Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
Med Phys. 2020 Feb;47(2):530-540. doi: 10.1002/mp.13933. Epub 2019 Dec 3.
Accurate segmentation of the prostate on computed tomography (CT) for treatment planning is challenging due to CT's poor soft tissue contrast. Magnetic resonance imaging (MRI) has been used to aid prostate delineation, but its final accuracy is limited by MRI-CT registration errors. We developed a deep attention-based segmentation strategy on CT-based synthetic MRI (sMRI) to deal with the CT prostate delineation challenge without MRI acquisition.
We developed a prostate segmentation strategy which employs an sMRI-aided deep attention network to accurately segment the prostate on CT. Our method consists of three major steps. First, a cycle generative adversarial network was used to estimate an sMRI from CT images. Second, a deep attention fully convolution network was trained based on sMRI and the prostate contours deformed from MRIs. Attention models were introduced to pay more attention to prostate boundary. The prostate contour for a query patient was obtained by feeding the patient's CT images into the trained sMRI generation model and segmentation model.
The segmentation technique was validated with a clinical study of 49 patients by leave-one-out experiments and validated with an additional 50 patients by hold-out test. The Dice similarity coefficient, Hausdorff distance, and mean surface distance indices between our segmented and deformed MRI-defined prostate manual contours were 0.92 ± 0.09, 4.38 ± 4.66, and 0.62 ± 0.89 mm, respectively, with leave-one-out experiments, and were 0.91 ± 0.07, 4.57 ± 3.03, and 0.62 ± 0.65 mm, respectively, with hold-out test.
We have proposed a novel CT-only prostate segmentation strategy using CT-based sMRI, and validated its accuracy against the prostate contours that were manually drawn on MRI images and deformed to CT images. This technique could provide accurate prostate volume for treatment planning without requiring MRI acquisition, greatly facilitating the routine clinical workflow.
由于 CT 软组织对比度差，因此对 CT 进行精确的前列腺分割对于治疗计划具有挑战性。磁共振成像（MRI）已用于辅助前列腺描绘，但由于 MRI-CT 配准误差，其最终准确性受到限制。我们开发了一种基于 CT 合成磁共振成像（sMRI）的深度注意分割策略，无需进行 MRI 采集即可解决 CT 前列腺描绘挑战。
我们开发了一种前列腺分割策略，该策略采用 sMRI 辅助深度注意网络在 CT 上准确分割前列腺。我们的方法包括三个主要步骤。首先，使用循环生成对抗网络从 CT 图像估算 sMRI。其次，基于 sMRI 和从 MRI 变形的前列腺轮廓训练深度注意全卷积网络。注意力模型被引入以更关注前列腺边界。通过将患者的 CT 图像输入到训练有素的 sMRI 生成模型和分割模型中，为查询患者获得前列腺轮廓。
通过 49 例患者的留一法实验和 50 例患者的保留法实验对分割技术进行了验证。通过留一法实验，我们分割和变形的 MRI 定义的前列腺手动轮廓之间的 Dice 相似系数，Hausdorff 距离和平均表面距离指数分别为 0.92±0.09，4.38±4.66 和 0.62±0.89mm，而通过保留法实验，这些指数分别为 0.91±0.07，4.57±3.03 和 0.62±0.65mm。
我们提出了一种新颖的基于 CT 的 sMRI 的仅 CT 前列腺分割策略，并根据手动绘制在 MRI 图像上并变形到 CT 图像上的前列腺轮廓验证了其准确性。该技术可以在不进行 MRI 采集的情况下为治疗计划提供准确的前列腺体积，从而极大地促进了常规临床工作流程。
