论文标题
使用完全卷积冗余模型的脊柱椎间盘标记
Spine intervertebral disc labeling using a fully convolutional redundant counting model
论文作者
论文摘要
标记椎间盘具有相关性，因为它显着使临床医生能够了解患者症状（疼痛，麻痹）与确切的脊髓损伤水平之间的关系。但是，手动标记这些光盘是一项乏味和用户偏见的任务，它将受益于自动化方法。尽管已经存在一些自动化方法用于MRI和CT-SCAN，但它们要么不公开，要么未能在各种成像对比度上概括。在本文中，我们将完全卷积网络（FCN）与成立模块结合在一起，以本地化和标记椎间盘。我们演示了在公共可用的多中心和多对比度MRI数据库中的概念验证应用程序（n = 235个受试者）。该代码可在https://github.com/neuropoly/vertebral-labeling-deep-learning上公开获取。
Labeling intervertebral discs is relevant as it notably enables clinicians to understand the relationship between a patient's symptoms (pain, paralysis) and the exact level of spinal cord injury. However manually labeling those discs is a tedious and user-biased task which would benefit from automated methods. While some automated methods already exist for MRI and CT-scan, they are either not publicly available, or fail to generalize across various imaging contrasts. In this paper we combine a Fully Convolutional Network (FCN) with inception modules to localize and label intervertebral discs. We demonstrate a proof-of-concept application in a publicly-available multi-center and multi-contrast MRI database (n=235 subjects). The code is publicly available at https://github.com/neuropoly/vertebral-labeling-deep-learning.
