تقرير
Teeth Localization and Lesion Segmentation in CBCT Images using SpatialConfiguration-Net and U-Net
| العنوان: | Teeth Localization and Lesion Segmentation in CBCT Images using SpatialConfiguration-Net and U-Net |
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| المؤلفون: | Hadzic, Arnela, Kirnbauer, Barbara, Stern, Darko, Urschler, Martin |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | The localization of teeth and segmentation of periapical lesions in cone-beam computed tomography (CBCT) images are crucial tasks for clinical diagnosis and treatment planning, which are often time-consuming and require a high level of expertise. However, automating these tasks is challenging due to variations in shape, size, and orientation of lesions, as well as similar topologies among teeth. Moreover, the small volumes occupied by lesions in CBCT images pose a class imbalance problem that needs to be addressed. In this study, we propose a deep learning-based method utilizing two convolutional neural networks: the SpatialConfiguration-Net (SCN) and a modified version of the U-Net. The SCN accurately predicts the coordinates of all teeth present in an image, enabling precise cropping of teeth volumes that are then fed into the U-Net which detects lesions via segmentation. To address class imbalance, we compare the performance of three reweighting loss functions. After evaluation on 144 CBCT images, our method achieves a 97.3% accuracy for teeth localization, along with a promising sensitivity and specificity of 0.97 and 0.88, respectively, for subsequent lesion detection. Comment: Accepted for VISIGRAPP 2024 (Track: VISAPP), 8 pages |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2312.12189 |
| رقم الأكسشن: | edsarx.2312.12189 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |