NeuRegenerate: A Framework for Visualizing Neurodegeneration

Boorboor, Saeed; Mathew, Shawn; Ananth, Mala; Talmage, David A.; Role, Lorna W.; Kaufman, Arie

doi:10.1109/tvcg.2021.3127132

Cited by 6 publications

(5 citation statements)

References 39 publications

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“…Thus, the global loss for image is defined by incorporating the alpha factor, , to ensure that the loss is representative of the actual class imbalance on a per-image basis. This approach shares conceptual similarities with methodologies such as NeuRegenerate’s density multiplier 9 , which adapts model behavior to address the tile-stitching artifacts. In NeuRegenerate’s case, this adaptation is based on the overlap between synthetic and real inputs in a 3D volumetric context, particularly when computing the reconstruction loss within a generative adversarial network setting.…”

Section: Methodsmentioning

confidence: 99%

“…These developments have important implications for numerous biological experiments that explore the dynamics and the behavior of immune cells. In particular, the process of phagocytosis, in which microglial cells engulf and eliminate protein deposits or aggregates, has garnered attention in the field of neurodegenerative diseases 4 – 9 . A deeper understanding of this phenomenon is essential for elucidating the complex mechanisms underlying these disorders and their progression.…”

Section: Introductionmentioning

confidence: 99%

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PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies

Ounissi,

Latouche,

Racoceanu

2024

Sci Rep

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Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells from background make this task very challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce an end-to-end, scalable, and versatile real-time framework for quantifying and analyzing phagocytic activity. Our proposed pipeline is able to process large data-sets and includes a data quality verification module to counteract potential perturbations such as microscope movements and frame blurring. We also propose an explainable cell segmentation module to improve the interpretability of deep learning methods compared to black-box algorithms. This includes two interpretable deep learning capabilities: visual explanation and model simplification. We demonstrate that interpretability in deep learning is not the opposite of high performance, by additionally providing essential deep learning algorithm optimization insights and solutions. Besides, incorporating interpretable modules results in an efficient architecture design and optimized execution time. We apply this pipeline to quantify and analyze microglial cell phagocytosis in frontotemporal dementia (FTD) and obtain statistically reliable results showing that FTD mutant cells are larger and more aggressive than control cells. The method has been tested and validated on several public benchmarks by generating state-of-the art performances. To stimulate translational approaches and future studies, we release an open-source end-to-end pipeline and a unique microglial cells phagocytosis dataset for immune system characterization in neurodegenerative diseases research. This pipeline and the associated dataset will consistently crystallize future advances in this field, promoting the development of efficient and effective interpretable algorithms dedicated to the critical domain of neurodegenerative diseases’ characterization. https://github.com/ounissimehdi/PhagoStat.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies

Ounissi,

Latouche,

Racoceanu

2024

Sci Rep

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“…A planar visualization for studying corresponding structural changes within a structure is possible by juxtaposing the projected structures in a single view. We demonstrate this idea in Fig 9 using an isolated healthy olfactory neuron from a dense brain microscopy volume and its corresponding diseased state predicted using [11]. A planar embedding of the neuron's skeleton data (Fig.…”

Section: Planar Visualizationmentioning

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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“…However, VR helped them to identify clusters in the distribution of glycogen, which they later could automatically detect using the DBSCAN-algorithm [EKS * 96]. Based on this observation, they conducted a quantitative analysis of the spatial relationship with the clusters to pre-or postsynaptic elements using the Neuromorph Blender integration [JNC * 15,JBK18].…”

Section: Datamentioning

confidence: 99%

“…While the mentioned modalities have resolutions that are not sufficient to resolve synapses, expansion microscopy (ExM) physically expands brain tissues to overcome the resolution limitation of light microscopy [CTB15, WZB19]. Additionally, imaging Neurons at a micrometer scale facilitates capturing functional networks of Neuronal systems [WTY * 15, RHC20], trace synaptic circuits [LWK * 07], and analyze its geometry and functional connectivity across disease and aging [BMA * 21]. Moreover, light microscopy has also been used recently for the connectivity analysis of defined cell types by using spectral connectomics [SHW * 20].…”

Section: Data Acquisitionmentioning

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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NeuRegenerate: A Framework for Visualizing Neurodegeneration

Cited by 6 publications

References 39 publications

PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies

PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies

Geometry-Aware Planar Embedding of Treelike Structures

A Survey of Visualization and Analysis in High‐Resolution Connectomics

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