NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images

Ghahremani, Parmida; Boorboor, Saeed; Mirhosseini, Pooya; Gudisagar, Chetan; Ananth, Mala; Talmage, David A.; Role, Lorna W.; Kaufman, Arie

doi:10.1109/tvcg.2021.3109460

Cited by 14 publications

(9 citation statements)

References 49 publications

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“…NeuroConstruct [GBM * 21] adopts a hybrid approach of using intensity correlation (for coarse registration) and feature‐based alignment (for fine tUning) to register segmented light microscopy brain volumes. Following global registration, similar to Yigitsoy and Navab [YN13], a tensor‐based method is used to propagate and register exiting/entering Neurites across adjacent sections.…”

Section: Alignment and Registrationmentioning

confidence: 99%

A Survey of Visualization and Analysis in High‐Resolution Connectomics

Beyer

Troidl

Boorboor³

et al. 2022

Computer Graphics Forum

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The field of connectomics aims to reconstruct the wiring diagram of Neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the Neuronal connectivity, however, relies on many individual steps, starting from high‐resolution data acquisition to automated segmentation, proofreading, interactive data exploration, and circuit analysis. All of these steps have to handle large and complex datasets and rely on or benefit from integrated visualization methods. In this state‐of‐the‐art report, we describe visualization methods that can be applied throughout the connectomics pipeline, from data acquisition to circuit analysis. We first define the different steps of the pipeline and focus on how visualization is currently integrated into these steps. We also survey open science initiatives in connectomics, including usable open‐source tools and publicly available datasets. Finally, we discuss open challenges and possible future directions of this exciting research field.

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Section: Alignment and Registrationmentioning

confidence: 99%

A Survey of Visualization and Analysis in High‐Resolution Connectomics

Beyer

Troidl

Boorboor³

et al. 2022

Computer Graphics Forum

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“…Connectomics [48] is an emerging field for techniques that study complex neural connection maps. By mapping the brain connectivity, neuroscientists will be able to analyze how the human brain functions and its degradation process as a result of cognitive decline or disease [21]. A common challenge in this domain is the immensity of volume data.…”

Section: Applicationsmentioning

confidence: 99%

Geometry-Aware Planar Embedding of Treelike Structures

Hu¹,

Boorboor²,

Marino³

et al. 2022

Preprint

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The growing complexity of spatial and structural information in 3D data makes data inspection and visualization a challenging task. We describe a method to create a planar embedding of 3D treelike structures using their skeleton representations. Our method maintains the original geometry, without overlaps, to the best extent possible, allowing exploration of the topology within a single view. We present a novel camera view generation method which maximizes the visible geometric attributes (segment shape and relative placement between segments). Camera views are created for individual segments and are used to determine local bending angles at each node by projecting them to 2D. The final embedding is generated by minimizing an energy function (the weights of which are user adjustable) based on branch length and the 2D angles, while avoiding intersections. The user can also interactively modify segment placement within the 2D embedding, and the overall embedding will update accordingly. A global to local interactive exploration is provided using hierarchical camera views that are created for subtrees within the structure. We evaluate our method both qualitatively and quantitatively and demonstrate our results by constructing planar visualizations of line data (traced neurons) and volume data (CT vascular and bronchial data).

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“…Their MLP-based model learns particle end locations given their start locations and file cycles. [154] VC-Net 3D volume patch, multislice composited 2D MIP vessel mask Nguyen et al [116] cryo-EM image, dense pseudo labels soft labels Ghahremani et al [40] NeuroConstruct batch of grayscale images probability map He et al [65] super-voxel graph with neighborhood relations feature classification per super-voxel Deng et al [31] Vortex-Net sample local patch hard labels Berenjkoub et al [11] velocity patch binary classification of vortex boundary Kashir et al [81] input map (velocity, vorticity) binary segmentation of vortical structure Borkiewicz et al [15] CloudFindr image patch predicted mask…”

Section: Prediction [ ]mentioning

confidence: 99%

“…They compared three models: 3D U-Net, 3D U-Net+ResNet, and 3D DenseNet, along with three losses: BCE, MSE, and AWL, and reported that 3D U-Net+ResNet with MSE loss works best. Ghahremani et al [40] developed NeuroConstruct to reconstruct 3D neurites from optical microscopy brain images. Their 3D CNN-based segmentation model consists of multiple stages of residual U-block (RSU) connected in the big U-structure.…”

Section: Object Detection and Segmentation [ ]mentioning

confidence: 99%

“…A close look at the categorization under CNN shows that encoder+decoder is the most diverse, covering all five research tasks, while decoder covers two tasks (i.e., data [2](U-Net, deformable CNN), [47] [11](CNN vs. ResNet vs. U-Net), [15](U-Net), [27] (GAN) [49] [50](GAN), [55] (GAN) [35], [40](nested encoder+decoder), [45], [46], [53] [76](ESPCN), [106] (GAN), [126] [54] (GAN), [56] (GAN), [70] (GAN) [159] (FRVSR-Net), [161](EnhanceNet) [81](symmetric FCN), [84](AE), [103](residual AE) [162] (multi-pass GAN) [116](U-Net vs. U-Net+ResNet vs. DenseNet) [167] (ESRGAN, WGAN) [117](AE), [154](multi-stream CNN) [168] (GAN), [182] [158](U-Net, V-Net), [163], [164] MLP [57], [183](FCCNN) - [84], [109], [160] RNN [164](stacked LSTM) [163](LSTM) -GNN [52](GCN), [64](GCN) [131](GCN) -CNN+MLP [24], [26](Siamese) [63] [12] (GAN), [48](AE) [31], [62], [85] [97](Geo-CNN)…”

Section: Network Architecturesmentioning

confidence: 99%

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DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization

Wang¹,

Han²

2022

Preprint

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Since 2016, we have witnessed the tremendous growth of artificial intelligence+visualization (AI+VIS) research. However, existing survey papers on AI+VIS focus on visual analytics and information visualization, not scientific visualization (SciVis). In this paper, we survey related deep learning (DL) works in SciVis, specifically in the direction of DL4SciVis: designing DL solutions for solving SciVis problems. To stay focused, we primarily consider works that handle scalar and vector field data but exclude mesh data. We classify and discuss these works along six dimensions: domain setting, research task, learning type, network architecture, loss function, and evaluation metric. The paper concludes with a discussion of the remaining gaps to fill along the discussed dimensions and the grand challenges we need to tackle as a community. This state-of-the-art survey guides SciVis researchers in gaining an overview of this emerging topic and points out future directions to grow this research.

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NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images

Cited by 14 publications

References 49 publications

A Survey of Visualization and Analysis in High‐Resolution Connectomics

A Survey of Visualization and Analysis in High‐Resolution Connectomics

Geometry-Aware Planar Embedding of Treelike Structures

DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization

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