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Title: Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

Journal Article · · Scientific Reports
 [1]; ORCiD logo [2];  [3];  [4]; ORCiD logo [5];  [6]; ORCiD logo [7]; ORCiD logo [7];  [7]; ORCiD logo [5];  [3];  [1]
  1. Univ. of Notre Dame, IN (United States)
  2. Univ. of Notre Dame, IN (United States); Argonne National Lab. (ANL), Argonne, IL (United States). Center for Nanoscale Materials
  3. Argonne National Lab. (ANL), Argonne, IL (United States). Center for Nanoscale Materials; Univ. of Chicago, IL (United States)
  4. Northwestern Univ., Evanston, IL (United States)
  5. Yale Univ., New Haven, CT (United States)
  6. Institute of Science and Technology Austria (IST) (Austria)
  7. Allen Inst. for Brain Science, Seattle, WA (United States)
+ Show Author Affiliations

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-specifc contextual clues and requires no training. This approach generalizes across diferent modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal refectance (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 fdelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufcient 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.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States). Argonne Leadership Computing Facility (ALCF)
Sponsoring Organization:
USDOE Office of Science (SC); Office of the Director of National Intelligence (ODNI); Intelligence Advanced Research Projects Activity (IARPA); NVIDIA Corporation; National Science Foundation (NSF)
Grant/Contract Number:
AC02-06CH11357; D16PC00002; D16PC00004; CNS-1629914
OSTI ID:
1624428
Journal Information:
Scientific Reports, Vol. 8, Issue 1; ISSN 2045-2322
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 9 works
Citation information provided by
Web of Science

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Figures / Tables (8)

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