Abstract：
Three-dimensional (3D) ultrasound can assess the margins of resected tongue carcinoma during surgery. Manual segmentation (MS) is time-consuming, labour-intensive, and subject to operator variability. This study aims to investigate use of a 3D deep learning model for fast intraoperative segmentation of tongue carcinoma in 3D ultrasound volumes. Additionally, it investigates the clinical effect of automatic segmentation. A 3D No New U-Net (nnUNet) was trained on 113 manually annotated ultrasound volumes of resected tongue carcinoma. The model was implemented on a mobile workstation and clinically validated on 16 prospectively included tongue carcinoma patients. Different prediction settings were investigated. Automatic segmentations with multiple islands were adjusted by selecting the best-representing island. The final margin status (FMS) based on automatic, semi-automatic, and manual segmentation was computed and compared with the histopathological margin. The standard 3D nnUNet resulted in the best-performing automatic segmentation with a mean (SD) Dice volumetric score of 0.65 (0.30), Dice surface score of 0.73 (0.26), average surface distance of 0.44 (0.61) mm, Hausdorff distance of 6.65 (8.84) mm, and prediction time of 8 seconds. FMS based on automatic segmentation had a low correlation with histopathology (r = 0.12, p = 0.67); MS resulted in a moderate but insignificant correlation with histopathology (r = 0.4, p = 0.12, n = 16). Implementing the 3D nnUNet yielded fast, automatic segmentation of tongue carcinoma in 3D ultrasound volumes. Correlation between FMS and histopathology obtained from these segmentations was lower than the moderate correlation between MS and histopathology.
摘要：
三维（3D）超声可以评估手术期间切除的舌癌的边缘。手动分割(MS)是耗时的，劳动密集型,并受操作员可变性的影响。本研究旨在研究3D深度学习模型在3D超声体积中快速分割舌癌的应用。此外,它研究了自动分割的临床效果。3DNoNewU-Net（nnUNet）在113个手动注释的切除的舌癌超声体积上进行了训练。该模型在移动工作站上实施，并在16名前瞻性纳入的舌癌患者中进行了临床验证。研究了不同的预测设置。通过选择最佳代表岛来调整具有多个岛的自动分割。最终边距状态（FMS）基于自动，半自动,并计算手动分割，并与组织病理学边缘进行比较。标准3DnnUNet产生了性能最佳的自动分割，平均（SD）骰子体积评分为0.65（0.30），骰子表面评分为0.73（0.26），平均表面距离为0.44(0.61)mm，Hausdorff距离6.65(8.84)mm,预测时间为8秒。基于自动分割的FMS与组织病理学的相关性较低（r=0.12，p=0.67）；MS与组织病理学的相关性中等，但不明显（r=0.4，p=0.12，n=16）。实现3DnnUNet很快就产生了，舌癌三维超声容积的自动分割.从这些分段获得的FMS与组织病理学之间的相关性低于MS与组织病理学之间的中度相关性。
