Sparse Annotation is Sufficient for Bootstrapping Dense Segmentation.

ABSTRACT: Producing dense 3D reconstructions from biological imaging data is a challenging instance segmentation task that requires significant ground-truth training data for effective and accurate deep learning-based models. Generating training data requires intense human effort to annotate each instance of an object across serial section images. Our focus is on the especially complicated brain neuropil, comprising an extensive interdigitation of dendritic, axonal, and glial processes visualized through serial section electron microscopy. We developed a novel deep learning-based method to generate dense 3D segmentations rapidly from sparse 2D annotations of a few objects on single sections. Models trained on the rapidly generated segmentations achieved similar accuracy as those trained on expert de

SUBMITTER: Thiyagarajan VV 

PROVIDER: S-EPMC11195258 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature