[Parcellation] Generate segmentation scores

Generate segmentation scores (precision, recall, F1) for a given parcellation based on a reference.

Parameters

input_type: OpenChoice ( input )

This parameter allows to change the database type of the input segmentation. It helps to automatically fill the process with the corresponding masks and reference.

input_segmentation: P:EM Parcellation ( input )

The evaluated segmentation.

input_mask: P:Skull Stripping Mask ( optional, input )

A mask in which scores should be computed. If a skull-stripping mask was used prior to the automated segmentation, it allows to discard voxels outisde this mask.

input_reference: P:Reference Parcellation ( input )

Tee ground truth segmentation.

input_reference_mask: P:Reference Parcellation Mask ( optional, input )

A mask in which to compute segmentation scores. If the reference segmentation does not cover the entire volume (if only a few section were selected for example), it allows to only use voxels which have an actual ground truth.

input_hierarchy: P:Atlas Labels Hierarchy ( input )

Hierarchy linking class values to regions. It also allows to compute classification scores in a hierarchical manner, by aggregating regions.

subject: String ( optional, input )

Subject name or ID. It will be stored in the output CSV.

analysis: ListOf( String ) ( optional, input )

Analysis name. It will be stored in the output CSV. Allows to keep track of parameters or to compare different algorithms.

output_directory: Directory ( optional, input )

Directory to write the output CSV.

output_csv: CSV file ( output )

Output table. One score per label and per hierarchy node are computed, plus three averaged scores (micro, macro, weighted).

Technical information

Toolbox : Primatologist

User level : 0

Identifier : segmentationScores

File name : brainvisa/toolboxes/primatologist/processes/tools/segmentationScores.py

Supported file formats :

input_segmentation :

gz compressed NIFTI-1 image, Aperio svs, DICOM image, Directory, ECAT i image, ECAT v image, FDF image, FreesurferMGH, FreesurferMGZ, GIS image, Hamamatsu ndpi, Hamamatsu vms, Hamamatsu vmu, JPEG image, Leica scn, MINC image, NIFTI-1 image, SPM image, Sakura svslide, TIFF image, TIFF image, TIFF(.tif) image, TIFF(.tif) image, Ventana bif, Zeiss czi, gz compressed MINC image, gz compressed NIFTI-1 image

input_mask :

gz compressed NIFTI-1 image, Aperio svs, DICOM image, Directory, ECAT i image, ECAT v image, FDF image, FreesurferMGH, FreesurferMGZ, GIS image, Hamamatsu ndpi, Hamamatsu vms, Hamamatsu vmu, JPEG image, Leica scn, MINC image, NIFTI-1 image, SPM image, Sakura svslide, TIFF image, TIFF image, TIFF(.tif) image, TIFF(.tif) image, Ventana bif, Zeiss czi, gz compressed MINC image, gz compressed NIFTI-1 image

input_reference :

gz compressed NIFTI-1 image, Aperio svs, DICOM image, Directory, ECAT i image, ECAT v image, FDF image, FreesurferMGH, FreesurferMGZ, GIS image, Hamamatsu ndpi, Hamamatsu vms, Hamamatsu vmu, JPEG image, Leica scn, MINC image, NIFTI-1 image, SPM image, Sakura svslide, TIFF image, TIFF image, TIFF(.tif) image, TIFF(.tif) image, Ventana bif, Zeiss czi, gz compressed MINC image, gz compressed NIFTI-1 image

input_reference_mask :

gz compressed NIFTI-1 image, Aperio svs, DICOM image, Directory, ECAT i image, ECAT v image, FDF image, FreesurferMGH, FreesurferMGZ, GIS image, Hamamatsu ndpi, Hamamatsu vms, Hamamatsu vmu, JPEG image, Leica scn, MINC image, NIFTI-1 image, SPM image, Sakura svslide, TIFF image, TIFF image, TIFF(.tif) image, TIFF(.tif) image, Ventana bif, Zeiss czi, gz compressed MINC image, gz compressed NIFTI-1 image

input_hierarchy :

Hierarchy, Hierarchy

output_directory :

Directory, Directory

output_csv :

CSV file, CSV file