Publisher Correction: Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

Journal Article · Thu Nov 29 00:00:00 EST 2018 · Scientific Reports

DOI:https://doi.org/10.1038/s41598-018-36220-7· OSTI ID:1629360

Shahbazi, Ali ^[1]; Kinnison, Jeffery  ^[2]; Vescovi, Rafael ^[3]; Du, Ming ^[4]; Hill, Robert  ^[5]; Joesch, Maximilian ^[6]; Takeno, Marc  ^[7]; Zeng, Hongkui  ^[7]; da Costa, Nuno Maçarico ^[7]; Grutzendler, Jaime  ^[5]; Kasthuri, Narayanan ^[3]; Scheirer, Walter J. ^[1]

---

1. Univ. of Notre Dame, Notre Dame, IN (United States). Dept. of Computer Science and Engineering
2. Univ. of Notre Dame, Notre Dame, IN (United States). Dept. of Computer Science and Engineering; 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, Chicago, IL (United States). Department of Neurobiology
4. Northwestern Univ., Evanston, IL (United States). Dept. of Materials Science and Engineering
5. Yale University, New Haven, CT (United States). Dept. of Neurology
6. Neuroethology Group, IST Austria, Klosterneuburg, Austria
7. Allen Institute for Brain Science, Seattle, WA (United States)

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.

• Research Organization: Argonne National Lab (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC02-06CH11357
• OSTI ID: 1629360
• Journal Information: Scientific Reports, Vol. 8, Issue 1; ISSN 2045-2322
• Publisher: Nature Publishing Group
• Country of Publication: United States
• Language: English