利用三阶段神经网络检测 CBCT 扫描中的下颌骨骨折

N van Nistelrooij, S Schitter, P van Lierop, K El Ghoul, D König, M Hanisch, A Tel, T Xi, D G E Thiem, R Smeets, L Dubois, T Flügge, B van Ginneken, S Bergé, S Vinayahalingam
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引用次数: 0

摘要

继鼻骨骨折之后,下颌骨骨折是面部骨骼最常见的损伤。准确识别骨折位置对于有效处理这些损伤至关重要。为了满足这一需求,我们开发了一种创新的人工智能方法--JawFracNet,用于自动检测锥形束计算机断层扫描(CBCT)中的下颌骨骨折。JawFracNet 采用三阶段神经网络模型,处理 CBCT 扫描的三维斑块。第一阶段预测补丁中下颌骨的分割掩模,第二阶段预测骨折的分割,第三阶段对补丁是否包含骨折进行分类。JawFracNet 的最终输出是整个扫描的骨折分割,它是通过汇总和统一体素级和斑块级预测而获得的。本研究共纳入了 164 个无下颌骨骨折的 CBCT 扫描和 171 个有下颌骨骨折的 CBCT 扫描。对 JawFracNet 的评估表明,在检测下颌骨骨折方面,其精确度为 0.978,灵敏度为 0.956。本研究首次提出了在 CBCT 扫描中检测下颌骨骨折的基准。通过在grand-challenge.org网站上公开共享代码和提供JawFracNet的访问权限,促进了直接复制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting Mandible Fractures in CBCT Scans Using a 3-Stage Neural Network.

After nasal bone fractures, fractures of the mandible are the most frequently encountered injuries of the facial skeleton. Accurate identification of fracture locations is critical for effectively managing these injuries. To address this need, JawFracNet, an innovative artificial intelligence method, has been developed to enable automated detection of mandibular fractures in cone-beam computed tomography (CBCT) scans. JawFracNet employs a 3-stage neural network model that processes 3-dimensional patches from a CBCT scan. Stage 1 predicts a segmentation mask of the mandible in a patch, which is subsequently used in stage 2 to predict a segmentation of the fractures and in stage 3 to classify whether the patch contains any fracture. The final output of JawFracNet is the fracture segmentation of the entire scan, obtained by aggregating and unifying voxel-level and patch-level predictions. A total of 164 CBCT scans without mandibular fractures and 171 CBCT scans with mandibular fractures were included in this study. Evaluation of JawFracNet demonstrated a precision of 0.978 and a sensitivity of 0.956 in detecting mandibular fractures. The current study proposes the first benchmark for mandibular fracture detection in CBCT scans. Straightforward replication is promoted by publicly sharing the code and providing access to JawFracNet on grand-challenge.org.

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