Pelvic multi-organ segmentation on cone-beam CT for prostate adaptive radiotherapy
Pelvic multi-organ segmentation on cone-beam CT for prostate adaptive radiotherapy
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DOI：
10.1002/mp.14196
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发表时间：
2020-05-11
期刊：
影响因子：
3.8
通讯作者：
Yang, Xiaofeng
中科院分区：
文献类型：
--
作者：
Fu, Yabo;Lei, Yang;Yang, Xiaofeng
背景和目的本研究的目的是开发一种基于深度学习的方法，以同时分割五个盆腔器官，包括前列腺，膀胱，直肠，左和右股骨头在锥形束CT（CBCT）上，作为前列腺自适应放疗planning.Materials和方法所需的元素，我们建议利用CBCT和基于CBCT的合成MRI（sMRI）进行软组织和骨结构的分割，因为它们为盆腔器官分割提供了补充信息。CBCT图像具有上级骨结构对比度，sMRI具有上级软组织对比度。在分割之前，使用循环一致性对抗网络（CycleGAN）生成sMRI，该网络使用配对的CBCT-MR图像进行训练。为了将CBCT和sMRI的优势联合收割机结合起来，我们开发了一种具有后期特征融合的跨模态注意力金字塔网络。我们的方法分别处理CBCT和sMRI输入，以提取CBCT特异性和sMRI特异性特征，然后将它们组合在后期融合网络中进行最终分割。该网络使用100个患者的数据集进行训练和测试，每个数据集包括CBCT和手动医生轮廓。为了进行比较，我们训练了另外两个具有不同网络输入和架构的网络。结果该方法的分割结果与地面真实值之间的骰子相似系数和平均表面距离分别为0.96 ± 0.03，0.65 ± 0.67 mm; 0.91 ± 0.08，0.93 ± 0.96 mm;膀胱、前列腺、直肠、左右股骨头分别为0.93 +/- 0.04、0.72 +/- 0.61 mm; 0.95 +/-0.05、1.05 +/- 1.40 mm; 0.95 +/- 0.05、1.08 +/- 1.48 mm。与其他两种竞争方法相比，我们的方法在分割精度方面表现出上级性能。结论我们开发了一种基于深度学习的分割方法，可以快速准确地从日常CBCT中同时分割五个盆腔器官。该方法可用于临床，以支持前列腺自适应放射治疗的快速目标和危险器官轮廓。
Background and purpose The purpose of this study is to develop a deep learning-based approach to simultaneously segment five pelvic organs including prostate, bladder, rectum, left and right femoral heads on cone-beam CT (CBCT), as required elements for prostate adaptive radiotherapy planning.Materials and methods We propose to utilize both CBCT and CBCT-based synthetic MRI (sMRI) for the segmentation of soft tissue and bony structures, as they provide complementary information for pelvic organ segmentation. CBCT images have superior bony structure contrast and sMRIs have superior soft tissue contrast. Prior to segmentation, sMRI was generated using a cycle-consistent adversarial networks (CycleGAN), which was trained using paired CBCT-MR images. To combine the advantages of both CBCT and sMRI, we developed a cross-modality attention pyramid network with late feature fusion. Our method processes CBCT and sMRI inputs separately to extract CBCT-specific and sMRI-specific features prior to combining them in a late-fusion network for final segmentation. The network was trained and tested using 100 patients' datasets, with each dataset including the CBCT and manual physician contours. For comparison, we trained another two networks with different network inputs and architectures. The segmentation results were compared to manual contours for evaluations.Results For the proposed method, dice similarity coefficients and mean surface distances between the segmentation results and the ground truth were 0.96 +/- 0.03, 0.65 +/- 0.67 mm; 0.91 +/- 0.08, 0.93 +/- 0.96 mm; 0.93 +/- 0.04, 0.72 +/- 0.61 mm; 0.95 +/- 0.05, 1.05 +/- 1.40 mm; and 0.95 +/- 0.05, 1.08 +/- 1.48 mm for bladder, prostate, rectum, left and right femoral heads, respectively. As compared to the other two competing methods, our method has shown superior performance in terms of the segmentation accuracy.Conclusion We developed a deep learning-based segmentation method to rapidly and accurately segment five pelvic organs simultaneously from daily CBCTs. The proposed method could be used in the clinic to support rapid target and organs-at-risk contouring for prostate adaptive radiation therapy.