We present a method for fast biomedical image atlas construction using neural
fields. Atlases are key to biomedical image analysis tasks, yet conventional
and deep network estimation methods remain time-intensive. In this preliminary
work, we frame subject-specific atlas building as learning a neural field of
deformable spatiotemporal observations. We apply our method to learning
subject-specific atlases and motion stabilization of dynamic BOLD MRI
time-series of fetuses in utero. Our method yields high-quality atlases of
fetal BOLD time-series with $\sim$5-7$\times$ faster convergence compared to
existing work. While our method slightly underperforms well-tuned baselines in
terms of anatomical overlap, it estimates templates significantly faster, thus
enabling rapid processing and stabilization of large databases of 4D dynamic
MRI acquisitions. Code is available at
this https URL

利用神经场快速构建生物医学图像图谱的方法；将个体特定图谱的构建视为学习可变形时空观测的神经场；将该方法应用于胎儿子宫内动态 BOLD MRI 时间序列的学习和运动稳定化；与现有方法相比，我们的方法收敛速度快 5-7 倍，为胎儿 BOLD 时间序列提供高质量的图谱，虽然在解剖重叠方面略低于优化的基准模型，但在模板估计方面显著提高了速度，从而使得大型数据库的 4D 动态 MRI 采集能够快速处理和稳定。

学习 4D 胎儿磁共振时间序列的动态神经场

Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series

We propose an approach to detect flying objects such as UAVs and aircrafts
when they occupy a small portion of the field of view, possibly moving against
complex backgrounds, and are filmed by a camera that itself moves.
Solving such a difficult problem requires combining both appearance and
motion cues. To this end we propose a regression-based approach to motion
stabilization of local image patches that allows us to achieve effective
classification on spatio-temporal image cubes and outperform state-of-the-art
techniques.
As the problem is relatively new, we collected two challenging datasets for
UAVs and Aircrafts, which can be used as benchmarks for flying objects
detection and vision-guided collision avoidance.

提出了一种基于回归的运动稳定方法，旨在检测被拍摄的机动飞行器在复杂背景中移动的情况，该方法在运动与外观两方面共同作用下可以对比先进技术更有效地运用时空图像块进行分类，并通过收集两个具有挑战性的数据集为飞行物体检测和视觉引导式避碰创建基准。