MitoVis: A Unified Visual Analytics System for End-to-End Neuronal Mitochondria Analysis

Choi, Junyoung; Oh, Hyun-Jic; Lee, Hakjun; Kim, Su Yeon; Kwon, Seok-Kyu; Jeong, Won-Ki

doi:10.1109/tvcg.2022.3233548

Cited by 3 publications

(4 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ReLeSR was then aspirated, and cells were incubated in a 37°C incubator for 5 minutes before being collected and split at a 1:10 dilution in a new plate. Upon reaching 80% confluency, iPSCs were differentiated into motor neurons as per the protocol outlined by Du et al 11 . In brief, the natural developmental signaling factors were recapitulated with small molecules including Ascorbic acid (72132, StemCell technologies), CHIR99021 (72054, StemCell Technologies), DMH-1 (73634, StemCell Technologies), SB-431542 (72234, StemCell Technologies), Retinoic acid (R2625, Sigma, St. Louis, USA), Purmorphamine (72204, StemCell Technologies), ROCK inhibitor (Y-27632, 72304, StemCell technologies), and Compound E (73952, StemCell Technologies) using the protocol 26 .…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Classification of iPSC-Derived Cultures Using Convolutional Neural Networks to Identify Single Differentiated Neurons for Isolation or Measurement

Patel,

Mohammed Ali,

Kaushik

et al. 2023

Preprint

View full text Add to dashboard Cite

Understanding neurodegenerative disease pathology depends on a close examination of neurons and their processes. However, image-based single-cell analyses of neurons often require laborious and time-consuming manual classification tasks. Here, we present a machine learning approach leveraging convolutional neural network (CNN) classifiers that have the capability to accurately identify various classes of neuronal images, including single neurons. We developed the Single Neuron Identification Model 20-Class (SNIM20) which was trained on a dataset of induced pluripotent stem cell (iPSC)-derived motor neurons, containing over 12,000 images from 20 distinct classes. SNIM20 is built in TensorFlow and trained on images of differentiated iPSC cultures stained for nuclei and microtubules. This classifier demonstrated high predictive accuracy (AUC = 0.99) for distinguishing single neurons. Additionally, the 2-stage training framework can be used more broadly for cellular classification tasks. A variation was successfully trained on images of a human osteosarcoma cell line (U2OS) for single-cell classification (AUC = 0.99). While this framework was primarily designed for single-cell microraft-based identification and capture, it also works with cells in standard plate formats. We additionally explore the impact of specific fluorescent channels and brightfield images, class groupings, and transfer learning on the quality of the classification. This framework can both assist in high throughput neuronal or cellular identification and be used to train a custom classifier for the user’s needs.

show abstract

Section: Methodsmentioning

confidence: 99%

“…While there are image-based algorithms and ML tools that enable neuronal segmentation, cell tracing, and identification of neurons, single neuron analyses remain difficult [4][5][6][7][8][9] . aNNs make an ideal choice to overcome limitations in the existing tools and have been implemented in similar contexts [10][11][12][13][14][15] .…”

Section: Introductionmentioning

confidence: 99%

Classification of iPSC-Derived Cultures Using Convolutional Neural Networks to Identify Single Differentiated Neurons for Isolation or Measurement

Patel,

Mohammed Ali,

Kaushik

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Imaging data, in particular, is well-suited for visualizations due to the inherent visual nature of the data [72]. Visualizations of imaging data have been applied to subcellular structures [14,69]. However, for our use case, we are interested in the analysis of whole cells, not subcellular structures.…”

Section: Cell Biology Visualizationsmentioning

confidence: 99%

“…When interacting with data to either correct errors or modify designs, snippets can serve as a preview for possible user actions. Choi et al show snippets of classification recommendations for microscopy images [14]. Similarly, Coffey et al use preview snippets to show potential design alternatives for medical devices [16].…”

Section: Image Snippetsmentioning

confidence: 99%

Aardvark: Composite Visualizations of Trees, Time-Series, and Images

Lange,

Judson-Torres,

Zangle

et al. 2024

Preprint

View full text Add to dashboard Cite

How do cancer cells grow, divide, proliferate and die? How do drugs influence these processes? These are difficult questions that we can attempt to answer with a combination of time-series microscopy experiments, classification algorithms, and data visualization. However, collecting this type of data and applying algorithms to segment and track cells and construct lineages of proliferation is error-prone; and identifying the errors can be challenging since it often requires cross-checking multiple data types. Similarly, analyzing and communicating the results necessitates synthesizing different data types into a single narrative.State-of-the-art visualization methods for such data use independent line charts, tree diagrams, and images in separate views. However, this spatial separation requires the viewer of these charts to combine the relevant pieces of data in memory.To simplify this challenging task, we describe design principles for weaving cell images, time-series data, and tree data into a cohesive visualization. Our design principles are based on choosing a primary data type that drives the layout and integrates the other data types into that layout. We then introduce Aardvark, a system that uses these principles to implement novel visualization techniques. Based on Aardvark, we demonstrate the utility of each of these approaches for discovery, communication, and data debugging in a series of case studies.

show abstract

Projection Ensemble: Visualizing the Robust Structures of Multidimensional Projections

Jung,

Choi,

2023

2023 IEEE Visualization and Visual Analytics (VIS)

View full text Add to dashboard Cite

MitoVis: A Unified Visual Analytics System for End-to-End Neuronal Mitochondria Analysis

Cited by 3 publications

References 62 publications

Classification of iPSC-Derived Cultures Using Convolutional Neural Networks to Identify Single Differentiated Neurons for Isolation or Measurement

Classification of iPSC-Derived Cultures Using Convolutional Neural Networks to Identify Single Differentiated Neurons for Isolation or Measurement

Aardvark: Composite Visualizations of Trees, Time-Series, and Images

Projection Ensemble: Visualizing the Robust Structures of Multidimensional Projections

Contact Info

Product

Resources

About