35 citations · 51 across the 42 of their papers we have counts for
49 papers
Anchored Slice Propagation with Boundary Transport for 3D CT Segmentation from Three Orthogonal Annotated Slices
Tushar Kataria, Md Rakibul Haque, Shireen Y. Elhabian
Sli2Vol propagates a binary mask through affinities between adjacent slices, but an error can persist because each prediction becomes the input to the next step. Three orthogonal a…
Lesion Overlap Ranks PET Segmentation Models but Does Not Certify Patient-Level Tumor Burden
Tushar Kataria, Gerald Sabin, Ponnuswamy Sadayappan +1
Metabolic tumor volume (MTV) summarizes whole-body PET disease burden for prognostic stratification and treatment assessment. Automated MTV is useful only if its estimate is reliab…
FLASHSWIN: Unlocking Large Windows and Dense Tokens in Swin Vision Transformers with Memory Efficient Attention
Tushar Kataria, Gerald Sabin, Ponnuswamy Sadayappan +1
High-resolution vision backbones have long been forced to trade away local token density to afford larger receptive fields. Hierarchical Swin transformers impose this compromise be…
TRACE: Artifact-Robust Statistical Shape Modeling from Imperfect Surface Scans - A Case Study in Craniosynostosis 3D Photography
Sanjay Bhandari, Nawazish Khan, Alzbeta Novotna +7
Craniosynostosis severity analysis increasingly relies on statistical shape models (SSMs) to quantify cranial morphology, but most existing workflows depend on computed tomography…
Inference-Time Orthogonal Seeding Enables Geometry-Aligned 3D Organ Segmentation for Slice-Propagation Methods
Md Rakibul Haque, Tushar Kataria, Shireen Y. Elhabian
Dense voxel-level annotation remains a major bottleneck in 3D medical image segmentation. Single-slice propagation methods such as Sli2Vol reduce this burden by propagating one ann…
SCALP: Semi-Supervised Statistical Shape Modeling from Imperfect 3D Photogrammetry via Landmark-Anchored Spectral Warp
Nawazish Khan, Sanjay Bhandari, Sarang Joshi +8
Correspondence-based statistical shape modeling (SSM) is vital for population-level morphometric analysis, but conventional pipelines assume clean, fully registered surfaces. Real-…
Timesynth: A Temporal Fidelity Framework for Health Signal Digital Twins
Md Rakibul Haque, Shireen Elhabian, Warren Woodrich Pettine
Forecasting models for health-signal digital twins must preserve the oscillatory, frequency, phase, and state-transition dynamics of physiological signals, yet the pointwise metric…
TimeSynth: A Framework for Uncovering Systematic Biases in Time Series Forecasting
Md Rakibul Haque, Vishwa Goudar, Shireen Elhabian +1
Time series forecasting is a fundamental tool with wide ranging applications, yet recent debates question whether complex nonlinear architectures truly outperform simple linear mod…
Building Trust in Virtual Immunohistochemistry: Automated Assessment of Image Quality
Tushar Kataria, Shikha Dubey, Mary Bronner +4
Deep learning models can generate virtual immunohistochemistry (IHC) stains from hematoxylin and eosin (H&E) images, offering a scalable and low-cost alternative to laboratory IHC.…
BoundarySeg:Boundary-Aware Multi-Task Learning for Semi-Supervised 3D Medical Image Segmentation with Limited Unlabeled Data
Tushar Kataria, Shireen Y. Elhabian
Data scarcity is a primary bottleneck in 3D medical image segmentation: expert voxel-level annotations are expensive, and privacy regulations often severely restrict access to unan…
Mesh2SSM++: A Probabilistic Framework for Unsupervised Learning of Statistical Shape Model of Anatomies from Surface Meshes
Krithika Iyer, Mokshagna Sai Teja Karanam, Shireen Elhabian
Anatomy evaluation is crucial for understanding the physiological state, diagnosing abnormalities, and guiding medical interventions. Statistical shape modeling (SSM) is vital in t…
Optimization-Driven Statistical Models of Anatomies using Radial Basis Function Shape Representation
