AFQ.nn.babyseg

Attributes

logger

Classes

BabySegLabels

Enum where members are also (and must be) ints

Functions

_get_model(model_name)
run_babyseg(ort, t1_img, onnx_kwargs)	Run the BabySeg Model
pve_from_babyseg(babyseg_data)	Compute partial volume estimates from BabySeg segmentation.

Module Contents

AFQ.nn.babyseg.logger

class AFQ.nn.babyseg.BabySegLabels

Bases: enum.IntEnum

Enum where members are also (and must be) ints

BACKGROUND = 0

LEFT_CEREBRAL_WHITE_MATTER = 1

LEFT_CEREBRAL_CORTEX = 2

LEFT_LATERAL_VENTRICLE = 3

LEFT_CEREBELLUM_CORTEX = 4

LEFT_THALAMUS = 5

LEFT_CAUDATE = 6

BRAIN_STEM = 7

LEFT_HIPPOCAMPUS = 8

LEFT_AMYGDALA = 9

LEFT_VENTRAL_DC = 10

RIGHT_CEREBRAL_WHITE_MATTER = 11

RIGHT_CEREBRAL_CORTEX = 12

RIGHT_LATERAL_VENTRICLE = 13

RIGHT_CEREBELLUM_CORTEX = 14

RIGHT_THALAMUS = 15

RIGHT_CAUDATE = 16

RIGHT_HIPPOCAMPUS = 17

RIGHT_AMYGDALA = 18

RIGHT_VENTRAL_DC = 19

LEFT_BASAL_GANGLIA = 20

RIGHT_BASAL_GANGLIA = 21

AFQ.nn.babyseg._get_model(model_name)

AFQ.nn.babyseg.run_babyseg(ort, t1_img, onnx_kwargs)

Run the BabySeg Model

References

[1]

Hoffmann M, Zöllei L, Dalca AV. Deep infant brain segmentation from multi-contrast MRI. Asilomar Conference on Signals, Systems, and Computers, 2025, pp. 974-981. https://arxiv.org/abs/2512.05114

[2]

Hoffmann M. Domain-randomized deep learning for neuroimage analysis. IEEE Signal Processing Magazine, 42(4):78-90, 2025. https://arxiv.org/abs/2507.13458

AFQ.nn.babyseg.pve_from_babyseg(babyseg_data)

Compute partial volume estimates from BabySeg segmentation.

Parameters:

babyseg_datandarray

The output segmentation from BabySeg.

Returns:

pvendarray

PVE data with CSF, GM, and WM segmentations.