We present SurfDist, a convolutional neural network architecture for three-dimensional volumetric instance segmentation. SurfDist is a modification of the popular model architecture StarDist-3D which enables learning instance boundaries as closed piecewise compositions of smooth parametric surfaces. This parameterization breaks StarDist-3D’s coupling of instance dimension and instance voxel resolution, and it produces predictions which may be upsampled to arbitrarily high resolutions without introduction of voxelization artifacts. For datasets with blobshaped instances, common in biomedical imaging, SurfDist can achieve higher segmentation accuracy than StarDist3D with more compact instance parameterizations.

We describe our proposed demonstration of GeoDeepDive, a system that helps geoscientists discover information and knowledge buried in the text, tables, and figures of geology journal articles. This requires solving a host of classical data management challenges including data acquisition (e.g., from scanned documents), data extraction, and data integration. SIGMOD attendees will see demonstrations of three aspects of our system: (1) an end-to-end system that is of a high enough quality to perform novel geological science, but is written by a small enough team so that each aspect can be manageably explained; (2) a simple feature engineering system that allows a user to write in familiar SQL or Python; and (3) the effect of different sources of feedback on result quality including expert labeling, distant supervision, traditional rules, and crowd-sourced data.