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STRAINet: Spatially Varying sTochastic Residual AdversarIal Networks for MRI Pelvic Organ Segmentation

机译:ryrate:用于MRI骨盆器官分割的空间变化随机残留对抗网络

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Accurate segmentation of pelvic organs is important for prostate radiation therapy. Modern radiation therapy starts to use a magnetic resonance image (MRI) as an alternative to computed tomography image because of its superior soft tissue contrast and also free of risk from radiation exposure. However, segmentation of pelvic organs from MRI is a challenging problem due to inconsistent organ appearance across patients and also large intrapatient anatomical variations across treatment days. To address such challenges, we propose a novel deep network architecture, called "Spatially varying sTochastic Residual AdversarIal Network" (STRAINet), to delineate pelvic organs from MRI in an end-to-end fashion. Compared to the traditional fully convolutional networks (FCN), the proposed architecture has two main contributions: 1) inspired by the recent success of residual learning, we propose an evolutionary version of the residual unit, i.e., stochastic residual unit, and use it to the plain convolutional layers in the FCN. We further propose long-range stochastic residual connections to pass features from shallow layers to deep layers; and 2) we propose to integrate three previously proposed network strategies to form a new network for better medical image segmentation: a) we apply dilated convolution in the smallest resolution feature maps, so that we can gain a larger receptive field without overly losing spatial information; b) we propose a spatially varying convolutional layer that adapts convolutional filters to different regions of interest; and c) an adversarial network is proposed to further correct the segmented organ structures. Finally, STRAINet is used to iteratively refine the segmentation probability maps in an autocontext manner. Experimental results show that our STRAINet achieved the state-of-the-art segmentation accuracy. Further analysis also indicates that our proposed network components contribute most to the performance.
机译:骨盆器官的精确细分对于前列腺放射治疗是重要的。现代放射治疗开始使用磁共振图像(MRI)作为计算断层摄影图像的替代方案,因为其卓越的软组织对比,也没有辐射暴露的风险。然而,由于患者对患者的外表不一致,并且在治疗时的含量较大的内裤解剖学变化,骨盆器官的分割是一个具有挑战性的问题。为了解决这些挑战,我们提出了一种新颖的深度网络架构,称为“空间不同随机残留对抗网络”(rsient),以以端到端的方式从MRI描绘骨盆器官。与传统的全卷积网络(FCN)相比,拟议的建筑有两个主要贡献:1)灵感来自最近的剩余学习成功,我们提出了一种剩余单元的进化版本,即随机剩余单位,并用它FCN中的普通卷积层。我们进一步提出了远程随机残留连接,通过浅层到深层的功能; 2)我们建议将三种先前提出的网络策略集成,以形成更好的医学图像分割的新网络:a)我们在最小的分辨率中映射应用扩张卷积,以便我们可以在没有过度丢失空间信息的情况下获得更大的接收领域; b)我们提出了一种空间不同的卷积层,使卷积滤波器适应不同的感兴趣区域; c)提出了对抗性网络以进一步校正分段器官结构。最后,使用过滤器以自动图文方式迭代地细化分割概率图。实验结果表明,我们的rsient实现了最先进的分割精度。进一步的分析还表明,我们所提出的网络组件对性能最为贡献。

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