Tipix rapid visualization of large datasets
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tipiX Rapid Visualization of Large Datasets. Adrian V. Dalca, Ramesh Sridharan, Natalia Rost, Polina Golland. Alzheimer’s Disease, Brain MRI, PET 2000+. Stroke, Brain MRI 4000+. Autism, Brain MRI , fMRI 1000+. Heart, Brain MRI 2500+, 6000. Brain Mapping

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tipiX Rapid Visualization of Large Datasets

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Tipix rapid visualization of large datasets

tipiXRapid Visualization of Large Datasets

Adrian V. Dalca, Ramesh Sridharan, Natalia Rost, Polina Golland


Tipix rapid visualization of large datasets

Alzheimer’s Disease, Brain

MRI, PET2000+

Stroke, Brain

MRI

4000+

Autism, Brain

MRI, fMRI

1000+

Heart, Brain

MRI

2500+, 6000

Brain Mapping

MRI, fMRI, MRA, DTI, PET~800

COPD Lung

CT

10,000+


Outline

Outline

Current methods for exploring datasets

Our method: tipiX

User study

Conclusion


Outline1

Outline

Current methods for exploring datasets

Our method: tipiX

User study

Conclusion


Current methods visualize subjects

Current Methods: Visualize Subjects


Current methods visualize subjects1

Current Methods: Visualize Subjects


Current methods grids

Current Methods: Grids


Statistics

Statistics

Summarize data and results

Appropriate statistics are difficult to determine

Provide concise, superficial overview of data

Usually won’t detect patterns, imaging details


Outline2

Outline

Current methods for exploring datasets

Our method: tipiX

User study

Conclusion


Tipix demonstration

tipiX Demonstration

tipiX demonstration Dataset


Exploring dimensions

Exploring Dimensions

Dataset Dimensions

Spatial dimensions (2D slice)

Volume Depth

Subject ID

Longitudinal data

Temporal data (fMRI)

Different scan modality

Clinical Factors (e.g. Age)

Parameter sweeps

Trying different algorithms

Dorsal-ventral

Subject ID


Tipix demo

tipiX Demo

Slice vs subject number

  • Lesion

  • Registration

  • Difficult registration

    Groups vs age

  • Time evolution of vascular disease

    Slice vs parameter grid

  • Parameter sweep

    Slice vs subject number Grid

  • Original dataset exploration


White matter hyperintensity

White Matter Hyperintensity


Features

Features

Open source, works directly on modern browser

  • Processing is client-side

    Streamlined collaboration: specific tipiX link encodes dataset

  • Assumes collaborators have data access

    Many features present and in development

  • Display volume info, volume control slice:drop binding, website embedding…

  • Come to the demo!


Outline3

Outline

Current methods for exploring datasets

Our method: tipiX

User study

Conclusion


User study

User Study

Threshold registration quality500 subjects

1

500

ordered subjects


Outline4

Outline

Current methods for exploring datasets

Our method: tipiX

User study

Conclusion


Conclusions

Conclusions

Visualization of large datasets across many dimensions is important

tipiX presents an intuitive visualization platform

  • Enables pattern detection, outlier detection, visualization of trends

  • Web implementation enables easy collaboration, open-source development

    User study demonstrates promising ability to explore datasets quickly


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