Comparative parotid gland segmentation by using ResNet-18 and MobileNetV2 based DeepLab v3+architectures from magnetic resonance images

Sunnetci K. M., KABA E., BEYAZAL ÇELİKER F., ALKAN A.

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, cilt.35, sa.1, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 35 Sayı: 1
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1002/cpe.7405
  • Dergi Adı: CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: augmentation, DeepLab v3+, MobileNetV2, parotid gland, ResNet-18, semantic segmentation, AUTO-SEGMENTATION, HEAD, CT, ORGANS, RISK, RADIOTHERAPY, DELINEATION, FRAMEWORK, MODEL
  • Recep Tayyip Erdoğan Üniversitesi Adresli: Evet

Özet

Nowadays, artificial intelligence-based medicine plays an important role in determining correlations not comprehensible to humans. In addition, the segmentation of organs at risk is a tedious and time-consuming procedure. Segmentation of these organs or tissues is widely used in early diagnosis, treatment planning, and diagnosis. In this study, we trained semantic segmentation networks to segment healthy parotid glands using deep learning. The dataset we used in the study was obtained from Recep Tayyip Erdogan University Training and Research Hospital, and there were 72 T2-weighted magnetic resonance (MR) images in this dataset. After these images were manually segmented by experts, masks of these images were obtained according to them and all images were cropped. Afterward, these cropped images and masks were rotated 45 degrees, 120 degrees, and 210 degrees, quadrupling the number of images. We trained ResNet-18/MobileNetV2-based DeepLab v3+ without augmentation and ResNet-18/MobileNetV2-based DeepLab v3+ with augmentation using these datasets. Here, we set the training set and testing set sizes for all architectures to be 80% and 20%, respectively. We designed two different graphical user interface (GUI) applications so that users can easily segment their parotid glands by utilizing all of these deep learning-based semantic segmentation networks. From the results, mean-weighted dice values of MobileNetV2-based DeepLab v3+ without augmentation and ResNet-18-based DeepLab v3+ with augmentation were equal to 0.90845-0.93931 and 0.93237-0.96960, respectively. We also noted that the sensitivity (%), specificity (%), F-1 score (%) values of these models were equal to 83.21, 96.65, 85.04 and 89.81, 97.84, 87.80, respectively. As a result, these designed models were found to be clinically successful, and the user-friendly GUI applications of these proposed systems can be used by clinicians. This study is competitive as it uses MR images, can automatically segment both parotid glands, the results are meaningful according to the literature and have software application.