4.7 Article

Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

期刊

SCIENTIFIC REPORTS
卷 8, 期 -, 页码 -

出版社

NATURE PUBLISHING GROUP
DOI: 10.1038/s41598-018-32628-3

关键词

-

资金

  1. Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) [D16PC00002]
  2. IARPA via Department of Interior/Interior Business Center (DoI/IBC) [D16PC00004]
  3. National Science Foundation (NSF) [CNS-1629914]
  4. DOE Office of Science User Facility [DE-AC02-06CH11357]
  5. NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE [R00NS099469, R01NS089734] Funding Source: NIH RePORTER

向作者/读者索取更多资源

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 (mu CT) 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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据