Abstract
Generative inpainting of brain MRI volumes is essential for
synthesizing healthy tissue in pathological regions, improving the accuracy
and reliability of automated downstream brain analysis applications
such as image registration, brain extraction, and segmentation. However,
standard 3D approaches are computationally prohibitive, while efficient
2D slice-wise methods suffer from severe inter-slice discontinuities. Furthermore,
traditional models rely on conditional training, requiring taskspecific
learning of masked inputs. We propose a zero-shot brain MRI
inpainting framework utilizing 2.5D unconditional flow priors to capture
spatial context along the superior-inferior axis without the overhead of
full 3D convolutions. During training, our flow matching model learns the
joint distribution of adjacent axial slice triplets, serving as a generative
prior of healthy-appearing tissue while explicitly excluding pathological
regions from the loss function. At inference, the model processes the input
triplets autoregressively along the depth axis. We employ the Restora-
Flow solver to constrain the unconditional prior using the input mask,
achieving accurate zero-shot inpainting. Evaluations show our 2.5D strategy
resolves the structural discontinuities of 2D baselines, synthesizing
plausible healthy tissue while maintaining volumetric consistency across
the axial, sagittal, and coronal planes. As a final step, we generate and
average an ensemble of multiple stochastic reconstructions to form the
final prediction. Quantitative results benchmarked on the official BraTS
2026 Inpainting Challenge validation set demonstrate the effectiveness
of our proposed approach, yielding an SSIM of 0.816 ± 0.112, MSE of
0.007 ± 0.005, and PSNR of 22.923 ± 4.343.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/BraTS_Inpainting_003.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=sm4S2RpZS3
BibTex
@InProceedings{HadArn_ZeroShot_MICCAISAT2026,
author = { Hadzic, Arnela AND Thaler, Franz AND Joham, Simon Johannes AND Urschler, Martin},
title = { { Zero-Shot Brain MRI Inpainting with 2.5D Unconditional Flow Priors } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17254},
month = {pending},
page = {pending}
}