Prediction of Plantar Shear Stress Distribution by Conditional GAN with Attention Mechanism

Jinghui Guo, Ali Ersen, Yang Gao, Yu Lin, Latifur Khan, Metin Yavuz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Diabetic foot ulcers (DFUs) are known to have multifactorial etiology. Among the biomechanical factors that lead to plantar ulcers, shear stresses have been either neglected or unmeasured due to challenges in complexity and equipment availability. The purpose of this study is to develop a software that predicts plantar shear stress using plantar pressure and temperature distributions. Thirty-one subjects, 8 of them at risk of developing DFUs were recruited, and plantar thermography, pressure and shear stress distributions were collected. We introduce the conditional generative adversarial networks (cGAN) for shear stress distribution prediction and propose an attention mechanism to improve the model’s accuracy. The networks can learn the mapping from pressure to shear stress distribution. The attention mechanism can merge temperature distribution into GAN without resizing or aligning it manually. We then test on our dataset with 185 groups. The predicted anteroposterior shear stress distributions give accuracy on peak location prediction and 14.12 kPa on global root mean square error. Our initial results are promising in terms of feasibility of our approach in predicting plantar shear stresses and this approach may benefit to address the DFU risks before ulceration.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages770-780
Number of pages11
ISBN (Print)9783030597122
DOIs
StatePublished - 2020
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: Oct 4 2020Oct 8 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12262 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period10/4/2010/8/20

Keywords

  • Conditional GAN
  • Diabetic foot ulcers
  • Plantar shear stress
  • Positional attention

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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