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Accurate topology is key when performing meaningful anatomical segmentations,however, it is often overlooked in traditional deep learning methods. In thiswork we propose TEDS-Net: a novel segmentation method that guarantees accuratetopology. Our method is built upon a continuous diffeomorphic framework, whichenforces topology preservation. However, in practice, diffeomorphic fields arerepresented using a finite number of parameters and sampled using methods suchas linear interpolation, violating the theoretical guarantees. We thereforeintroduce additional modifications to more strictly enforce it. Our networklearns how to warp a binary prior, with the desired topologicalcharacteristics, to complete the segmentation task. We tested our method onmyocardium segmentation from an open-source 2D heart dataset. TEDS-Netpreserved topology in 100% of the cases, compared to 90% from the U-Net,without sacrificing on Hausdorff Distance or Dice performance. Code will bemade available at: www.github.com/mwyburd/TEDS-Net

TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations