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Real-time 3D fluorescence microscopy is crucial for the spatiotemporalanalysis of live organisms, such as neural activity monitoring. The eXtendedfield-of-view light field microscope (XLFM), also known as Fourier light fieldmicroscope, is a straightforward, single snapshot solution to achieve this. TheXLFM acquires spatial-angular information in a single camera exposure. In asubsequent step, a 3D volume can be algorithmically reconstructed, making itexceptionally well-suited for real-time 3D acquisition and potential analysis.Unfortunately, traditional reconstruction methods (like deconvolution) requirelengthy processing times (0.0220 Hz), hampering the speed advantages of theXLFM. Neural network architectures can overcome the speed constraints at theexpense of lacking certainty metrics, which renders them untrustworthy for thebiomedical realm. This work proposes a novel architecture to perform fast 3Dreconstructions of live immobilized zebrafish neural activity based on aconditional normalizing flow. It reconstructs volumes at 8 Hz spanning512x512x96 voxels, and it can be trained in under two hours due to the smalldataset requirements (10 image-volume pairs). Furthermore, normalizing flowsallow for exact Likelihood computation, enabling distribution monitoring,followed by out-of-distribution detection and retraining of the system when anovel sample is detected. We evaluate the proposed method on a cross-validationapproach involving multiple in-distribution samples (genetically identicalzebrafish) and various out-of-distribution ones.

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