论著摘要 |【AI-CT】利用卷积神经网络对头颈部CT图像中的危及器官的分割(双语版)

2018-01-22 14:06:15 admin 0
标签:   人工智能 CT 分割 头颈部 卷积神经网络 CNN 放疗

Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

发表日期: 2017.02.19   来源:Med Phys. 2017 Feb;44(2):547-557.

作者:

Bulat Ibragimov1, Lei Xing1.

作者介绍:

1. Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, 94305, USA.

摘要

Abstact

目的

精确分割危及器官(OARs)是有效规划头颈部(HaN)癌症治疗中的放射治疗有效方案制定的关键步骤。在这项工作中,我们提出了第一个基于深度学习的算法,用于在HaN CT图像中分割OAR,并将其性能与最先进的自动分割算法,商业软件和观察者间变异性进行比较。

Purpose

Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs in HaN CT images, and compared its performance against state-of-the-art automated segmentation algorithms, commercial software, and interobserver variability.

方法

卷积神经网络(CNN)是深度学习领域的一个概念,用来研究来自训练组CT图像的OAR的一致强度模式,并在以前看不见的测试组CT图像中分割OAR。对于CNN训练组,我们在训练组CT图像提取了有着代表性数量的感兴趣的OAR的体素周围的的正强度斑块,以及围绕周围结构的体素的负强度斑块。这些修补程序通过一系列的CNN层,捕捉局部图像特征,如角点,端点和边缘,并将它们组合成更复杂的高阶特征,可以有效地描述OAR。训练过的网络被用于在相应的OAR被预期定位的测试组图像中感兴趣的区域中对体素进行分类。然后我们使用马尔科夫随机场算法来平滑获得的分类结果。我们最终提取了被CNN分类为OAR的平滑体素中最大的连通分量,进行扩张腐蚀操作以去除导致测试图像中的OAR分割的组分的空洞。

Methods

Convolutional neural networks (CNNs)-a concept from the field of deep learning-were used to study consistent intensity patterns of OARs from training CT images and to segment the OAR in a previously unseen test CT image. For CNN training, we extracted a representative number of positive intensity patches around voxels that belong to the OAR of interest in training CT images, and negative intensity patches around voxels that belong to the surrounding structures. These patches then passed through a sequence of CNN layers that captured local image features such as corners, end-points, and edges, and combined them into more complex high-order features that can efficiently describe the OAR. The trained network was applied to classify voxels in a region of interest in the test image where the corresponding OAR is expected to be located. We then smoothed the obtained classification results by using Markov random fields algorithm. We finally extracted the largest connected component of the smoothed voxels classified as the OAR by CNN, performed dilate-erode operations to remove cavities of the component, which resulted in segmentation of the OAR in the test image.

结果

使用50幅CT图像对CNNs的表现进行了验证,包括脊髓,下颌骨,腮腺,颌下腺,喉,咽,眼球,视神经和视交叉等方面的分割。所获得的分割结果在视交叉处的Dice系数(DSC)为37.4%,而下颌骨的DSC 为89.5%。我们还分析了在文献中报道的最新算法和商业软件的性能,并且观察到CNN在脊髓,下颌骨,腮腺,喉,咽,眼球和视神经等的分割方面表现出类似或更优越的性能,但下颌下腺和视交叉的分割性能较差。

Results

The performance of CNNs was validated on segmentation of spinal cord, mandible, parotid glands, submandibular glands, larynx, pharynx, eye globes, optic nerves, and optic chiasm using 50 CT images. The obtained segmentation results varied from 37.4% Dice coefficient (DSC) for chiasm to 89.5% DSC for mandible. We also analyzed the performance of state-of-the-art algorithms and commercial software reported in the literature, and observed that CNNs demonstrate similar or superior performance on segmentation of spinal cord, mandible, parotid glands, larynx, pharynx, eye globes, and optic nerves, but inferior performance on segmentation of submandibular glands and optic chiasm.

结论

我们得到,卷积神经网络在含有50个HaN CT图像的代表性数据库中,可以准确地分割大部分OARs。同时,如MR图像中包含附加信息,可能对一些界限不清晰的OAR有益。

Conclusions

We concluded that convolution neural networks can accurately segment most of OARs using a representative database of 50 HaN CT images. At the same time, inclusion of additional information, for example, MR images, may be beneficial to some OARs with poorly visible boundaries.

关键词:

卷积神经网络;深度学习;头颈部;放疗;分割

Keywords:

convolutional neural networks; deep learning; head and neck; radiotherapy; segmentation

阅读原文:PMID: 28205307  PMCID: PMC5383420 [Available on 2018-02-01]  DOI: 10.1002/mp.12045


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