Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
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portfolio
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publications
Benchmarking off-the-shelf statistical shape modeling tools in clinical applications
Published in Medical Image Analysis, 2022
Systematic benchmarking of statistical shape modeling tools for clinical workflows on real-world anatomical datasets.
Recommended citation: A. Goparaju, K. Iyer, A. Bône, N. Hu, H.B. Henninger, A.E. Anderson, S. Elhabian (2022). "Benchmarking off-the-shelf statistical shape modeling tools in clinical applications." Medical Image Analysis 76: 102271. https://doi.org/10.1016/j.media.2021.102271
RENs: Relevance Encoding Networks
Published in arXiv, 2022
Network model for encoding relevance in unsupervised learning.
Recommended citation: K. Iyer, R. Bhalodia, S. Elhabian (2022). "RENs: Relevance Encoding Networks." arXiv:2205.13061. https://arxiv.org/abs/2205.13061
Integrating atomistic simulations and machine learning to design multi-principal element alloys with superior elastic modulus
Published in Journal of Materials Research, 2022
Combining molecular simulations and ML for accelerated discovery of advanced alloys.
Recommended citation: M. Grant, M.R. Kunz, K. Iyer, L.I. Held, T. Tasdizen, J.A. Aguiar, P.P. Dholabhai (2022). "Integrating atomistic simulations and machine learning to design multi-principal element alloys with superior elastic modulus." Journal of Materials Research 37(8): 1497-1512. https://link.springer.com/article/10.1557/s43578-022-00557-7
Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries
Published in STACOM MICCAI, 2022
Statistical modeling of biventricular heart anatomy with anatomical boundary sharing.
Recommended citation: K. Iyer, A. Morris, B. Zenger, K. Karanth, B.A. Orkild, O. Korshak, S. Elhabian (2022). "Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries." arXiv:2209.02706. https://arxiv.org/abs/2209.02706
All roads lead to rome: Diverse etiologies of tricuspid regurgitation create a predictable constellation of right ventricular shape changes
Published in Frontiers in Physiology, 2022
Shape changes in right ventricle due to diverse etiologies of tricuspid regurgitation.
Recommended citation: B.A. Orkild, B. Zenger, K. Iyer, L.C. Rupp, M.M. Ibrahim, A.G. Khashani, S.Y. Elhabian (2022). "All roads lead to rome: Diverse etiologies of tricuspid regurgitation create a predictable constellation of right ventricular shape changes." Frontiers in Physiology 13: 908552. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2022.908552/full
Statistical shape modeling of multi-organ anatomies with shared boundaries
Published in Frontiers in Bioengineering and Biotechnology, 2023
Building statistical shape models for multi-organ systems sharing anatomical boundaries.
Recommended citation: K. Iyer, A. Morris, B. Zenger, K. Karanth, N. Khan, B.A. Orkild, O. Korshak, S. Elhabian (2023). "Statistical shape modeling of multi-organ anatomies with shared boundaries." Frontiers in Bioengineering and Biotechnology 10: 1078800. https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2022.1078800/full
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images
Published in SHAPE-MI MICCAI 2023, 2023
Adversarial data augmentation approach for robust statistical shape modeling.
Recommended citation: M.S.T. Karanam, T. Kataria, K. Iyer, S. Elhabian (2023). "ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images." arXiv:2307.03273. https://arxiv.org/abs/2307.03273
Mesh2ssm: From surface meshes to statistical shape models of anatomy
Published in MICCAI, 2023
A novel pipeline enabling direct construction of statistical shape models from mesh data.
Recommended citation: K. Iyer, S.Y. Elhabian (2023). "Mesh2ssm: From surface meshes to statistical shape models of anatomy." MICCAI. https://arxiv.org/abs/2305.07805
SCorP: Statistics-informed dense correspondence prediction directly from unsegmented medical images
Published in MIUA, 2024
Dense correspondence prediction directly from volumetric images using statistics-informed anatomy prior.
Recommended citation: K. Iyer, J. Adams, S.Y. Elhabian (2024). "SCorP: Statistics-informed dense correspondence prediction directly from unsegmented medical images." MIUA 142-157. https://arxiv.org/abs/2404.17967
Weakly supervised Bayesian shape modeling from unsegmented medical images
Published in ShapeMI MICCAI, 2024
Bayesian shape modeling from unsegmented images with weak supervision.
