Lightweight Encoder-Decoder Architecture for Foot Ulcer Segmentation

• URL: http://arxiv.org/abs/2207.02515v1
• Date: Wed, 6 Jul 2022 08:42:29 GMT
• Title: Lightweight Encoder-Decoder Architecture for Foot Ulcer Segmentation
• Authors: Shahzad Ali, Arif Mahmood, Soon Ki Jung
• Abstract summary: Continuous monitoring of foot ulcer healing is needed to ensure the efficacy of a given treatment and to avoid any possibility of deterioration. We developed a model that is similar in spirit to the well-established encoder-decoder and residual convolution neural networks. A simple patch-based approach for model training, test time augmentations, and majority voting on the obtained predictions resulted in superior performance.
• Score: 12.729149322066249
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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