Image Segmentation

Non-deep Learning based Segmentation Approaches

We have worked on a number of different general methods for image segmentation based on convex relaxations ( [zach09a] [zach09b] ). We have also devised segmentation methods for the segmentations of fiber bundles from diffusion weighted images ( [niethammer09] ) and to extract connectivity information from diffusion tensor images ( [niethammer10] ). For the segmentation of biological structures it is often know a-priori how large a particular object should be. We have therefore explored a segmentation method which can impose constraints on the segmentation area ( [niethammer13] ).

Multi-atlas Segmentation

We developed multi-atlas segmentation approaches for knee cartilage ( [shan14] ) and have most recently explored deep-learning approaches to predict the trustworthiness of individual atlases for multi-atlas segmentation approaches ( [ding19] ).

Image Segmentation via Deep Learning

As one of our main research directions is image registration, we are exploring image segmentation approaches based on deep-learning which can benefit from our fast deep-learning registration approaches. This has led us so far, for example, to an approach for joint segmentation and registration ( [xu19] ) as well as to a segmentation approach based on data augmentation at test time ( [shen20] ).



Marc Niethammer
Marc Niethammer
Professor of Computer Science

My research interests include image registration, image segmentation, shape analysis, machine learning, and biomedical applications.