1 citations · 1 across the 5 of their papers we have counts for
5 papers
MorphoFlow: Sparse-Supervised Generative Shape Modeling with Adaptive Latent Relevance
Mokshagna Sai Teja Karanam, Tushar Kataria, Shireen Elhabian
Statistical shape modeling (SSM) is central to population level analysis of anatomical variability, yet most existing approaches rely on densely annotated segmentations and fixed l…
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…
MORPH-LER: Log-Euclidean Regularization for Population-Aware Image Registration
Mokshagna Sai Teja Karanam, Krithika Iyer, Sarang Joshi +1
Spatial transformations that capture population-level morphological statistics are critical for medical image analysis. Commonly used smoothness regularizers for image registration…
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…
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images
Mokshagna Sai Teja Karanam, Tushar Kataria, Krithika Iyer +1
Statistical shape models (SSM) have been well-established as an excellent tool for identifying variations in the morphology of anatomy across the underlying population. Shape model…