Automatic segmentation for magnetic resonance imaging guided individual elective lymph node irradiation in head and neck cancer patients

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Abstract

Background and purpose

In head and neck squamous cell carcinoma (HNSCC) patients, the radiation dose to nearby organs at risk can be reduced by restricting elective neck irradiation from lymph node levels to individual lymph nodes. However, manual delineation of every individual lymph node is time-consuming and error prone. Therefore, automatic magnetic resonance imaging (MRI) segmentation of individual lymph nodes was developed and tested using a convolutional neural network (CNN).

Materials and methods

In 50 HNSCC patients (UMC-Utrecht), individual lymph nodes located in lymph node levels Ib-II-III-IV-V were manually segmented on MRI by consensus of two experts, obtaining ground truth segmentations. A 3D CNN (nnU-Net) was trained on 40 patients and tested on 10. Evaluation metrics were Dice Similarity Coefficient (DSC), recall, precision, and F1-score. The segmentations of the CNN was compared to segmentations of two observers. Transfer learning was used with 20 additional patients to re-train and test the CNN in another medical center.

Results

nnU-Net produced automatic segmentations of elective lymph nodes with median DSC: 0.72, recall: 0.76, precision: 0.78, and F1-score: 0.78. The CNN had higher recall compared to both observers (p = 0.002). No difference in evaluation scores of the networks in both medical centers was found after re-training with 5 or 10 patients.

Conclusion

nnU-Net was able to automatically segment individual lymph nodes on MRI. The detection rate of lymph nodes using nnU-Net was higher than manual segmentations. Re-training nnU-Net was required to successfully transfer the network to the other medical center.
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磁共振成像引导头颈部癌症患者进行个体选择性淋巴结照射的自动分割技术
背景和目的在头颈部鳞状细胞癌(HNSCC)患者中,通过将选择性颈部照射从淋巴结水平限制到单个淋巴结,可以减少对附近危险器官的辐射剂量。然而,人工划定每个淋巴结既费时又容易出错。因此,我们使用卷积神经网络(CNN)开发并测试了单个淋巴结的自动磁共振成像(MRI)分割。材料与方法在 50 名 HNSCC 患者(UMC-Utrecht)中,通过两名专家的共识,对位于淋巴结 Ib-II-III-IV-V 层的单个淋巴结进行了 MRI 人工分割,获得了基本真实分割结果。在 40 名患者身上训练了 3D CNN(nnU-Net),并在 10 名患者身上进行了测试。评估指标包括骰子相似系数(DSC)、召回率、精确度和 F1-分数。CNN 的分割结果与两名观察者的分割结果进行了比较。结果nnU-Net对选择性淋巴结进行了自动分割,DSC中位数为0.72,召回率为0.76,精确度为0.1:0.76,精确度:0.78,F1-分数:0.78。与两位观察者相比,CNN 的召回率更高(p = 0.002)。结论 nnU-Net 能够自动分割 MRI 上的单个淋巴结。使用 nnU-Net 的淋巴结检测率高于人工分割。需要重新训练 nnU-Net 才能将网络成功转移到另一家医疗中心。
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来源期刊
Physics and Imaging in Radiation Oncology
Physics and Imaging in Radiation Oncology Physics and Astronomy-Radiation
CiteScore
5.30
自引率
18.90%
发文量
93
审稿时长
6 weeks
期刊最新文献
Results of 2023 survey on the use of synthetic computed tomography for magnetic resonance Imaging-only radiotherapy: Current status and future steps Head and neck automatic multi-organ segmentation on Dual-Energy Computed Tomography Automatic segmentation for magnetic resonance imaging guided individual elective lymph node irradiation in head and neck cancer patients Development of a novel 3D-printed dynamic anthropomorphic thorax phantom for evaluation of four-dimensional computed tomography Technical feasibility of delivering a simultaneous integrated boost in partial breast irradiation
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