• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Society of Photo-Optical Instrumentation Engineers;SPIE Medical Imaging Conference >Localization and Labeling of Cervical Vertebral Bones in the Micro-CT Images of Rabbit Fetuses Using a 3D Deep Convolutional Neural Network
【24h】

Localization and Labeling of Cervical Vertebral Bones in the Micro-CT Images of Rabbit Fetuses Using a 3D Deep Convolutional Neural Network

机译：使用3D深度卷积神经网络的兔胎儿微CT图像中颈椎骨的定位和标记

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In developmental and reproductive toxicology (DART) studies, high-throughput micro-CT imaging of Dutch-Beltedrabbit fetuses has been used as a method for the assessment of compound-induced skeletal abnormalities. Sinceperforming visual inspection of the micro-CT images by the DART scientists is a time- and resource-intensive task, anautomatic strategy was proposed to localize, segment out, label, and evaluate each bone on the skeleton in a testingenvironment. However, due to the lack of robustness in this bone localization approach, failures on localizing certainbones on the critical path while traversing the skeleton, e.g., the cervical vertebral bones, could lead to localization errorsfor other bones downstream. Herein an approach based on deep convolutional neural networks (CNN) is proposed toautomatically localize each cervical vertebral bone represented by its center. For each center, a 3D probability map withGaussian decay is computed with the center itself being the maximum. From cervical vertebrae C1 to C7, the 7 volumesof distance transforms are stacked in order to form a 4-dimensional array. The deep CNN with a 3D U-Net architectureis used to estimate the probability maps for vertebral bone centers from the CT images as the input. A post-processingscheme is then applied to find all the regions with positive response, eliminate the false ones using a point-basedregistration method, and provide the locations and labels for the 7 cervical vertebral bones. Experiments were carried outon a dataset of 345 rabbit fetus micro-CT volumes. The images were randomly divided into training/validation/testingsets at an 80/10/10 ratio. Results demonstrated a 94.3% success rate for localization and labeling on the testing dataset of35 images, and for all the successful cases the average bone-by-bone localization error was at 0.84 voxel.
机译：在发育和生殖毒理学（DART）研究中，Dutch-Belted的高通量显微CT成像 兔胎儿已被用作评估化合物诱发的骨骼异常的方法。自从 DART科学家对微CT图像进行视觉检查是一项耗时和资源密集的任务， 提出了一种自动策略来对测试中的骨骼上的每个骨骼进行定位，分割，标记和评估 环境。但是，由于这种骨骼定位方法缺乏鲁棒性，因此某些骨骼的定位失败。 穿过骨骼的关键路径上的骨骼（例如颈椎骨骼）可能会导致定位错误 对于下游的其他骨骼。本文提出了一种基于深度卷积神经网络（CNN）的方法来 自动定位其中心所代表的每个颈椎骨。对于每个中心，具有 高斯衰减以中心本身为最大值进行计算。从颈椎C1到C7，共7卷 距离变换的堆栈被堆叠以形成4维阵列。具有3D U-Net架构的深层CNN 用于从CT图像作为输入来估计椎骨中心的概率图。后处理 然后应用该方案找到所有具有正响应的区域，使用基于点的点消除虚假区域 登记方法，并提供7个颈椎骨骼的位置和标签。进行了实验 在345个兔胎儿微型CT量的数据集上。图像被随机分为训练/验证/测试 设置为80/10/10的比例。结果显示，在测试数据集上进行本地化和标记的成功率为94.3％ 35张图片，对于所有成功的病例，平均每个骨骼的定位误差为0.84体素。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号