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.

我们引入了一种条件隐式神经图谱 (CINA)，用于从正常和病理性胎儿脑的磁共振图像 (MRI) 中生成时空图谱，完全不依赖于仿射或非刚性配准。CINA 在训练过程中学习了胎儿大脑的普遍表示，并将个体特定信息编码为潜在代码。在训练完毕后，CINA 可以构建一个忠实的图谱，其中包含任何妊娠时期和训练领域内涵盖的解剖变异的胎儿大脑的组织概率图。因此，CINA 能够表示正常和病理性大脑。此外，训练完的 CINA 模型可通过对潜在代码进行测试时优化，适应未见过的被试的脑 MRI。然后，CINA 可以生成适用于特定被试的概率组织图。我们在来自 dHCP 和 FeTA 数据集的共 198 例正常和异常胎儿大脑的磁共振图像上评估了我们的方法。我们展示了 CINA 表示胎儿大脑图谱的能力，该图谱可以在妊娠时期和脑解剖变异（如脑室容积或皮质折叠程度）上灵活进行条件。这使得它成为对正常和病理性大脑建模的合适工具。我们通过组织分割和年龄预测来量化我们图谱的准确性，并将其与现有基线进行比较。CINA 在正常大脑和脑室扩大的病理性大脑方面表现出更高的准确性。此外，CINA 在胎儿大脑年龄预测中得分的平均绝对误差为 0.23 周，进一步证实了胎儿大脑发育的准确表示。