

Values:

Title	Name	Deprecated Name
Update	Update	TRUE
Clear	Clear	FALSE

[]

name: updateDone, type: Trigger, persistent: no

Notifies that an update was performed (Check status interface fields to identify success or failure).

Has Valid Output

name: hasValidOutput, type: Bool, persistent: no

Indicates validity of output field values (success of computation).

Status Code

name: statusCode, type: Enum, persistent: no

Reflects module’s status (successful or failed computations) as one of some predefined enumeration values.

Values:

Title	Name
Ok	Ok
Invalid input object	Invalid input object
Invalid input parameter	Invalid input parameter
Internal error	Internal error

Status Message

name: statusMessage, type: String, persistent: no

Gives additional, detailed information about status code as human-readable message.

Crop input to non-zero regions

name: inCropNonZeroRegions, type: Bool, default: TRUE

If true, the inImage is reduced to the bounding box of all non-zero voxels (after ApplyGlobalModalityLUT).

Use only non-zero regions for nonCT and CT2 z-score

name: inUseNonZeroRegionForZScore, type: Bool, default: FALSE

If true, only non-zero voxels (AFTER normalizing image values regarding DICOM Rescale Intercept and Slope) are considered when computing mean and standard deviation for z scoring (in nonCT and CT2 z-score normalization mode).

Minimum Output Image Size

name: inMinimumOutImageSize, type: IntVector3, default: 64 64 64, deprecated name: inPatchSize

Desired minimum output image size. Should be equals to the patch size of the neural network applied later on.

Voxel Size

name: inVoxelSize, type: Vector3, default: 1 1 1

Desired output voxel size.

Normalization Scheme

name: inNormalizationScheme, type: Enum, default: nonCT

Scheme by which image values are normalized.

Values:

Title	Name	Description
CT	CT	Normalize by z-scoring with Mean and Standard Deviation and clipping with Lower Clipping Threshold and Upper Clipping Threshold.
CT2	CT2	Normalize by clipping with Lower Clipping Threshold and Upper Clipping Threshold.
nonCT	nonCT	Normalize by z-scoring with mean and standard deviation of input.
none	none	Do not normalize.

Lower Clipping Threshold

name: inLowerClippingThreshold, type: Double, default: 0

Threshold below which image values are clipped if Normalization Scheme is CT or CT2.

Upper Clipping Threshold

name: inUpperClippingThreshold, type: Double, default: 0

Threshold above which image values are clipped if Normalization Scheme is CT or CT2.

Mean

name: inMean, type: Double, default: 0

Value that is subtracted from the image if Normalization Scheme is CT.

Standard Deviation

name: inStandardDeviation, type: Double, default: 0

Value by which the image is divided if Normalization Scheme is CT.

Hidden Fields

doNotClearOnFailedUpdate

name: doNotClearOnFailedUpdate, type: Bool, persistent: no

Prevents automated clear after update failed. This does not affect status fields. It enables the developer to analyze module’s state after failure.