Hong Xu, Shireen Y. Elhabian
Particle-based shape modeling (PSM) is a popular approach to automatically quantify shape variability in populations of anatomies. The PSM family of methods employs optimization to…
VIMs: Virtual Immunohistochemistry Multiplex staining via Text-to-Stain Diffusion Trained on Uniplex Stains
Shikha Dubey, Yosep Chong, Beatrice Knudsen +1
This paper introduces a Virtual Immunohistochemistry Multiplex staining (VIMs) model designed to generate multiple immunohistochemistry (IHC) stains from a single hematoxylin and e…
Probabilistic 3D Correspondence Prediction from Sparse Unsegmented Images
Krithika Iyer, Shireen Y. Elhabian
The study of physiology demonstrates that the form (shape)of anatomical structures dictates their functions, and analyzing the form of anatomies plays a crucial role in clinical re…
On the Viability of Semi-Supervised Segmentation Methods for Statistical Shape Modeling
Asma Khan, Tushar Kataria, Janmesh Ukey +1
Statistical Shape Models (SSMs) excel at identifying population level anatomical variations, which is at the core of various clinical and biomedical applications, including morphol…
SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images
Krithika Iyer, Jadie Adams, Shireen Y. Elhabian
Statistical shape modeling (SSM) is a powerful computational framework for quantifying and analyzing the geometric variability of anatomical structures, facilitating advancements i…
Point2SSM++: Self-Supervised Learning of Anatomical Shape Models from Point Clouds
Jadie Adams, Shireen Elhabian
Correspondence-based statistical shape modeling (SSM) stands as a powerful technology for morphometric analysis in clinical research. SSM facilitates population-level characterizat…
Weakly Supervised Bayesian Shape Modeling from Unsegmented Medical Images
Jadie Adams, Krithika Iyer, Shireen Elhabian
Anatomical shape analysis plays a pivotal role in clinical research and hypothesis testing, where the relationship between form and function is paramount. Correspondence-based stat…
Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation
Rachaell Nihalaani, Tushar Kataria, Jadie Adams +1
Supervised methods for 3D anatomy segmentation demonstrate superior performance but are often limited by the availability of annotated data. This limitation has led to a growing in…
StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining
Tushar Kataria, Beatrice Knudsen, Shireen Y. Elhabian
Hematoxylin and Eosin (H&E) staining is widely regarded as the standard in pathology for diagnosing diseases and tracking tumor recurrence. While H&E staining shows tissue structur…
EfficientMorph: Parameter-Efficient Transformer-Based Architecture for 3D Image Registration
Abu Zahid Bin Aziz, Mokshagna Sai Teja Karanam, Tushar Kataria +1
Transformers have emerged as the state-of-the-art architecture in medical image registration, outperforming convolutional neural networks (CNNs) by addressing their limited recepti…
MASSM: An End-to-End Deep Learning Framework for Multi-Anatomy Statistical Shape Modeling Directly From Images
Janmesh Ukey, Tushar Kataria, Shireen Y. Elhabian
Statistical Shape Modeling (SSM) effectively analyzes anatomical variations within populations but is limited by the need for manual localization and segmentation, which relies on…
Particle-Based Shape Modeling for Arbitrary Regions-of-Interest
Hong Xu, Alan Morris, Shireen Y. Elhabian
Statistical Shape Modeling (SSM) is a quantitative method for analyzing morphological variations in anatomical structures. These analyses often necessitate building models on targe…
Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images
K M Arefeen Sultan, Benjamin Orkild, Alan Morris +6
Accurate assessment of left atrial fibrosis in patients with atrial fibrillation relies on high-quality 3D late gadolinium enhancement (LGE) MRI images. However, obtaining such ima…