EDBT 2026 Demo / reviewers in the wild / expert
Dustin Scheinost
dblp:69/3436
· DBLP profile ↗
14ranked-venue papers
0as first author
6since 2021 · last 2024
0000-0002-6301-1167ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 13 · 6 since 2021Graphics, computer vision, multimedia, augmented reality and games · 11 · 4 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Overcoming Atlas Heterogeneity in Federated Learning for Cross-Site Connectome-Based Predictive Modeling
Qinghao Liang, Brendan Adkinson, Rongtao Jiang, Dustin Scheinost |
MICCAI (10) | 4 |
| 2023 | Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available
Javid Dadashkarimi, Amin Karbasi, Qinghao Liang, Matthew Rosenblatt, Stephanie Noble, Maya Foster, Raimundo X. Rodriguez, Brendan Adkinson, Jean Ye, Huili Sun, Chris Camp, Michael Farruggia, Link Tejavibulya, Rongtao Jiang, Angeliki Pollatou, Dustin Scheinost |
Medical Image Anal. | 17 |
| 2022 | Combining Multiple Atlases to Estimate Data-Driven Mappings Between Functional Connectomes Using Optimal Transport
Javid Dadashkarimi, Amin Karbasi, Dustin Scheinost |
MICCAI (1) | 3 |
| 2022 | The Semi-constrained Network-Based Statistic (scNBS): Integrating Local and Global Information for Brain Network Inference
Stephanie Noble, Dustin Scheinost |
MICCAI (1) | 3 |
| 2021 | Data-Driven Mapping Between Functional Connectomes Using Optimal Transport
Javid Dadashkarimi, Amin Karbasi, Dustin Scheinost |
MICCAI (3) | 3 |
| 2021 | BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis
Xiaoxiao Li 0001, Yuan Zhou 0004, Nicha C. Dvornek, Muhan Zhang, Juntang Zhuang, Dustin Scheinost, Lawrence H. Staib, Pamela Ventola, James S. Duncan |
Medical Image Anal. | 7 |
| 2020 | Poincaré Embedding Reveals Edge-Based Functional Networks of the Brain
Gal Mishne, Dustin Scheinost |
MICCAI (7) | 3 |
| 2020 | The Constrained Network-Based Statistic: A New Level of Inference for Neuroimaging
Stephanie Noble, Dustin Scheinost |
MICCAI (7) | 2 |
| 2019 | Combining Multiple Behavioral Measures and Multiple Connectomes via Multipath Canonical Correlation Analysis
Xilin Shen, R. Todd Constable, Dustin Scheinost |
MICCAI (3) | 4 |
| 2018 | Combining Multiple Connectomes via Canonical Correlation Analysis Improves Predictive Models
Abigail S. Greene, R. Todd Constable, Dustin Scheinost |
MICCAI (3) | 4 |
| 2017 | A Submodular Approach to Create Individualized Parcellations of the Human Brain
Mehraveh Salehi, Amin Karbasi, Dustin Scheinost, R. Todd Constable |
MICCAI (1) | 3 |
| 2011 | A non-rigid registration method for serial lower extremity hybrid SPECT/CT imaging
Jung W. Suh, Dustin Scheinost, Donald P. Dione, Lawrence W. Dobrucki, Albert J. Sinusas, Xenophon Papademetris |
Medical Image Anal. | 2 |
| 2009 | A Non-rigid Registration Method for Serial microCT Mouse Hindlimb Images
Jung W. Suh, Dustin Scheinost, Donald P. Dione, Lawrence W. Dobrucki, Albert J. Sinusas, Xenophon Papademetris |
MICCAI (1) | 2 |
| 2008 | Novel interaction techniques forneurosurgical planning and stereotactic navigationabstractNeurosurgical planning and image guided neurosurgery require the visualization of multimodal data obtained from various functional and structural image modalities, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), functional MRI, Single photon emission computed tomography (SPECT) and so on. In the case of epilepsy neurosurgery for example, these images are used to identify brain regions to guide intracranial electrode implantation and resection. Generally, such data is visualized using 2D slices and in some cases using a 3D volume rendering along with the functional imaging results. Visualizing the activation region effectively by still preserving sufficient surrounding brain regions for context is exceedingly important to neurologists and surgeons. We present novel interaction techniques for visualization of multimodal data to facilitate improved exploration and planning for neurosurgery. We extended the line widget from VTK to allow surgeons to control the shape of the region of the brain that they can visually crop away during exploration and surgery. We allow simple spherical, cubical, ellipsoidal and cylindrical (probe aligned cuts) for exploration purposes. In addition we integrate the cropping tool with the image-guided navigation system used for epilepsy neurosurgery. We are currently investigating the use of these new tools in surgical planning and based on further feedback from our neurosurgeons we will integrate them into the setup used for image-guided neurosurgery. Alark Joshi, Dustin Scheinost, Kenneth P. Vives, Dennis D. Spencer, Lawrence H. Staib, Xenophon Papademetris |
IEEE Trans. Vis. Comput. Graph. | 2 |