  In this article, we describe a modified implementation of Mask Region-based
Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a
liquid argon TPC and applied to MicroBooNE neutrino data. Our implementation of
this network, called sMask-RCNN, uses sparse submanifold convolutions to
increase processing speed on sparse datasets, and is compared to the original
dense version in several metrics. The networks are trained to use wire readout
images from the MicroBooNE liquid argon time projection chamber as input and
produce individually labeled particle interactions within the image. These
outputs are identified as either cosmic ray muon or electron neutrino
interactions. We find that sMask-RCNN has an average pixel clustering
efficiency of 85.9% compared to the dense network's average pixel clustering
efficiency of 89.1%. We demonstrate the ability of sMask-RCNN used in
conjunction with MicroBooNE's state-of-the-art Wire-Cell cosmic tagger to veto
events containing only cosmic ray muons. The addition of sMask-RCNN to the
Wire-Cell cosmic tagger removes 70% of the remaining cosmic ray muon background
events at the same electron neutrino event signal efficiency. This event veto
can provide 99.7% rejection of cosmic ray-only background events while
maintaining an electron neutrino event-level signal efficiency of 80.1%. In
addition to cosmic ray muon identification, sMask-RCNN could be used to extract
features and identify different particle interaction types in other 3D-tracking
detectors.
