Real-time 3D fluorescence microscopy is crucial for the spatiotemporal
analysis of live organisms, such as neural activity monitoring. The eXtended
field-of-view light field microscope (XLFM), also known as Fourier light field
microscope, is a straightforward, single snapshot solution to achieve this. The
XLFM acquires spatial-angular information in a single camera exposure. In a
subsequent step, a 3D volume can be algorithmically reconstructed, making it
exceptionally well-suited for real-time 3D acquisition and potential analysis.
Unfortunately, traditional reconstruction methods (like deconvolution) require
lengthy processing times (0.0220 Hz), hampering the speed advantages of the
XLFM. Neural network architectures can overcome the speed constraints at the
expense of lacking certainty metrics, which renders them untrustworthy for the
biomedical realm. This work proposes a novel architecture to perform fast 3D
reconstructions of live immobilized zebrafish neural activity based on a
conditional normalizing flow. It reconstructs volumes at 8 Hz spanning
512x512x96 voxels, and it can be trained in under two hours due to the small
dataset requirements (10 image-volume pairs). Furthermore, normalizing flows
allow for exact Likelihood computation, enabling distribution monitoring,
followed by out-of-distribution detection and retraining of the system when a
novel sample is detected. We evaluate the proposed method on a cross-validation
approach involving multiple in-distribution samples (genetically identical
zebrafish) and various out-of-distribution ones.

本文提出了一种基于条件归一化流的新架构，可实现快速三维立体重建，对于实时三维采集和潜在分析非常适用，并能够实现精确的似然度计算，从而进行分布监测、基于样本检测的重训练。

条件归一化流的快速光场三维显微镜，具有分布外检测和自适应能力

Fast light-field 3D microscopy with out-of-distribution detection and  adaptation through Conditional Normalizing Flows

3D fluorescence microscopy of living organisms has increasingly become an
essential and powerful tool in biomedical research and diagnosis. An exploding
amount of imaging data has been collected, whereas efficient and effective
computational tools to extract information from them are still lagging behind.
This is largely due to the challenges in analyzing biological data. Interesting
biological structures are not only small, but are often morphologically
irregular and highly dynamic. Although tracking cells in live organisms has
been studied for years, existing tracking methods for cells are not effective
in tracking subcellular structures, such as protein complexes, which feature in
continuous morphological changes including split and merge, in addition to fast
migration and complex motion. In this paper, we first define the problem of
multi-object portion tracking to model the protein object tracking process. A
multi-object tracking method with portion matching is proposed based on 3D
segmentation results. The proposed method distills deep feature maps from deep
networks, then recognizes and matches object portions using an extended search.
Experimental results confirm that the proposed method achieves 2.96% higher on
consistent tracking accuracy and 35.48% higher on event identification accuracy
than the state-of-art methods.

本文提出了一种基于 3D 分割结果的多目标跟踪方法，该方法可通过提取深度神经网络中的特征图来识别和匹配物体部分，该方法在跟踪准确性和事件识别准确性方面比现有的方法分别提高了 2.96% 和 35.48%。