TOPOLOGY-PRESERVING DEEP SUPERVISION FOR 3D AXON CENTERLINE SEGMENTATION USING PARTIALLY ANNOTATED DATA

Dense axon centerline detection and tracing is an essential task for understanding brain connectivity and functionality. Collecting large amounts of annotated 3D brain imagery to automate this process is time-consuming and costly. To expedite annotation tool use, development of accurate centerline detection techniques using limited annotated data is needed, especially in the case when incomplete annotations are provided. In this work, we explore creating a new topology preserving loss function in conjunction with a deep supervision paradigm to overcome this challenge. Using annotated volumes with varied levels of expert annotations, we show that our training paradigm outperforms existing methods, achieving comparable performance with only 50% of the annotations, whereas the baseline requires 75% for similar results.