Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy
Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy
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DOI：
10.1186/s13014-020-01528-0
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发表时间：
2020-05-11
影响因子：
3.6
通讯作者：
T. van den Berg, Cornelis A.
中科院分区：
文献类型：
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作者：
Savenije, Mark H. F.;Maspero, Matteo;T. van den Berg, Cornelis A.
背景结构描绘在放射治疗中是必要的，但耗时的人工程序。最近，卷积神经网络被提出用来加速和自动化这一过程，并取得了令人满意的结果。随着磁共振成像(MRI)引导放射治疗的出现，基于MR的分割变得越来越重要。然而，大多数研究都是基于计算机断层扫描(CT)的自动轮廓绘制。目的探讨基于深度学习的MRI自动划桨技术在临床应用的可行性。材料与方法150例确诊为前列腺癌的患者接受单纯MR放射治疗。用3TMRI采集三维(3D)T1加权双重破坏梯度回声序列，用于合成CT的生成。最初的48名患者被纳入可行性研究，训练两个名为DeepMedic和密集V-Net(DV-Net)的3D卷积网络来分割膀胱、直肠和股骨。为了进行比较，考虑了一个基于atlas的软件的研究版本。根据临床描述计算骰子相似系数、95%Hausdorff距离(HD95)和平均距离。对于8名患者，专家RTT对所有三种方法的轮廓质量进行了评分。在三种方法中进行了选择，并对97名患者进行了选择，并实施了在临床工作流程中自动使用的方法。对于连续的53例患者，根据临床使用的轮廓计算Dice、HD95和平均距离。结果DeepMedic、DV-Net和Atlas软件分别在60 S、4 S和10~15分钟内生成等高线。与基于atlas的软件相比，这两个网络的性能都更高。定性分析表明，DeepMedic的描述需要较少的调整，其次是DV-Net和基于atlas的软件。DeepMedic已在临床上实施。经过对DeepMedic的再培训和对后续患者的测试，性能略有改善。结论使用两个内部训练的网络可获得较高的划线符合率，显著加快划线速度。对不同的方法进行了比较，导致在临床工作流程中成功地采用了其中一种神经网络DeepMedic。DeepMedic在临床环境中保持了可行性研究中获得的准确性。
Background Structure delineation is a necessary, yet time-consuming manual procedure in radiotherapy. Recently, convolutional neural networks have been proposed to speed-up and automatise this procedure, obtaining promising results. With the advent of magnetic resonance imaging (MRI)-guided radiotherapy, MR-based segmentation is becoming increasingly relevant. However, the majority of the studies investigated automatic contouring based on computed tomography (CT). Purpose In this study, we investigate the feasibility of clinical use of deep learning-based automatic OARs delineation on MRI. Materials and methods We included 150 patients diagnosed with prostate cancer who underwent MR-only radiotherapy. A three-dimensional (3D) T1-weighted dual spoiled gradient-recalled echo sequence was acquired with 3T MRI for the generation of the synthetic-CT. The first 48 patients were included in a feasibility study training two 3D convolutional networks called DeepMedic and dense V-net (dV-net) to segment bladder, rectum and femurs. A research version of an atlas-based software was considered for comparison. Dice similarity coefficient, 95% Hausdorff distances (HD95), and mean distances were calculated against clinical delineations. For eight patients, an expert RTT scored the quality of the contouring for all the three methods. A choice among the three approaches was made, and the chosen approach was retrained on 97 patients and implemented for automatic use in the clinical workflow. For the successive 53 patients, Dice, HD95 and mean distances were calculated against the clinically used delineations. Results DeepMedic, dV-net and the atlas-based software generated contours in 60 s, 4 s and 10-15 min, respectively. Performances were higher for both the networks compared to the atlas-based software. The qualitative analysis demonstrated that delineation from DeepMedic required fewer adaptations, followed by dV-net and the atlas-based software. DeepMedic was clinically implemented. After retraining DeepMedic and testing on the successive patients, the performances slightly improved. Conclusion High conformality for OARs delineation was achieved with two in-house trained networks, obtaining a significant speed-up of the delineation procedure. Comparison of different approaches has been performed leading to the succesful adoption of one of the neural networks, DeepMedic, in the clinical workflow. DeepMedic maintained in a clinical setting the accuracy obtained in the feasibility study.