Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.
Equipment was generously donated by the NVIDIA Corporation, and made available by the National Science Foundation (NSF) through grant #CNS-1629914. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.
Shabazi A, Kinnison J, Vescovi R, et al. Flexible learning-free segmentation and reconstruction of neural volumes. Scientific Reports. 2018;8(1). doi:10.1038/s41598-018-32628-3
Shabazi, A., Kinnison, J., Vescovi, R., Du, M., Hill, R., Jösch, M. A., … Scheirer, W. (2018). Flexible learning-free segmentation and reconstruction of neural volumes. Scientific Reports. Nature Publishing Group. https://doi.org/10.1038/s41598-018-32628-3
Shabazi, Ali, Jeffery Kinnison, Rafael Vescovi, Ming Du, Robert Hill, Maximilian A Jösch, Marc Takeno, et al. “Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes.” Scientific Reports. Nature Publishing Group, 2018. https://doi.org/10.1038/s41598-018-32628-3.
A. Shabazi et al., “Flexible learning-free segmentation and reconstruction of neural volumes,” Scientific Reports, vol. 8, no. 1. Nature Publishing Group, 2018.
Shabazi A, Kinnison J, Vescovi R, Du M, Hill R, Jösch MA, Takeno M, Zeng H, Da Costa N, Grutzendler J, Kasthuri N, Scheirer W. 2018. Flexible learning-free segmentation and reconstruction of neural volumes. Scientific Reports. 8(1), 14247.
Shabazi, Ali, et al. “Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes.” Scientific Reports, vol. 8, no. 1, 14247, Nature Publishing Group, 2018, doi:10.1038/s41598-018-32628-3.
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