A deep learning approach for masseter muscle segmentation on ultrasonography

Keser G., Bayrakdar İ. Ş., Namdar Pekiner F. M., Çelik Ö., Orhan K.

JOURNAL OF ULTRASONOGRAPHY, cilt.22, sa.91, ss.204-208, 2022 (ESCI) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Sayı: 91
  • Basım Tarihi: 2022
  • Doi Numarası: 10.15557/jou.2022.0034
  • Dergi Adı: JOURNAL OF ULTRASONOGRAPHY
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Academic Search Premier, EMBASE, Directory of Open Access Journals
  • Sayfa Sayıları: ss.204-208
  • Anahtar Kelimeler: ultrasonography, masseter muscle, deep learning, artificial intelligence, ARTIFICIAL-INTELLIGENCE, ORAL RADIOLOGY, NEURAL-NETWORK, THICKNESS, SIZE, ULTRASOUND, CLASSIFICATION, PERCEPTIONS, KNOWLEDGE, ATTITUDES
  • Marmara Üniversitesi Adresli: Evet

Özet

Aim: Deep learning algorithms have lately been used for medical image processing, and they have showed promise in a range of applications. The purpose of this study was to develop and test computer-based diagnostic tools for evaluating masseter muscle segmentation on ultrasonography images. Materials and methods: A total of 383 anonymous adult masseter muscle retrospective ultrasonographic images were evaluated. The masseter muscle was labeled on ultrasonography images using the polygonal type labeling method with the CranioCatch labeling program (CranioCatch, Eskisehir, Turkey). All images were re-checked and verified by Oral and Maxillofacial Radiology experts. This data set was divided into training (n = 312), verification (n = 38) and test (n = 33) sets. In the study, an artificial intelligence model was developed using PyTorch U-Net architecture, which is a deep learning approach. Results: In our study, the artificial intelligence deep learning model known as U-net provided the detection and segmentation of all test images, and when the success rate in the estimation of the images was evaluated, the F1, sensitivity and precision results of the model were 1.0, 1.0 and 1.0, respectively. Conclusion: Artificial intelligence shows promise in automatic segmentation of masseter muscle on ultrasonography images. This strategy can aid surgeons, radiologists, and other medical practitioners in reducing diagnostic time.