Executables¶

Synthesis Trainer¶

train a patch-based regressor for MR image synthesis

usage: synth-train [-h] -s SOURCE_DIR [SOURCE_DIR ...] -t TARGET_DIR
                   [-o OUTPUT] [-m MASK_DIR] [-r {rf,xg,pr,mlr,mlp}] [-v]
                   [--cross-validate] [-ps PATCH_SIZE] [-fp] [-ns N_SAMPLES]
                   [-cr CTX_RADIUS [CTX_RADIUS ...]] [-th THRESHOLD]
                   [-pd POLY_DEG] [--mean] [--use-xyz] [-n N_JOBS]
                   [-msl MIN_SAMP_LEAF] [-nt N_TREES] [-mf MAX_FEATURES]
                   [-md MAX_DEPTH] [-nr NUM_RESTARTS] [-mi MAX_ITERATIONS]
                   [-hls HIDDEN_LAYER_SIZES [HIDDEN_LAYER_SIZES ...]]
                   [-rs RANDOM_SEED]

Required¶

`-s, --source-dir`
	path to directory with domain images (multiple paths can be provided for multi-modal synthesis, put T1-w images first if they are not skull-stripped)
`-t, --target-dir`
	path to directory with target images

Options¶

`-o, --output`	path to output the trained regressor
`-m, --mask-dir`	optional directory of brain masks for images
`-r, --regr-type`
	Possible choices: rf, xg, pr, mlr, mlp specify type of regressor to use Default: “rf”
`-v, --verbosity`
	increase output verbosity (e.g., -vv is more than -v) Default: 0
`--cross-validate`
	do leave one out cross-validation on the provided dataset (e.g., if 5 datasets are provided, then 5 models are trained where all the data are used except one). Default: False

Synthesis Options¶

`-ps, --patch-size`
	patch size extracted for regression [Default=3] Default: 3
`-fp, --full-patch`
	use the full patch in regression vs a reduced size patch [Default=False] Default: False
`-ns, --n-samples`
	use randomly sampled (with replacement) n_samples voxels for training regressor (None uses all voxels) [Default=None]
`-cr, --ctx-radius`
	context radii to use when extracting patches [Default=(3,5,7)] Default: (3, 5, 7)
`-th, --threshold`
	threshold for foreground and background (above is foreground) [Default=0] Default: 0
`-pd, --poly-deg`
	degree of polynomial features derived from extracted patches (None means do not use polynomial features) [Default=None]
`--mean`	learn to take the mean value of input patch to the mean value of output patches Default: False
`--use-xyz`	use the x,y,z coordinates of voxels as features Default: False

Regressor Options¶

`-n, --n-jobs`	number of processors to use (-1 is all processors) [Default=-1] Default: -1
`-msl, --min-samp-leaf`
	minimum number of samples in each leaf in rf (see min_samples_leaf) [Default=5] Default: 5
`-nt, --n-trees`	number of trees in rf or xg (see n_estimators) [Default=60] Default: 60
`-mf, --max-features`
	proportion of features to use in rf (see max_features) [Default=1/3] Default: 0.3333333333333333
`-md, --max-depth`
	maximum tree depth in rf or xg [Default=None (3 for xg)]
`-nr, --num-restarts`
	number of restarts for mlr (since finds local optimum) [Default=8] Default: 8
`-mi, --max-iterations`
	maximum number of iterations for mlr and mlp [Default=20] Default: 20
`-hls, --hidden-layer-sizes`
	number of neurons in each hidden layer for mlp [Default=(100,)] Default: (100,)
`-rs, --random-seed`
	set random seed for reproducibility [Default=0] Default: 0

Synthesis Predictor¶

synthesize MR images via patch-based regression

usage: synth-predict [-h] -s SOURCE_DIR [SOURCE_DIR ...] -t TRAINED_MODEL
                     [-o OUTPUT_DIR] [-m MASK_DIR] [-v] [--cross-validate]

Required¶

`-s, --source-dir`
	path to directory with domain images
`-t, --trained-model`
	path to the trained model (.pkl)

Options¶

`-o, --output-dir`
	path to output the synthesized images
`-m, --mask-dir`	optional directory of brain masks for images
`-v, --verbosity`
	increase output verbosity (e.g., -vv is more than -v) Default: 0
`--cross-validate`
	do leave one out cross-validation on the provided dataset (e.g., if 5 datasets are provided, then 5 models are trained where all the data are used except one). Default: False