Oshane O. Thomas

The paper introduces an open-source photogrammetry workflow using 3D Slicer and SlicerMorph to generate high-quality 3D models of biological specimens, showing improved accuracy over existing methods.

The paper develops a focal-loss transformer model to detect exencephaly in imbalanced micro-CT embryo data, achieving high accuracy and interpretable saliency maps.

This paper develops a deep learning method for automatic landmark acquisition in geometric morphometrics, showing it is faster and comparable in accuracy to existing methods on rodent mandible datasets.

This paper introduces morphVQ, an automated shape analysis method that captures morphological variation using functional maps and descriptor learning, achieving comparable accuracy to manual methods and auto3DGM while being more computationally efficient.