@conference {70247, title = {LDDMEm: Large Deformation Diffeomorphic Metric Embedding}, booktitle = {Medical Image Computing and Computer Assisted Intervention {\textendash} MICCAI 2025}, year = {2026}, month = {2026 JAN 01}, abstract = {

We present a method, open-source software, and experiments which embed arbitrary deformation vector fields produced by any method (e.g., ANTs or VoxelMorph) in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. This decouples formal diffeomorphic shape analysis from image registration, which has many practical benefits. Shape analysis can be added to study designs without modification to already chosen image registration methods and existing databases of deformation fields can be reanalyzed within the LDDMM framework without repeating image registrations. Pairwise time series studies can be extended to full time series regression with minimal added computing. The diffeomorphic rigor of image registration methods can be compared by embedding deformation fields and comparing projection distances. Finally, the added value of formal diffeomorphic shape analysis can be more fairly evaluated when it is derived from and compared to a baseline set of deformation fields. In brief, the method is a straightforward use of geodesic shooting in diffeomorphisms with a deformation field as the target, rather than an image. This is simpler than the image registration case which leads to a faster implementation that requires fewer user derived parameters.

}, doi = {10.1007/978-3-032-04947-6_31}, url = {https://www.doi.org/10.1007/978-3-032-04947-6_31}, author = {Fleishman, Greg M. and Fletcher, P. Thomas} } @conference {68367, title = {Tracking by Weakly-Supervised Learning and Graph Optimization for Whole-Embryo C. elegans lineages}, booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention}, year = {2022}, month = {2022 Sep 16}, publisher = {Springer Nature Switzerland}, organization = {Springer Nature Switzerland}, address = {Cham}, abstract = {

Tracking all nuclei of an embryo in noisy and dense fluorescence microscopy data is a challenging task. We build upon a recent method for nuclei tracking that combines weakly-supervised learning from a small set of nuclei center point annotations with an integer linear program (ILP) for optimal cell lineage extraction. Our work specifically addresses the following challenging properties of\ C. elegans\ embryo recordings: (1) Many cell divisions as compared to benchmark recordings of other organisms, and (2) the presence of polar bodies that are easily mistaken as cell nuclei. To cope with (1), we devise and incorporate a learnt cell division detector. To cope with (2), we employ a learnt polar body detector. We further propose automated ILP weights tuning via a structured SVM, alleviating the need for tedious manual set-up of a respective grid search.

}, isbn = {978-3-031-16439-2}, doi = {10.1007/978-3-031-16440-8}, url = {https://link.springer.com/10.1007/978-3-031-16440-8}, editor = {Wang, Linwei and Dou, Qi and Fletcher, P. Thomas and Speidel, Stefanie and Li, Shuo} }