Timezone: »

Learning Probabilistic Topological Representations Using Discrete Morse Theory
Xiaoling Hu · Dimitris Samaras · Chao Chen

Accurate delineation of fine-scale structures is a very important yet challenging problem. Existing methods use topological information as an additional training loss, but are ultimately making pixel-wise predictions. In this abstract, we propose the first deep learning based method to learn topological/structural representations. We use discrete Morse theory and persistent homology to construct an one-parameter family of structures as the topological/structural representation space. Furthermore, we learn a probabilistic model that can perform inference tasks in such a topological/structural representation space. Our method generates true structures rather than pixel-maps, leading to better topological integrity in automatic segmentation tasks. It also facilitates semi automatic interactive annotation/proofreading via the sampling of structures and structure-aware uncertainty.

Author Information

Xiaoling Hu (Stony Brook University)
Dimitris Samaras (Stony Brook University)
Chao Chen (Stony Brook University)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors