CINA: Conditional Implicit Neural Atlas for Spatio-Temporal
  Representation of Fetal Brains
Maik Dannecker Vanessa Kyriakopoulou Lucilio Cordero-Grande Anthony N. Price Joseph V. Hajnal Daniel Rueckert
We introduce a conditional implicit neural atlas (CINA) for spatio-temporal
atlas generation from Magnetic Resonance Images (MRI) of the neurotypical and
pathological fetal brain, that is fully independent of affine or non-rigid
registration. During training, CINA learns a general representation of the
fetal brain and encodes subject specific information into latent code. After
training, CINA can construct a faithful atlas with tissue probability maps of
the fetal brain for any gestational age (GA) and anatomical variation covered
within the training domain. Thus, CINA is competent to represent both,
neurotypical and pathological brains. Furthermore, a trained CINA model can be
fit to brain MRI of unseen subjects via test-time optimization of the latent
code. CINA can then produce probabilistic tissue maps tailored to a particular
subject. We evaluate our method on a total of 198 T2 weighted MRI of normal and
abnormal fetal brains from the dHCP and FeTA datasets. We demonstrate CINA's
capability to represent a fetal brain atlas that can be flexibly conditioned on
GA and on anatomical variations like ventricular volume or degree of cortical
folding, making it a suitable tool for modeling both neurotypical and
pathological brains. We quantify the fidelity of our atlas by means of tissue
segmentation and age prediction and compare it to an established baseline. CINA
demonstrates superior accuracy for neurotypical brains and pathological brains
with ventriculomegaly. Moreover, CINA scores a mean absolute error of 0.23
weeks in fetal brain age prediction, further confirming an accurate
representation of fetal brain development.