  We present the performance of a semantic segmentation network, SparseSSNet,
that provides pixel-level classification of MicroBooNE data. The MicroBooNE
experiment employs a liquid argon time projection chamber for the study of
neutrino properties and interactions. SparseSSNet is a submanifold sparse
convolutional neural network, which provides the initial machine learning based
algorithm utilized in one of MicroBooNE's $\nu_e$-appearance oscillation
analyses. The network is trained to categorize pixels into five classes, which
are re-classified into two classes more relevant to the current analysis. The
output of SparseSSNet is a key input in further analysis steps. This technique,
used for the first time in liquid argon time projection chambers data and is an
improvement compared to a previously used convolutional neural network, both in
accuracy and computing resource utilization. The accuracy achieved on the test
sample is $\geq 99\%$. For full neutrino interaction simulations, the time for
processing one image is $\approx$ 0.5 sec, the memory usage is at 1 GB level,
which allows utilization of most typical CPU worker machine.
