  Continuous monitoring of foot ulcer healing is needed to ensure the efficacy
of a given treatment and to avoid any possibility of deterioration. Foot ulcer
segmentation is an essential step in wound diagnosis. We developed a model that
is similar in spirit to the well-established encoder-decoder and residual
convolution neural networks. Our model includes a residual connection along
with a channel and spatial attention integrated within each convolution block.
A simple patch-based approach for model training, test time augmentations, and
majority voting on the obtained predictions resulted in superior performance.
Our model did not leverage any readily available backbone architecture,
pre-training on a similar external dataset, or any of the transfer learning
techniques. The total number of network parameters being around 5 million made
it a significantly lightweight model as compared with the available
state-of-the-art models used for the foot ulcer segmentation task. Our
experiments presented results at the patch-level and image-level. Applied on
publicly available Foot Ulcer Segmentation (FUSeg) Challenge dataset from
MICCAI 2021, our model achieved state-of-the-art image-level performance of
88.22% in terms of Dice similarity score and ranked second in the official
challenge leaderboard. We also showed an extremely simple solution that could
be compared against the more advanced architectures.
