Leveraging Pretrained Transformers for Efficient Segmentation and Lesion Detection in Cone-Beam Computed Tomography Scans.

ABSTRACT:

Introduction

Cone-beam computed tomography (CBCT) is widely used to detect jaw lesions, although CBCT interpretation is time-consuming and challenging. Artificial intelligence for CBCT segmentation may improve lesion detection accuracy. However, consistent automated lesion detection remains difficult, especially with limited training data. This study aimed to assess the applicability of pretrained transformer-based architectures for semantic segmentation of CBCT volumes when applied to periapical lesion detection.

Methods

CBCT volumes (n = 138) were collected and annotated by expert clinicians using 5 labels - "lesion," "restorative material," "bone," "tooth structure," and "background." U-Net (convolutional neural network-based) and Swin-UNETR (transformer-based) models, pr

SUBMITTER: Chen RQ 

PROVIDER: S-EPMC11471365 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature