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Unsupervised OCT Image Interpolation Using Deformable Registration and generative models

delete2026-01-01
delete0
PRE
AI
S
Shuwen Wei *
S
Samuel W. Remedios
Z
Zhangxing Bian
S
Shimeng Wang
J
Junyu Chen
Y
Yihao Liu
B
Bruno Jedynak
T
T. Y. Alvin Liu
S
Shiv Saidha
P
Peter A. Calabresi
J
Jerry L. Prince
A
Aaron Carass
DOI：10.1007/978-3-032-04965-0_62delete
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摘要

摘要

En 中文
光学相干层析成像（OCT）图像通常被采集为高度各向异性的体数据，其中扫描步长在快轴方向密集而在慢轴方向稀疏。这影响了图像分析，例如纵向对齐的图像配准。为创建更各向同性的体数据，可采用双三次插值沿慢轴进行插值，但这通常会生成模糊特征。基于配准的插值可以减少模糊，但往往难以生成逼真的OCT图像。深度生成模型可以采样出逼真的图像，但缺乏插值所需的局部结构一致性约束。在本文中，我们提出了一种无监督的图像插值方法，结合了基于配准的插值和深度生成模型，以克服各自的局限性并提高插值OCT图像的结构准确性和真实感。我们在真实的OCT数据集上比较了所提出的方法与双三次插值和基于配准的插值，并表明该方法实现了最佳的插值性能。
Keyword:
Unsupervised learning
Optical coherence tomography
Image interpolation
Deformable registration
Generative model

期刊

M
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2025, PT IV
IF:
0
论文数:
56
被引数:
0

机构

P
portland state university
学者数:
369
论文数: 246
被引数: 0
V
vanderbilt university
学者数:
5.1W
论文数: 4.1W
被引数: 59
J
Johns Hopkins Medicine
学者数:
1.8W
论文数: 1.3W
被引数: 5.0W
 
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