Recommended citation: J. Adams, K. Iyer, S. Y. Elhabian (2024). "Weakly supervised Bayesian shape modeling from unsegmented medical images." ShapeMI 1-17. https://arxiv.org/abs/2405.09697
Mesh2SSM++: A Probabilistic Framework for Unsupervised Learning of Statistical Shape Model of Anatomies from Surface Meshes
Published in arXiv, 2025
Probabilistic unsupervised shape modeling directly from meshes of anatomy.
Recommended citation: K. Iyer, M.S.T. Karanam, S. Elhabian (2025). "Mesh2SSM++: A Probabilistic Framework for Unsupervised Learning of Statistical Shape Model of Anatomies from Surface Meshes." arXiv:2502.07145. https://arxiv.org/abs/2502.07145
MORPH-LER: Log-Euclidean Regularization for Population-Aware Image Registration
Published in MIDL, 2025
Population-aware regularization in image registration with Log-Euclidean approach.
Recommended citation: M.S.T. Karanam, K. Iyer, S. Joshi, S. Elhabian (2025). "MORPH-LER: Log-Euclidean Regularization for Population-Aware Image Registration." arXiv:2502.02029. https://arxiv.org/abs/2502.02029
LEDA: Log-Euclidean Diffeomorphic Autoencoder for Efficient Statistical Analysis of Diffeomorphism
Published in IPMI, 2025
Statistical analysis of diffeomorphic deformations.
Recommended citation: K. Iyer, S. Elhabian, S. Joshi (2024). "LEDA: Log-Euclidean Diffeomorphic Autoencoder for Efficient Statistical Analysis of Diffeomorphism." arXiv:2412.16129. https://arxiv.org/abs/2412.16129
Post-Processing Methods for Improving Accuracy in MRI Inpainting
Published in arXiv (preprint; revised April 2026), 2025
Model ensembling and targeted post-processing to improve reconstruction of brain tissue in tumor regions.
Recommended citation: N. Kulkarni, K. Iyer, A. Tapp, A. Parida, D. Capellán-Martín, Z. Jiang, M. J. Ledesma-Carbayo, S. M. Anwar, M. G. Linguraru (2025). "Post-Processing Methods for Improving Accuracy in MRI Inpainting." arXiv:2510.15282. Revised April 2026. https://arxiv.org/abs/2510.15282
Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemble
Published in arXiv (preprint; revised June 2026), 2025
A modular brain tumor segmentation pipeline combining radiomic subtyping, model ensembles, and lesion-specific processing.
Recommended citation: D. Capellán-Martín, A. Parida, Z. Jiang, N. Kulkarni, K. Iyer, A. Tapp, S. M. Anwar, M. J. Ledesma-Carbayo, M. G. Linguraru (2025). "Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemble." arXiv:2512.14648. Revised June 2026. https://arxiv.org/abs/2512.14648
Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques
Published in arXiv (preprint; revised June 2026), 2025
Adaptive post-processing to refine glioma segmentations from pretrained models, evaluated in BraTS 2025 tasks.
Recommended citation: A. Parida, D. Capellán-Martín, Z. Jiang, N. Kulkarni, K. Iyer, A. Tapp, S. M. Anwar, M. J. Ledesma-Carbayo, M. G. Linguraru (2025). "Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques." arXiv:2512.14937. Revised June 2026. https://arxiv.org/abs/2512.14937
MRI-to-CT Synthesis With Cranial Suture Segmentations Using A Variational Autoencoder Framework
Published in arXiv (preprint), 2025
Pediatric MRI-to-CT synthesis with cranial bone and suture segmentation using variational autoencoders.
Recommended citation: K. Iyer, A. Tapp, A. Paulli, G. Dickerson, S. M. Anwar, N. Lepore, M. G. Linguraru (2025). "MRI-to-CT Synthesis With Cranial Suture Segmentations Using A Variational Autoencoder Framework." arXiv:2512.23894. https://arxiv.org/abs/2512.23894
Quantifying Sagittal Craniosynostosis Severity: A Machine Learning Approach With CranioRate
Published in The Cleft Palate-Craniofacial Journal, 2026
Machine learning for quantifying the severity of sagittal craniosynostosis.
Recommended citation: W. Tao, T. J. Somorin, J. Kueper, A. Dixon, N. Kass, N. Khan, K. Iyer, J. Wagoner, A. Rogers, R. Whitaker, S. Elhabian, J. A. Goldstein (2026). "Quantifying Sagittal Craniosynostosis Severity: A Machine Learning Approach With CranioRate." The Cleft Palate-Craniofacial Journal 63(6): 1708–1717. https://doi.org/10.1177/10556656251347366
talks
SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images
Published:
Oral presentation of SCorP in the Medical Images and Computational Models session (3:30–5:00 p.m., Room G.27), paper 35. The paper received the Best Paper Award in the Medical Images and Computational Models category.
