计算机科学
人工智能
可视化
变形
深度学习
模式识别(心理学)
管道(软件)
人工神经网络
人口
生成对抗网络
脑形态计量学
计算机视觉
机器学习
磁共振成像
社会学
人口学
放射科
程序设计语言
医学
作者
Saeed Boorboor,Shawn Mathew,Mala Ananth,David A. Talmage,Lorna W. Role,Arie Kaufman
出处
期刊:IEEE Transactions on Visualization and Computer Graphics
[Institute of Electrical and Electronics Engineers]
日期:2023-03-01
卷期号:29 (3): 1625-1637
被引量:4
标识
DOI:10.1109/tvcg.2021.3127132
摘要
Recent advances in high-resolution microscopy have allowed scientists to better understand the underlying brain connectivity. However, due to the limitation that biological specimens can only be imaged at a single timepoint, studying changes to neural projections is limited to general observations using population analysis. In this paper, we introduce NeuRegenerate, a novel end-to-end framework for the prediction and visualization of changes in neural fiber morphology within a subject, for specified age-timepoints.To predict projections, we present neuReGANerator, a deep-learning network based on cycle-consistent generative adversarial network (cycleGAN) that translates features of neuronal structures in a region, across age-timepoints, for large brain microscopy volumes. We improve the reconstruction quality of neuronal structures by implementing a density multiplier and a new loss function, called the hallucination loss.Moreover, to alleviate artifacts that occur due to tiling of large input volumes, we introduce a spatial-consistency module in the training pipeline of neuReGANerator. We show that neuReGANerator has a reconstruction accuracy of 94% in predicting neuronal structures. Finally, to visualize the predicted change in projections, NeuRegenerate offers two modes: (1) neuroCompare to simultaneously visualize the difference in the structures of the neuronal projections, across the age timepoints, and (2) neuroMorph, a vesselness-based morphing technique to interactively visualize the transformation of the structures from one age-timepoint to the other. Our framework is designed specifically for volumes acquired using wide-field microscopy. We demonstrate our framework by visualizing the structural changes in neuronal fibers within the cholinergic system of the mouse brain between a young and old specimen.
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