ACADSTAFF UGM

CREATION
Title : Comparison of Multi-Label U-Net and Mask R-CNN for panoramic radiograph segmentation to detect periodontitis
Author :

Dr. drg. Rini Widyaningrum, M.Biotech (1) Ika Candradewi, S.Si., M.Cs. (2) drg. Nur Rahman Ahmad Seno Aji, MDSc., Sp.Perio (K) MP (3) RONA AULIANISA (4)
Date : 12 2022
Keyword : radiography,panoramic,deep learning,periodontitis,tooth radiography,panoramic,deep learning,periodontitis,tooth
Abstract : Purpose Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches. Materials and Methods Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models. Results The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics (i.e., dice coefficient and intersection-over-union [IoU] score). Multi-Label U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively. Conclusion Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.
Group of Knowledge :
Original Language : English
Level : Internasional
Status :
Published
Document
No Title Document Type Action
1 0 Rini_2022_ISD_Bukti korespondensi.pdf
Document Type : [PAK] Bukti Korespondensi Penulis
[PAK] Bukti Korespondensi Penulis View
2 C5_2022 ISD Widyaningrum dkk_syarat khusus.pdf
Document Type : [PAK] Full Dokumen
[PAK] Full Dokumen View
3 REVISI-Published Ver-Comparison of Multi-Label U-Net and Mask R-CNN for panoramic radiograph segmentation to detect periodontitis.pdf
Document Type : [PAK] Cek Similarity
[PAK] Cek Similarity View