...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Medical Imaging >ψ-Net: Stacking Densely Convolutional LSTMs for Sub-Cortical Brain Structure Segmentation
【24h】

ψ-Net: Stacking Densely Convolutional LSTMs for Sub-Cortical Brain Structure Segmentation

机译：ψ网：堆叠密集卷积的LSTMS，用于子皮质脑结构细分

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Sub-cortical brain structure segmentation is of great importance for diagnosing neuropsychiatric disorders. However, developing an automatic approach to segmenting sub-cortical brain structures remains very challenging due to the ambiguous boundaries, complex anatomical structures, and large variance of shapes. This paper presents a novel deep network architecture, namely Psi-Net, for sub-cortical brain structure segmentation, aiming at selectively aggregating features and boosting the information propagation in a deep convolutional neural network (CNN). To achieve this, we first formulate a densely convolutional LSTM module (DC-LSTM) to selectively aggregate the convolutional features with the same spatial resolution at the same stage of a CNN. This helps to promote the discriminativeness of features at each CNN stage. Second, we stack multiple DC-LSTMs from the deepest stage to the shallowest stage to progressively enrich low-level feature maps with high-level context. We employ two benchmark datasets on sub-cortical brain structure segmentation, and perform various experiments to evaluate the proposed Psi-Net. The experimental results show that our network performs favorably against the state-of-the-art methods on both benchmark datasets.
机译：亚皮质脑结构细分对于诊断神经精神障碍具有重要意义。然而，由于模糊的边界，复杂的解剖结构和形状的大方差，开发分割子皮质脑结构的自动方法仍然非常具有挑战性。本文提出了一种新的深度网络架构，即Psi-Net，用于子皮质脑结构分割，旨在选择性地聚合特征并提高深度卷积神经网络（CNN）中的信息传播。为了实现这一点，我们首先装配密集的卷积LSTM模块（DC-LSTM），以在CNN的同一阶段选择性地聚合具有相同空间分辨率的卷积特征。这有助于促进每个CNN阶段的特征的歧视。其次，我们将多个DC-LSTM从最深阶段堆叠到最浅的阶段，以逐步丰富具有高级别上下文的低级特征映射。我们采用两个基准数据集在子皮质大脑结构分割上，并进行各种实验来评估所提出的PSI-Net。实验结果表明，我们的网络对两个基准数据集的最先进的方法有利地表现了有利的方法。

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