  This paper presents FUSegNet, a new model for foot ulcer segmentation in
diabetes patients, which uses the pre-trained EfficientNet-b7 as a backbone to
address the issue of limited training samples. A modified spatial and channel
squeeze-and-excitation (scSE) module called parallel scSE or P-scSE is proposed
that combines additive and max-out scSE. A new arrangement is introduced for
the module by fusing it in the middle of each decoder stage. As the top decoder
stage carries a limited number of feature maps, max-out scSE is bypassed there
to form a shorted P-scSE. A set of augmentations, comprising geometric,
morphological, and intensity-based augmentations, is applied before feeding the
data into the network. The proposed model is first evaluated on a publicly
available chronic wound dataset where it achieves a data-based dice score of
92.70%, which is the highest score among the reported approaches. The model
outperforms other scSE-based UNet models in terms of Pratt's figure of merits
(PFOM) scores in most categories, which evaluates the accuracy of edge
localization. The model is then tested in the MICCAI 2021 FUSeg challenge,
where a variation of FUSegNet called x-FUSegNet is submitted. The x-FUSegNet
model, which takes the average of outputs obtained by FUSegNet using 5-fold
cross-validation, achieves a dice score of 89.23%, placing it at the top of the
FUSeg Challenge leaderboard. The source code for the model is available on
https://github.com/mrinal054/FUSegNet.
