Deep learning model for automated segmentation of sphenoid sinus and middle skull base structures in CBCT volumes using nnU-Net v2.
Authors
Affiliations (8)
Affiliations (8)
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Alanya Alaaddin Keykubat University, Antalya, 07425, Turkey.
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Kocaeli University, Kocaeli, 41190, Turkey. [email protected].
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, 06560, Turkey.
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, 26040, Turkey.
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Selçuk University, Konya, 42130, Turkey.
- Department of Mathematics-Computer, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, 26040, Turkey.
- Director, Center of Research and Application for Computer-Aided Diagnosis and Treatment in Health (ESOGU-SABIT), Eskisehir Osmangazi University, Eskisehir, Turkey.
- Department of Radiology, Faculty of Medicine, Eskisehir Osmangazi University, Eskisehir, 26040, Turkey.
Abstract
The purpose of this study is the development of a deep learning model based on nnU-Net v2 for the automated segmentation of sphenoid sinus and middle skull base anatomic structures in cone-beam computed tomography (CBCT) volumes, followed by an evaluation of the model's performance. In this retrospective study, the sphenoid sinus and surrounding anatomical structures in 99 CBCT scans were annotated using web-based labeling software. Model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.01 for 1000 epochs. The performance of the model in automatically segmenting these anatomical structures in CBCT scans was evaluated using a series of metrics, including accuracy, precision, recall, dice coefficient (DC), 95% Hausdorff distance (95% HD), intersection on union (IoU), and AUC. The developed deep learning model demonstrated a high level of success in segmenting sphenoid sinus, foramen rotundum, and Vidian canal. Upon evaluation of the DC values, it was observed that the model demonstrated the highest degree of ability to segment the sphenoid sinus, with a DC value of 0.96. The nnU-Net v2-based deep learning model achieved high segmentation performance for the sphenoid sinus, foramen rotundum, and Vidian canal within the middle skull base, with the highest DC observed for the sphenoid sinus (DC: 0.96). However, the model demonstrated limited performance in segmenting other foramina of the middle skull base, indicating the need for further optimization for these structures.