Prior-based Coregistration and Cosegmentation
This work addresses coregistration and segmentation for brain imaging, but it appears incremental as it builds on existing methods with modular enhancements.
The authors tackled the problem of dense coregistration and cosegmentation by proposing a modular framework that uses classifier outputs instead of ground truth and combines them with deformable registration to improve alignment and segmentation, showing potential on two datasets with challenging brain structures.
We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map output of a classifier; second, we combine this output with population deformable registration to improve both alignment and segmentation. Our approach deforms all volumes towards consensus, taking into account image similarities and label consistency. Our pipeline can incorporate any classifier and similarity metric. Results on two datasets, containing annotations of challenging brain structures, demonstrate the potential of our method.