  The Liquid Argon Time Projection Chamber (LArTPC) is an advanced neutrino
detector technology widely used in recent and upcoming accelerator neutrino
experiments. It features a low energy threshold and high spatial resolution
that allow for comprehensive reconstruction of event topologies. In
current-generation LArTPCs, the recorded data consist of digitized waveforms on
wires produced by induced signal on wires of drifting ionization electrons,
which can also be viewed as two-dimensional (2D) (time versus wire) projection
images of charged-particle trajectories. For such an imaging detector, one
critical step is the signal processing that reconstructs the original charge
projections from the recorded 2D images. For the first time, we introduce a
deep neural network in LArTPC signal processing to improve the signal region of
interest detection. By combining domain knowledge (e.g., matching information
from multiple wire planes) and deep learning, this method shows significant
improvements over traditional methods. This work details the method, software
tools, and performance evaluated with realistic detector simulations.
