CINA: Conditional Implicit Neural Atlas for Spatio-Temporal Representation of Fetal Brains
<|reference_start|>CINA: Conditional Implicit Neural Atlas for Spatio-Temporal Representation of Fetal Brains: 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.<|reference_end|>
@article{dannecker2024cina:,
    title={CINA: Conditional Implicit Neural Atlas for Spatio-Temporal
  Representation of Fetal Brains},
    author={Maik Dannecker, Vanessa Kyriakopoulou, Lucilio Cordero-Grande, Anthony
  N. Price, Joseph V. Hajnal, Daniel Rueckert},
    journal={arXiv preprint arXiv:2403.08550},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08550},
    primaryClass={cs.LG cs.CV}
}