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Fully Automatic Segmentation, Identification and Preoperative Planning for Nasal Surgery of Sinuses Using Semi-Supervised Learning and Volumetric Reconstruction

Author

Listed:
  • Chung-Feng Jeffrey Kuo

    (Department of Materials Science & Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan)

  • Shao-Cheng Liu

    (Department of Otolaryngology-Head and Neck Surgery, Taichung Armed Forces General Hospital, Taichung 411, Taiwan
    Department of Otolaryngology-Head and Neck Surgery Tri-Service General Hospital, Taipei 114, Taiwan
    National Defense Medical Center, Taipei 114, Taiwan)

Abstract

The aim of this study is to develop an automatic segmentation algorithm based on paranasal sinus CT images, which realizes automatic identification and segmentation of the sinus boundary and its inflamed proportions, as well as the reconstruction of normal sinus and inflamed site volumes. Our goal is to overcome the current clinical dilemma of manually calculating the inflammatory sinus volume, which is objective and ineffective. A semi-supervised learning algorithm using pseudo-labels for self-training was proposed to train convolutional neural networks, which consisted of SENet, MobileNet, and ResNet. An aggregate of 175 CT sets was analyzed, 50 of which were from patients who subsequently underwent sinus surgery. A 3D view and volume-based modified Lund-Mackay score were determined and compared with traditional scores. Compared to state-of-the-art networks, our modifications achieved significant improvements in both sinus segmentation and classification, with an average pixel accuracy of 99.67%, an MIoU of 89.75%, and a Dice coefficient of 90.79%. The fully automatic nasal sinus volume reconstruction system was successfully obtained the relevant detailed information by accurately acquiring the nasal sinus contour edges in the CT images. The accuracy of our algorithm has been validated and the results can be effectively applied to actual clinical medicine or forensic research.

Suggested Citation

  • Chung-Feng Jeffrey Kuo & Shao-Cheng Liu, 2022. "Fully Automatic Segmentation, Identification and Preoperative Planning for Nasal Surgery of Sinuses Using Semi-Supervised Learning and Volumetric Reconstruction," Mathematics, MDPI, vol. 10(7), pages 1-32, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1189-:d:787503
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    References listed on IDEAS

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    1. Guilherme Giacomini & Ana Luiza Menegatti Pavan & João Mauricio Carrasco Altemani & Sergio Barbosa Duarte & Carlos Magno Castelo Branco Fortaleza & José Ricardo de Arruda Miranda & Diana Rodrigues de , 2018. "Computed tomography-based volumetric tool for standardized measurement of the maxillary sinus," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-12, January.
    2. Syed Furqan Qadri & Linlin Shen & Mubashir Ahmad & Salman Qadri & Syeda Shamaila Zareen & Muhammad Azeem Akbar, 2022. "SVseg: Stacked Sparse Autoencoder-Based Patch Classification Modeling for Vertebrae Segmentation," Mathematics, MDPI, vol. 10(5), pages 1-19, March.
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