Spotting L3 slice in CT scans using deep convolutional network and transfer learning
Spotting L3 slice in CT scans using deep convolutional network and transfer learning
复制标题
DOI：
10.1016/j.compbiomed.2017.05.018
复制
发表时间：
2017-08-01
影响因子：
7.7
通讯作者：
Modzelewski, Romain
中科院分区：
文献类型：
--
作者：
Belharbi, Soufiane;Chatelain, Clement;Modzelewski, Romain
在这篇文章中，我们提出了一个完整的自动化系统，用于在完整的3D计算机断层扫描检查（CT扫描）中定位特定切片。我们的方法不需要对扫描覆盖患者身体的哪一部分进行任何假设。它依赖于原始的机器学习回归方法。我们的模型是使用迁移学习技巧通过利用在imageNet数据库上预先训练的深度架构来学习的，因此它需要很少的注释来进行训练。整个流程包括三个步骤：i）将CT扫描转换为最大强度投影（MIP）图像，ii）以滑动窗口方式应用卷积神经网络（CNN）对MIP图像进行预测，以及iii）对预测序列进行稳健分析，以预测整个CT扫描中所需切片的高度。我们的方法被应用到检测的第三腰椎（L3）切片已被发现是代表全身组成。我们的系统在我们的临床中心收集的数据库上进行评估，该数据库包含来自不同患者的642个CT扫描。我们获得了1.91 +/- 2.69切片（小于5 mm）的平均定位误差，平均时间小于2.5 s/CT扫描，允许将所提出的系统集成到日常临床常规中。
In this article, we present a complete automated system for spotting a particular slice in a complete 3D Computed Tomography exam (CT scan). Our approach does not require any assumptions on which part of the patient's body is covered by the scan. It relies on an original machine learning regression approach. Our models are learned using the transfer learning trick by exploiting deep architectures that have been pre-trained on imageNet database, and therefore it requires very little annotation for its training. The whole pipeline consists of three steps: i) conversion of the CT scans into Maximum Intensity Projection (MIP) images, ii) prediction from a Convolutional Neural Network (CNN) applied in a sliding window fashion over the MIP image, and iii) robust analysis of the prediction sequence to predict the height of the desired slice within the whole CT scan. Our approach is applied to the detection of the third lumbar vertebra (L3) slice that has been found to be representative to the whole body composition. Our system is evaluated on a database collected in our clinical center, containing 642 CT scans from different patients. We obtained an average localization error of 1.91 +/- 2.69 slices (less than 5 mm) in an average time of less than 2.5 s/CT scan, allowing integration of the proposed system into daily clinical routines.