Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

Chung, Ji Ryang; Sung, Chul; Mayerich, David; Kwon, Jaerock; Miller, Daniel E.; Huffman, T. B.; Keyser, John; Abbott, Louise C.; Choe, Yoonsuck

doi:10.3389/fninf.2011.00029

Cited by 24 publications

(12 citation statements)

References 82 publications

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“…The Knife-Edge Scanning Microscope (KESM) [5], [6], shown in Fig. 1, is one of the first instruments to produce sub-micrometer resolution (∼1µm 3 ) data from whole small animal brains.…”

Section: Introductionmentioning

confidence: 99%

“…1, is one of the first instruments to produce sub-micrometer resolution (∼1µm 3 ) data from whole small animal brains. We successfully imaged, using the KESM, entire mouse brains stained with Golgi (neuronal morphology) [6], [7], [5], India ink (vascular network) [8], and Nissl (soma distribution) [7], [9]. The Nissl data set in particular enables detailed studies of whole-brain cortical and subcortical distribution of neuronal cell bodies.…”

Section: Introductionmentioning

confidence: 99%

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Scalable, incremental learning with MapReduce parallelization for cell detection in high-resolution 3D microscopy data

Sung

Woo

Goodman³

et al. 2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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Accurate estimation of neuronal count and distribution is central to the understanding of the organization and layout of cortical maps in the brain, and changes in the cell population induced by brain disorders. High-throughput 3D microscopy techniques such as Knife-Edge Scanning Microscopy (KESM) are enabling whole-brain survey of neuronal distributions. Data from such techniques pose serious challenges to quantitative analysis due to the massive, growing, and sparsely labeled nature of the data. In this paper, we present a scalable, incremental learning algorithm for cell body detection that can address these issues. Our algorithm is computationally efficient (linear mapping, non-iterative) and does not require retraining (unlike gradient-based approaches) or retention of old raw data (unlike instance-based learning). We tested our algorithm on our rat brain Nissl data set, showing superior performance compared to an artificial neural network-based benchmark, and also demonstrated robust performance in a scenario where the data set is rapidly growing in size. Our algorithm is also highly parallelizable due to its incremental nature, and we demonstrated this empirically using a MapReduce-based implementation of the algorithm. We expect our scalable, incremental learning approach to be widely applicable to medical imaging domains where there is a constant flux of new data.

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“…The Knife-Edge Scanning Microscope (KESM) [5], [6], shown in Fig. 1, is one of the first instruments to produce sub-micrometer resolution (∼1µm 3 ) data from whole small animal brains.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scalable, incremental learning with MapReduce parallelization for cell detection in high-resolution 3D microscopy data

Sung

Woo

Goodman³

et al. 2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

Self Cite

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“…The capabilities of the 2D sectioning techniques have been elevated in recent years by high-throughput processing of sections and imaging methods (e.g. Chung et al 2011 ; Ragan et al 2012 ; Osten and Margrie 2013 ) along with relevant computational routines. This made it possible to conduct large-scale projects on gene expression (Lein et al 2007 ), connectivity (Oh et al 2014 ), and relating brain changes to behavior (Vousden et al 2014 ; Kim et al 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Possum—A Framework for Three-Dimensional Reconstruction of Brain Images from Serial Sections

Majka

Wójcik

2015

Neuroinform

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Techniques based on imaging serial sections of brain tissue provide insight into brain structure and function. However, to compare or combine them with results from three dimensional imaging methods, reconstruction into a volumetric form is required. Currently, there are no tools for performing such a task in a streamlined way. Here we propose the Possum volumetric reconstruction framework which provides a selection of 2D to 3D image reconstruction routines allowing one to build workflows tailored to one’s specific requirements. The main components include routines for reconstruction with or without using external reference and solutions for typical issues encountered during the reconstruction process, such as propagation of the registration errors due to distorted sections. We validate the implementation using synthetic datasets and actual experimental imaging data derived from publicly available resources. We also evaluate efficiency of a subset of the algorithms implemented. The Possum framework is distributed under MIT license and it provides researchers with a possibility of building reconstruction workflows from existing components, without the need for low-level implementation. As a consequence, it also facilitates sharing and data exchange between researchers and laboratories.

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“…To address this challenge, the KESM Brain Atlas (KESMBA) was developed (Chung, Sung, 1high-speed line-scan camera, (2) microscope objective, (3) diamond knife assembly and light collimator, (4) specimen tank (5) three-axis precision stage, (6) white-light microscope illuminator, (7) water pump for the removal of sectioned tissue, (8) PC for stage control and image acquisition, (9) granite base, and (10) granite bridge. Mayerich, Kwon, Miller, Huffman, Abbott, Keyser, and Choe, 2011). This system is built on the Google Maps API, using multi-scale tiles with pseudo-3D rendering through transparent overlays.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Image Processing Pipeline and Parallel Generation of Multiscale Tiles for Web-based 3D Rendering of Whole Mouse Brain Vascular Networks

Kwon

2014

Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods

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Mapping out the complex vascular network in the brain is critical for understanding the transport of oxygen, nutrition, and signaling molecules. The vascular network can also provide us with clues to the relationship between neural activity and blood oxygen-related signals. Advanced high-throughput 3D imaging instruments such as the Knife-Edge Scanning Microscope (KESM) are enabling the imaging of the full vascular network in small animal brains (e.g., the mouse) at sub-micrometer resolution. The amount of data per brain (for KESM) is on the order of 2TB, thus it is a major challenge just to visualize it at full resolution. In this paper, we present an enhanced image processing pipeline for KESM mouse vascular network data set, and a parallel multi-scale tile generation system for web-based pseudo-3D rendering. The system allows full navigation of the data set at all resolution scales. We expect our approach to help in broader dissemination of large-scale, high-resolution 3D microscopy data.

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Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

Cited by 24 publications

References 82 publications

Scalable, incremental learning with MapReduce parallelization for cell detection in high-resolution 3D microscopy data

Scalable, incremental learning with MapReduce parallelization for cell detection in high-resolution 3D microscopy data

Possum—A Framework for Three-Dimensional Reconstruction of Brain Images from Serial Sections

Enhanced Image Processing Pipeline and Parallel Generation of Multiscale Tiles for Web-based 3D Rendering of Whole Mouse Brain Vascular Networks

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