Lightweight Encoder-Decoder Architecture for Foot Ulcer Segmentation

Shahzad Ali, Arif Mahmood, Soon Ki Jung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageEnglish
Title of host publicationFrontiers of Computer Vision - 28th International Workshop, IW-FCV 2022, Revised Selected Papers
EditorsKazuhiko Sumi, In Seop Na, Naoshi Kaneko
PublisherSpringer Science and Business Media Deutschland GmbH
Pages242-253
Number of pages12
ISBN (Print)9783031063800
DOIs
StatePublished - 2022
Event28th International Workshop on Frontiers of Computer Vision, IW-FCV 2022 - Virtual, Online
Duration: 21 Feb 202222 Feb 2022

Publication series

NameCommunications in Computer and Information Science
Volume1578 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference28th International Workshop on Frontiers of Computer Vision, IW-FCV 2022
CityVirtual, Online
Period21/02/2222/02/22

Keywords

  • Attention mechanism
  • Encoder-decoder architecture
  • Foot ulcer segmentation
  • Medical image segmentation

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