Lightweight Encoder-Decoder Architecture for Foot Ulcer Segmentation

TL;DR

This paper introduces a lightweight encoder-decoder neural network with residual and attention mechanisms for foot ulcer segmentation, achieving state-of-the-art results without transfer learning or large backbones.

Contribution

The authors developed a novel, lightweight foot ulcer segmentation model that outperforms existing methods without relying on pre-training or transfer learning.

Findings

Achieved 88.22% Dice score on FUSeg dataset

Ranked second in MICCAI 2021 challenge

Model has around 5 million parameters, making it lightweight

Abstract

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.