Deep learning - Segmentation Model Inference

None

Parameters

Input_Volume: 3D Volume ( input )

Output_Volume: 3D Volume ( output )

Model: 3D Volume ( input )

Classes: Integer ( input )

Dynamic_Scale_Factor: Float ( input )

Dynamic_Scale_Offset: Float ( input )

Patch_Size: ListOf( Integer ) ( input )

Patch_Overlap: Float ( input )

Buffer_Max_Size: Integer ( input )

N_Jobs: Integer ( input )

Allocate_GPUs: ListOf( Integer ) ( optional, input )

Verbose: Boolean ( input )

Technical information

Toolbox : Bioprocessing

User level : 3

Identifier : DLSegmentationModelInference

File name : brainvisa/toolboxes/bioprocessing/processes/research/projects/segmentation/DLSegmentationModelInference.py

Supported file formats :

Input_Volume :

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

Output_Volume :

gz compressed NIFTI-1 image, DICOM image, Directory, 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