Yeshua Talia, Ladyzhensky Shmuel, Abu-Nasser Amal, Abdalla-Aslan Ragda, Boharon Tami, Itzhak-Pur Avital, Alexander Asher, Chaurasia Akhilanand, Cohen Adir, Sosna Jacob, Leichter Isaac, Nadler Chen
Department of Applied Physics, The Jerusalem College of Technology, Jerusalem, Israel.
Oral Maxillofacial Imaging, Department of Oral Medicine, Sedation and Imaging, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel.
Eur Radiol. 2023 Nov;33(11):7507-7518. doi: 10.1007/s00330-023-09726-6. Epub 2023 May 16.
To develop an automated deep-learning algorithm for detection and 3D segmentation of incidental bone lesions in maxillofacial CBCT scans.
The dataset included 82 cone beam CT (CBCT) scans, 41 with histologically confirmed benign bone lesions (BL) and 41 control scans (without lesions), obtained using three CBCT devices with diverse imaging protocols. Lesions were marked in all axial slices by experienced maxillofacial radiologists. All cases were divided into sub-datasets: training (20,214 axial images), validation (4530 axial images), and testing (6795 axial images). A Mask-RCNN algorithm segmented the bone lesions in each axial slice. Analysis of sequential slices was used for improving the Mask-RCNN performance and classifying each CBCT scan as containing bone lesions or not. Finally, the algorithm generated 3D segmentations of the lesions and calculated their volumes.
The algorithm correctly classified all CBCT cases as containing bone lesions or not, with an accuracy of 100%. The algorithm detected the bone lesion in axial images with high sensitivity (95.9%) and high precision (98.9%) with an average dice coefficient of 83.5%.
The developed algorithm detected and segmented bone lesions in CBCT scans with high accuracy and may serve as a computerized tool for detecting incidental bone lesions in CBCT imaging.
Our novel deep-learning algorithm detects incidental hypodense bone lesions in cone beam CT scans, using various imaging devices and protocols. This algorithm may reduce patients' morbidity and mortality, particularly since currently, cone beam CT interpretation is not always preformed.
• A deep learning algorithm was developed for automatic detection and 3D segmentation of various maxillofacial bone lesions in CBCT scans, irrespective of the CBCT device or the scanning protocol. • The developed algorithm can detect incidental jaw lesions with high accuracy, generates a 3D segmentation of the lesion, and calculates the lesion volume.
开发一种用于检测和三维分割颌面部锥形束计算机断层扫描（CBCT）中偶然发现的骨病变的自动化深度学习算法。
数据集包括82例锥形束CT（CBCT）扫描，其中41例有组织学证实的良性骨病变（BL），41例为对照扫描（无病变），使用三种具有不同成像协议的CBCT设备获得。经验丰富的颌面部放射科医生在所有轴向切片上标记病变。所有病例分为子数据集：训练集（20214个轴向图像）、验证集（4530个轴向图像）和测试集（6795个轴向图像）。Mask-RCNN算法对每个轴向切片中的骨病变进行分割。对连续切片进行分析以提高Mask-RCNN性能，并将每个CBCT扫描分类为是否包含骨病变。最后，该算法生成病变的三维分割并计算其体积。
该算法正确地将所有CBCT病例分类为是否包含骨病变，准确率为100%。该算法在轴向图像中检测骨病变具有高灵敏度（95.9%）和高精度（98.9%），平均骰子系数为83.5%。
所开发的算法在CBCT扫描中检测和分割骨病变具有很高的准确性，可作为CBCT成像中检测偶然发现的骨病变的计算机化工具。
我们新颖的深度学习算法可使用各种成像设备和协议检测锥形束CT扫描中偶然发现的低密度骨病变。该算法可能降低患者的发病率和死亡率，特别是因为目前锥形束CT的解读并非总是进行。
• 开发了一种深度学习算法，用于自动检测和三维分割CBCT扫描中各种颌面部骨病变，而不考虑CBCT设备或扫描协议。• 所开发的算法能够高精度检测偶然发现的颌骨病变，生成病变的三维分割并计算病变体积。
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