AUTOMATIC DETECTION OF FACIAL LOCATIONS TO MEASURE FACIAL ASYMMETRY AFTER PAEDIATRIC RADIOTHERAPY
AUTOMATIC DETECTION OF FACIAL LOCATIONS TO MEASURE FACIAL ASYMMETRY AFTER PAEDIATRIC RADIOTHERAPY
复制标题
自动检测面部位置以测量小儿放射治疗后的面部不对称度
DOI：
10.1093/neuonc/noad147.059
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发表时间：
2023
期刊：
影响因子：
15.9
通讯作者：
Dronne C
中科院分区：
文献类型：
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作者：
Dronne C
接受头颈部肿瘤放疗的儿童，如头颈部横纹肌肉瘤（HN-RMS），在以后的生活中经常会出现面部不对称。在这里，我们提出了一个卷积神经网络（CNN），它将能够自动检测在随访检查期间拍摄的MR图像上的面部解剖标志。该模型将有助于定量跟踪面部不对称，从而揭示受辐射影响最大的生长中心，最终目标是确定精确的剂量耐受水平。方法使用61张儿科MRI图像的数据集来训练推断四个面部标志位置的单独模型。这些地点以前是由两名独立观察员手工标记的。在训练之前，将图像归一化并配准，以优化模型性能。如果观察到的标志（金标准位置）和训练模型预测的位置之间的距离小于每个面部标志的观察者间距离，则该模型被判定为具有可接受的准确性。结果：该模型预测的标志点更接近金标准位置，而不是观察者之间的位置。·视交叉：0.81 ± 0.45 mm（观察者间1.49 mm）。·鼻根：0.70 ± 0.62 mm（观察者间3.24 mm）。·左外甲：1.22 ± 0.44 mm（观察者间3.58 mm）。·右外甲：0.80 ± 0.82 mm（观察者间4.04 mm）。结论：这项研究显示了CNN在儿科MRI图像中准确定位头颈部标志的潜力。与手动技术相比，该工具可以更快、更准确地标记大型临床数据集中的颅面标志。
AIMS Children receiving radiotherapy for head and neck tumours, such as Head and Neck Rhabdomyosarcoma (HN- RMS), often experience facial asymmetry later in life. Here, we present a convolutional neural network (CNN) that will enable the automatic detection of facial anatomical landmarks on MR images taken during follow-up examinations. This model will facilitate quantitative tracking of facial asymmetry, thus revealing the centres of growth that are most affected by radiation, with the ultimate goal of defining precise dose tolerance levels. METHOD A dataset of 61 paediatric MRI images was used to train separate models that infer the locations of four facial landmarks. These locations had previously been manually labelled by two independent observers. Before training, the images were normalised and registered to optimise model performance. The model was judged to be of acceptable accuracy if the distances between the observed landmark (gold-standard location) and the location predicted by the trained models were smaller than the inter-observer distances of each facial landmark. RESULTS The model predicted landmarks which were closer to the gold-standard location than the inter-observer locations. In detail: • Optic chiasm: 0.81 ± 0.45 mm (inter-observer 1.49 mm). • Nasion: 0.70 ± 0.62 mm (inter-observer 3.24 mm). • Left Ectoconchion: 1.22 ± 0.44 mm (inter-observer 3.58 mm). • Right Ectoconchion: 0.80 ± 0.82 mm (inter-observer 4.04 mm). CONCLUSIONS This research shows promising potential for a CNN to accurately locate head and neck landmarks in paediatric MRI images. This tool can label craniofacial landmarks in large clinical datasets quicker and more accurately than can be achieved through manual techniques.