PURPOSE Volumetric assessment of meningiomas represents a valuable tool for treatment planning and evaluation of tumor growth as it enables a more precise assessment of tumor size than conventional diameter methods. This study established a dedicated meningioma deep learning model based on routine magnetic resonance imaging (MRI) data and evaluated its performance for automated tumor segmentation. METHODS The MRI datasets included T1-weighted/T2-weighted, T1-weighted contrast-enhanced (T1CE) and FLAIR of 126 patients with intracranial meningiomas (grade I: 97, grade II: 29). For automated segmentation, an established deep learning model architecture (3D deep convolutional neural network, DeepMedic, BioMedIA) operating on all four MR sequences was used. Segmentation included the following two components: (i) contrast-enhancing tumor volume in T1CE and (ii) total lesion volume (union of lesion volume in T1CE and FLAIR, including solid tumor parts and surrounding edema). Preprocessing of imaging data included registration, skull stripping, resampling, and normalization. After training of the deep learning model using manual segmentations by 2 independent readers from 70 patients (training group), the algorithm was evaluated on 56 patients (validation group) by comparing automated to ground truth manual segmentations, which were performed by 2 experienced readers in consensus. RESULTS Of the 56 meningiomas in the validation group 55 were detected by the deep learning model. In these patients the comparison of the deep learning model and manual segmentations revealed average dice coefficients of 0.91 ± 0.08 for contrast-enhancing tumor volume and 0.82 ± 0.12 for total lesion volume. In the training group, interreader variabilities of the 2 manual readers were 0.92 ± 0.07 for contrast-enhancing tumor and 0.88 ± 0.05 for total lesion volume. CONCLUSION Deep learning-based automated segmentation yielded high segmentation accuracy, comparable to manual interreader variability.

中文翻译：

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多参数 MRI 中的自动脑膜瘤分割：深度学习模型和手动分割的可比有效性。

目的脑膜瘤的体积评估是治疗计划和评估肿瘤生长的宝贵工具，因为它比传统的直径方法能够更精确地评​​估肿瘤大小。本研究基于常规磁共振成像 (MRI) 数据建立了专用的脑膜瘤深度学习模型，并评估了其自动肿瘤分割的性能。方法 MRI 数据集包括 126 名颅内脑膜瘤患者的 T1 加权/T2 加权、T1 加权对比增强 (T1CE) 和 FLAIR（I 级：97，II 级：29）。对于自动分割，使用了在所有四个 MR 序列上运行的已建立的深度学习模型架构（3D 深度卷积神经网络、DeepMedic、BioMedIA）。细分包括以下两个组成部分：(i) T1CE 中的对比增强肿瘤体积和 (ii) 总病灶体积（T1CE 和 FLAIR 中病灶体积的结合，包括实体瘤部分和周围水肿）。成像数据的预处理包括配准、颅骨剥离、重采样和归一化。在使用来自 70 名患者（训练组）的 2 名独立读者使用手动分割训练深度学习模型后，通过比较自动化与地面实况手动分割，对 56 名患者（验证组）评估该算法，该算法由 2 名经验丰富的读者在共识。结果 验证组的 56 个脑膜瘤中有 55 个被深度学习模型检测到。在这些患者中，深度学习模型和手动分割的比较显示，对比增强肿瘤体积的平均骰子系数为 0.91 ± 0.08，而平均骰子系数为 0。82 ± 0.12 总病变体积。在训练组中，2 位手动阅读器的阅读器间变异对于对比增强肿瘤为 0.92 ± 0.07，对于总病变体积为 0.88 ± 0.05。结论基于深度学习的自动分割产生了高分割精度，可与手动阅读器间的可变性相媲美。