Deep learning - Segmentation Model Inference

None

Paramètres

Input_Volume: Volume 3D ( entrée )

Output_Volume: Volume 3D ( sortie )

Model: Volume 3D ( entrée )

Classes: Entier ( input )

Dynamic_Scale_Factor: Réel ( input )

Dynamic_Scale_Offset: Réel ( input )

Patch_Size: ListOf( Entier ) ( input )

Patch_Overlap: Réel ( input )

Buffer_Max_Size: Entier ( input )

N_Jobs: Entier ( input )

Allocate_GPUs: ListOf( Entier ) ( optional, input )

Verbose: Booléen ( input )

Informations techniques

Toolbox : Bioprocessing

Niveau d'utilisateur : 3

Identifiant : DLSegmentationModelInference

Nom de fichier : brainvisa/toolboxes/bioprocessing/processes/research/projects/segmentation/DLSegmentationModelInference.py

Supported file formats :

Input_Volume :

gz compressed NIFTI-1 image, Aperio svs, DICOM image, Répertoire, 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

Output_Volume :

gz compressed NIFTI-1 image, DICOM image, Répertoire, ECAT i image, ECAT v image, FDF image, GIS image, JPEG image, MINC image, NIFTI-1 image, SPM image, TIFF image, TIFF(.tif) image, gz compressed MINC image, gz compressed NIFTI-1 image

Model :

HDF5 File, HDF5 File