Manasi Datar

Towards Integrating Epistemic Uncertainty Estimation into the Radiotherapy Workflow

The precision of contouring target structures and organs-at-risk (OAR) in radiotherapy planning is crucial for ensuring treatment efficacy and patient safety. Recent advancements in deep learning...

Improved Pectoral Muscle Segmentation in Mammograms through Regression-Based Deep Learning and Knowledge Distillation

Color and texture based segmentation of molecular pathology images usING HSOMS

Deformable registration with spatially varying degrees of freedom constraints

GPU accelerated, robust method for voxelization of solid objects

Propagation of Myocardial Fibre Architecture Uncertainty on Electromechanical Model Parameter Estimation: A Case Study

Longitudinal Parameter Estimation in 3D Electromechanical Models: Application to Cardiovascular Changes in Digestion

Anatomically Guided Registration for Multimodal Images

Longitudinal Analysis Using Personalised 3D Cardiac Models with Population-Based Priors: Application to Paediatric Cardiomyopathies

SVF-Net: Learning Deformable Image Registration Using Shape Matching

Towards Integrating Epistemic Uncertainty Estimation into the Radiotherapy Workflow

Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy

Geometric Correspondence for Ensembles of Nonregular Shapes

Geodesic Distances to Landmarks for Dense Correspondence on Ensembles of Complex Shapes

Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging

Natural Scene Segmentation Based on Information Fusion and Homogeneity Property