Unbiased Atlas Construction for Neonatal Cortical Surfaces via Unsupervised Learning

Cheng, Jieyu; Dalca, Adrian V.; Zöllei, Lilla

doi:10.1007/978-3-030-60334-2_33

Cited by 3 publications

(1 citation statement)

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“…Further, as a single template cannot capture the high levels of structural variability within a population (e.g., age and cohort), we consider conditional template estimation with continuous and/or categorical attributes. Conditional templates constructed on image sets with diverse covariates enable subpopulation modeling accounting for information learned from the overall population and obviate the need for arbitrary thresholding of demographic information to perform independent analyses [22,25,104].…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Registration for Improved Conditional Deformable Templates

Dey¹,

Ren²,

Dalca³

et al. 2021

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Deformable templates are essential to large-scale medical image registration, segmentation, and population analysis. Current conventional and deep network-based methods for template construction use only regularized registration objectives and often yield templates with blurry and/or anatomically implausible appearance, confounding downstream biomedical interpretation. We reformulate deformable registration and conditional template estimation as an adversarial game wherein we encourage realism in the moved templates with a generative adversarial registration framework conditioned on flexible image covariates. The resulting templates exhibit significant gain in specificity to attributes such as age and disease, better fit underlying group-wise spatiotemporal trends, and achieve improved sharpness and centrality. These improvements enable more accurate population modeling with diverse covariates for standardized downstream analyses and easier anatomical delineation for structures of interest.

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Section: Introductionmentioning

confidence: 99%