Recurrent convolutional neural networks for mandible segmentation from computed tomography
University Medical Center Groningen（格罗宁根大学医学中心）；University of Groningen（格罗宁根大学）；Department of Oral and Maxillofacial Surgery（口腔颌面外科）；Medical Imaging Center (MIC)（医学影像中心）；D Lab（3D实验室）
Bingjiang Qiu, Jiapan Guo, Joep Kraeima, Haye H. Glas, Ronald J. H. Borra, Max J. H. Witjes, Peter M. A. van Ooijen
Recently, accurate mandible segmentation in CT scans based on deep learning methods has attracted much attention. However, there still exist two major challenges, namely, metal artifacts among mandibles and large variations in shape or size among individuals. To address these two challenges, we propose a recurrent segmentation convolutional neural network (RSegCNN) that embeds segmentation convolutional neural network (SegCNN) into the recurrent neural network (RNN) for robust and accurate segmentation of the mandible. Such a design of the system takes into account the similarity and continuity of the mandible shapes captured in adjacent image slices in CT scans. The RSegCNN infers the mandible information based on the recurrent structure with the embedded encoder-decoder segmentation (SegCNN) components. The recurrent structure guides the system to exploit relevant and important information from adjacent slices, while the SegCNN component focuses on the mandible shapes from a single CT slice. We conducted extensive experiments to evaluate the proposed RSegCNN on two head and neck CT datasets. The experimental results show that the RSegCNN is significantly better than the state-of-the-art models for accurate mandible segmentation.
Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy
The University of Tokyo（东京大学）；National Institute for Quantum and Radiological Science and Technology（国立量子与放射科学技术研究所）
在肺癌患者的放射治疗期间，需要最小化递送到肿瘤周围的健康组织的辐射，由于呼吸运动和线性加速器系统的延迟，这是困难的。在这项研究中，我们首先使用Lucas-Kanade金字塔光流算法进行变形图像配准的胸部计算机断层扫描图像的四个肺癌患者。然后，我们根据先前计算的变形场跟踪靠近肺肿瘤的三个内部点，并使用使用实时递归学习（RTRL）和梯度裁剪训练的递归神经网络（RNN）预测它们的位置。呼吸数据是相当规则的，以大约2.5Hz采样，并且包括脊柱方向上的人工漂移。追踪点的运动幅度范围为12.0 mm ~ 22.7 mm。最后，我们提出了一种简单的方法，用于恢复和预测三维肿瘤图像的跟踪点和初始肿瘤图像的基础上的线性对应模型和Nadaraya-Watson非线性回归。测试集上RNN预测对应的均方根误差，最大误差和抖动小于线性预测和最小均方（LMS）获得的相同性能指标。特别地，与RNN相关联的最大预测误差等于1.51mm，分别比与线性预测和LMS相关联的最大误差低16.1%和5.0%。RTRL的每个时间步长的平均预测时间等于119 ms，这小于400 ms标记位置采样时间。预测图像中的肿瘤位置在视觉上看起来是正确的，这通过原始图像和预测图像之间的高平均互相关（等于0.955）来确认。
