• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>SPIE Medical Imaging Conference >Deep learning for medical image segmentation - using the IBM TrueNorth Neurosynaptic System
【24h】

Deep learning for medical image segmentation - using the IBM TrueNorth Neurosynaptic System

机译：深度学习医学图像分割 - 使用IBM Truentorth Neurosynaptic系统

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

摘要

Deep convolutional neural networks have found success in semantic image segmentation tasks in computer vision and medical imaging. These algorithms are executed on conventional von Neumann processor architectures or GPUs. This is suboptimal. Xeuromorphic processors that replicate the structure of the brain are better-suited to train and execute deep learning models for image segmentation by relying on massively-parallel processing. However, given that they closely emulate the human brain, on-chip hardware and digital memory limitations also constrain them. Adapting deep learning models to execute image segmentation tasks on such chips, requires specialized training and validation.In this work, we demonstrate for the first-time, spinal image segmentation performed using a deep learning network implemented on neuromorphic hardware of the IBM TrueNorth Neurosynaptic System and validate the performance of our network by comparing it to human-generated segmentations of spinal vertebrae and disks. To achieve this on neuromorphic hardware, the training model constrains the coefficients of individual neurons to {-1,0,1} using the Energy Efficient Deep Neuromorphic (EEDN) networks training algorithm. Given the ~1 million neurons and 256 million synapses, the scale and size of the neural network implemented by the IBM TrueNorth allows us to execute the requisite mapping between segmented images and non-uniform intensity MR images >20 times faster than on a GPU-accelerated network and using <0.1 W. This speed and efficiency implies that a trained neuromorphic chip can be deployed in intra-operative environments where real-time medical image segmentation is necessary.
机译：深度卷积神经网络在计算机视觉和医学成像中发现了在语义图像分割任务中的成功。这些算法在传统的von Neumann处理器架构或GPU上执行。这是次优。通过依赖于大规模平行的处理，复制大脑结构的雪核处理器更适合培训和执行图像分割的深度学习模型。然而，鉴于它们与人类大脑紧密地模拟，片上硬件和数字记忆局限也限制了它们。适应深度学习模型在此类芯片上执行图像分割任务，需要专门的培训和验证。在这项工作中，我们向第一次执行了使用IBM Truenthe NeuroSnapt系统的神经胸壁硬件中实现的深层学习网络进行的第一次进行的脊髓图像分割。并通过将其与脊髓椎骨和磁盘的人生成的分割进行比较来验证我们的网络性能。为了在神经形态硬件上实现这一点，训练模型使用节能深神经形状（EEDN）网络训练算法将个体神经元的系数约束为{-1,0,1}。鉴于〜100万神经元和25600万个突触，由IBM Truenorph实现的神经网络的规模和大小允许我们在分段图像和非均匀强度MR图像之间执行必要的映射，而不是GPU速度快20倍。加速网络和使用<0.1W。这种速度和效率意味着可以在需要实时医学图像分割的帧内环境中部署培训的神经形状芯片。

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