Robust Cell Detection for Large-Scale 3D Microscopy Using GPU-Accelerated Iterative Voting
| Autor: | Laura Montier, Jokubas Ziburkus, David Mayerich, Louise C. Abbott, Leila Saadatifard |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Computational complexity theory Channel (digital image) Computer science media_common.quotation_subject GPU Neuroscience (miscellaneous) KESM Scale (descriptive set theory) Image processing Terabyte lcsh:RC321-571 lcsh:QM1-695 03 medical and health sciences Cellular and Molecular Neuroscience 0302 clinical medicine big data Voting Microscopy cell detection Technology Report lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry media_common Resolution (electron density) lcsh:Human anatomy image processing 030104 developmental biology microscopy Anatomy Algorithm 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | Frontiers in Neuroanatomy Frontiers in Neuroanatomy, Vol 12 (2018) |
| ISSN: | 1662-5129 |
| DOI: | 10.3389/fnana.2018.00028 |
| Popis: | High-throughput imaging techniques, such as Knife-Edge Scanning Microscopy (KESM),are capable of acquiring three-dimensional whole-organ images at sub-micrometer resolution. These images are challenging to segment since they can exceed several terabytes (TB) in size, requiring extremely fast and fully automated algorithms. Staining techniques are limited to contrast agents that can be applied to large samples and imaged in a single pass. This requires maximizing the number of structures labeled in a single channel, resulting in images that are densely packed with spatial features. In this paper, we propose a three-dimensional approach for locating cells based on iterative voting. Due to the computational complexity of this algorithm, a highly efficient GPU implementation is required to make it practical on large data sets. The proposed algorithm has a limited number of input parameters and is highly parallel. |
| Databáze: | OpenAIRE |
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