51 citations · 51 across the 9 of their papers we have counts for
9 papers
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…
LEDA: Log-Euclidean Diffeomorphism Autoencoder for Efficient Statistical Analysis of Diffeomorphisms
Krithika Iyer, Shireen Elhabian, Sarang Joshi
Image registration is a core task in computational anatomy that establishes correspondences between images. Invertible deformable registration, which computes a deformation field a…
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…
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…
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…
Mesh2SSM: From Surface Meshes to Statistical Shape Models of Anatomy
Krithika Iyer, Shireen Elhabian
Statistical shape modeling is the computational process of discovering significant shape parameters from segmented anatomies captured by medical images (such as MRI and CT scans),…
Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries
Krithika Iyer, Alan Morris, Brian Zenger +4
Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis and the comparison of s…
Benchmarking off-the-shelf statistical shape modeling tools in clinical applications
Anupama Goparaju, Alexandre Bone, Nan Hu +8
Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technolog…