sct_deepseg_gm¶

S p i n a l C o r d G r a y M a t t e r ( G M ) S e g m e n t a t i o n u s i n g d e e p d i l a t e d c o n v o l u t i o n s . T h e c o n t r a s t o f t h e i n p u t i m a g e m u s t b e s i m i l a r t o a T 2 * - w e i g h t e d i m a g e : W M d a r k , G M b r i g h t a n d C S F b r i g h t . R e f e r e n c e : P e r o n e C S , C a l a b r e s e E , C o h e n - A d a d J . S p i n a l c o r d g r a y m a t t e r s e g m e n t a t i o n u s i n g d e e p d i l a t e d c o n v o l u t i o n s . S c i R e p 2 0 1 8 ; 8 ( 1 ) : 5 9 6 6 . Spinal Cord Gray Matter (GM) Segmentation using deep dilated convolutions. The contrast of the input image must be similar to a T2*-weighted image: WM dark, GM bright and CSF bright. Reference: Perone CS, Calabrese E, Cohen-Adad J. Spinal cord gray matter segmentation using deep dilated convolutions. Sci Rep 2018;8(1):5966.

usage: sct_deepseg_gm -i <file> [-o <file>] [-m {large,challenge}]
                      [-thr <float>] [-t] [-qc <str>] [-qc-dataset <str>]
                      [-qc-subject <str>] [-h] [-v <int>]

MANDATORY ARGUMENTS¶

-i: I m a g e f i l e n a m e t o s e g m e n t ( 3 D v o l u m e ) . E x a m p l e : ` t 2 s . n i i . g z ` . Image filename to segment (3D volume). Example: t2s.nii.gz.

OPTIONAL ARGUMENTS¶

-o

O u t p u t s e g m e n t a t i o n f i l e n a m e . E x a m p l e : ` s c _ g m _ s e g . n i i . g z ` Output segmentation file name. Example: sc_gm_seg.nii.gz

-m

P o s s i b l e c h o i c e s : l a r g e , c h a l l e n g e Possible choices: large, challenge

M o d e l t o u s e ( l a r g e o r c h a l l e n g e ) . T h e m o d e l ‘ l a r g e ‘ w i l l b e s l o w e r b u t w i l l y i e l d b e t t e r r e s u l t s . T h e m o d e l ‘ c h a l l e n g e ‘ w a s b u i l t u s i n g d a t a f r o m t h e f o l l o w i n g c h a l l e n g e : g o o . g l / h 4 A V a r . Model to use (large or challenge). The model ‘large’ will be slower but will yield better results. The model ‘challenge’ was built using data from the following challenge: goo.gl/h4AVar.

D e f a u l t : ` ` ‘ l a r g e ‘ ` ` Default: 'large'

-thr

T h r e s h o l d t o a p p l y i n t h e s e g m e n t a t i o n p r e d i c t i o n s , u s e 0 ( z e r o ) t o d i s a b l e i t . Threshold to apply in the segmentation predictions, use 0 (zero) to disable it.

D e f a u l t : ` ` 0 . 9 9 9 ` ` Default: 0.999

-t

E n a b l e T T A ( t e s t - t i m e a u g m e n t a t i o n ) . B e t t e r r e s u l t s , b u t t a k e s m o r e t i m e a n d p r o v i d e s n o n - d e t e r m i n i s t i c r e s u l t s . Enable TTA (test-time augmentation). Better results, but takes more time and provides non-deterministic results.

MISC ARGUMENTS¶

-qc

T h e p a t h w h e r e t h e q u a l i t y c o n t r o l g e n e r a t e d c o n t e n t w i l l b e s a v e d . The path where the quality control generated content will be saved.

-qc-dataset

I f p r o v i d e d , t h i s s t r i n g w i l l b e m e n t i o n e d i n t h e Q C r e p o r t a s t h e d a t a s e t t h e p r o c e s s w a s r u n o n If provided, this string will be mentioned in the QC report as the dataset the process was run on

-qc-subject

I f p r o v i d e d , t h i s s t r i n g w i l l b e m e n t i o n e d i n t h e Q C r e p o r t a s t h e s u b j e c t t h e p r o c e s s w a s r u n o n If provided, this string will be mentioned in the QC report as the subject the process was run on

-v

P o s s i b l e c h o i c e s : 0 , 1 , 2 Possible choices: 0, 1, 2

V e r b o s i t y . 0 : D i s p l a y o n l y e r r o r s / w a r n i n g s , 1 : E r r o r s / w a r n i n g s + i n f o m e s s a g e s , 2 : D e b u g m o d e . Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode.

D e f a u l t : ` ` 1 ` ` Default: 1