Exploring the Connectome: Petascale Volume Visualization of Microscopy Data Streams

Beyer, Johanna; Hadwiger, Markus; Al-Awami, Ali K.; Jeong, Won-Ki; Kasthuri, Narayanan; Lichtman, Jeff W.; Pfister, Hanspeter

doi:10.1109/mcg.2013.55

Cited by 37 publications

(42 citation statements)

References 8 publications

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“…Most visualization tools for connectomics are standalone applications, requiring high-performance workstations and modern GPUs [21][22][23][24][25]; thus, it is harder for these tools to achieve general scalability across both compute and users.…”

Section: Related Workmentioning

confidence: 99%

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Scalable Interactive Visualization for Connectomics

et al. 2017

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Connectomics has recently begun to image brain tissue at nanometer resolution, which produces petabytes of data. This data must be aligned, labeled, proofread, and formed into graphs, and each step of this process requires visualization for human verification. As such, we present the BUTTERFLY middleware, a scalable platform that can handle massive data for interactive visualization in connectomics. Our platform outputs image and geometry data suitable for hardware-accelerated rendering, and abstracts low-level data wrangling to enable faster development of new visualizations. We demonstrate scalability and extendability with a series of open source Web-based applications for every step of the typical connectomics workflow: data management and storage, informative queries, 2D and 3D visualizations, interactive editing, and graph-based analysis. We report design choices for all developed applications and describe typical scenarios of isolated and combined use in everyday connectomics research. In addition, we measure and optimize rendering throughput-from storage to display-in quantitative experiments. Finally, we share insights, experiences, and recommendations for creating an open source data management and interactive visualization platform for connectomics.

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Section: Related Workmentioning

confidence: 99%

“…Hadwiger et al [24] present a volume visualization system for large EM volumes, which was later extended to segmented EM volumes [25]. Several systems, such as Neuron Navigator [31] and Connectome Explorer [15] support interactive or visual queries to further explore these typically very large data sets.…”

Section: Related Workmentioning

confidence: 99%

Scalable Interactive Visualization for Connectomics

et al. 2017

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“…Registration has to be performed between slices and also between individual sub-tiles of a single slice. Several methods focus on registration of large EM data [5,8,42].…”

Section: Related Workmentioning

confidence: 99%

“…The resulting segmentations, on average, require over 120 manual corrections per cubic micron of tissue [30]. Previous research has focused on image acquisition, segmentation [28] and interactive visualization and analysis [16,8,7], but little research has focused on the proofreading stage [38].…”

Section: Introductionmentioning

confidence: 99%

Design and Evaluation of Interactive Proofreading Tools for Connectomics

Haehn¹,

Knowles-Barley

Roberts

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Proofreading with Dojo.We present a web-based application for interactive proofreading of automatic segmentations of connectome data acquired via electron microscopy. Split, merge and adjust functionality enables multiple users to correct the labeling of neurons in a collaborative fashion. Color-coded structures can be explored in 2D and 3D.Abstract-Proofreading refers to the manual correction of automatic segmentations of image data. In connectomics, electron microscopy data is acquired at nanometer-scale resolution and results in very large image volumes of brain tissue that require fully automatic segmentation algorithms to identify cell boundaries. However, these algorithms require hundreds of corrections per cubic micron of tissue. Even though this task is time consuming, it is fairly easy for humans to perform corrections through splitting, merging, and adjusting segments during proofreading. In this paper we present the design and implementation of Mojo, a fully-featured singleuser desktop application for proofreading, and Dojo, a multi-user web-based application for collaborative proofreading. We evaluate the accuracy and speed of Mojo, Dojo, and Raveler, a proofreading tool from Janelia Farm, through a quantitative user study. We designed a between-subjects experiment and asked non-experts to proofread neurons in a publicly available connectomics dataset. Our results show a significant improvement of corrections using web-based Dojo, when given the same amount of time. In addition, all participants using Dojo reported better usability. We discuss our findings and provide an analysis of requirements for designing visual proofreading software.

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“…Margulies et al [32] give an overview of different frameworks for visualizing the human connectome. Hadwiger et al [17] present a system for volume exploration of petavoxel EM data, which was later extended to handle segmented neurites [6]. Several applications have proposed interactive or visual queries to explore these typically very large data sets [10,31,41].…”

Section: Related Workmentioning

confidence: 99%