Post-Processing Methods for Improving Accuracy in MRI Inpainting
Published:
Talk on our BraTS 2025 inpainting work, combining model ensembling and targeted post-processing to improve MRI inpainting accuracy.
MRI-to-CT Synthesis with Cranial Suture Segmentation Using a Variational Autoencoder Framework
Published:
Oral presentation in the “From MRI or CBCT to a Usable CT” session, April 10, 2026, at 17:10 in Room 16. Paper ID: 1571226375.
Ultra-Low-Field MRI Brain Volumetry to Predict Early Cognitive Risk in Low-Resource Settings
Published:
Spotlight presentation at the Research & Innovation Mixer recognizing REI Week abstract awardees, presenting work on using ultra-low-field MRI brain volumetry to predict early cognitive risk in low-resource settings. The talk highlights scalable, low-cost neuroimaging biomarkers for developmental screening and the potential of AI-enabled portable MRI to expand access to pediatric brain health assessment globally.
AI-Enhanced ULF MRI Achieves High-Field MRI Diagnostic Accuracy in Neonatal Neuroimaging: A Blinded Multi-Reader Study
Published:
Presenting author for the oral presentation in the “AI Methods Beyond Reconstruction” session, May 11, 2026, at 09:04 in Auditorium 1. Abstract: 303-01-005.
AI-Enhanced Portable Pediatric MRI: From Scalable Brain Imaging to Global Developmental Outcomes
Published:
Invited talk jointly presented with Austin Tapp at the third annual AI for Pediatric Health Symposium, hosted by Virginia Tech and Children’s National Hospital. The presentation was scheduled at 4:45 p.m. in the session “Decoding Disease: Using AI to Uncover New Insights into Disease Biology and Risk” at Virginia Tech’s Academic Building One in Alexandria.
Invited Keynote at ShapeMI 2026
Published:
Invited keynote speaker at the Shape in Medical Imaging workshop, held in conjunction with MICCAI 2026. The workshop is scheduled for September 27, 2026.
teaching
Digital Communication Teaching Assistant
Undergraduate course, University of Pune, Electronics/Telecom, 2015
Teaching Assistant for Digital Communication
- Signal processing fundamentals
- Analog-to-digital conversion
- Delivered core lecture about mathematical concepts of digital communication, managed lab sessions, and graded assignments.
- Supported student success in coursework
- Mentored students for their projects and lab sessions
Applied Machine Learning Graduate TA
Graduate course, University of Utah, Dept of Biomedical Informatics, 2018
Graduate Teaching Assistant for Applied Machine Learning
- Supported instruction on core ML models, real-world datasets, and algorithm assessment.
- Delivered lectures on foundational and applied machine learning topics, and assisted students with project design and coding.
Computer Graphics Graduate Mentorship
Graduate mentorship, University of Utah, School of Computing, 2019
Graduate Mentorship in Computer Graphics
- Guided project teams, organized seminars, supported technical presentations, and reviewed student renderings.
Image Processing Graduate Mentorship
Graduate mentorship, University of Utah, Kahlert School of Computing, 2021
Graduate Mentorship in Image Processing
- Supervised and mentored graduate students working on image processing projects, with particular emphasis on mathematical topics, denoising, and segmentation.
- Delivered the full course module on Fourier analysis, providing comprehensive lectures (with video recordings archived for student reference).
- Fostered research independence and technical mastery in advanced imaging topics
- Received positive student feedback on clarity and depth of Fourier analysis instruction
Image Analysis Seminar Graduate TA
Graduate seminar, University of Utah, Kahlert School of Computing, 2022
Image Analysis Seminar – Graduate Teaching Assistant
- Organized journal club discussions, mentored student research presentations, and guided reading assignments in advanced topics.
- Interdisciplinary approaches to image-based research
Advanced Image Processing Graduate TA
Graduate course, University of Utah, Kahlert School of Computing, 2024
Graduate Teaching Assistant for Advanced Image Processing
- Responsibilities included leading review sessions, developing problem sets, facilitating student projects, and assisting with course and project grading.
- Worked closely with faculty to deliver modules on the latest medical image analysis deep learning models.
- Deep learning for image analysis