论著摘要 |【AI-Radiomics】使用随机森林算法对计算机断层扫描图像的组织分割的可行性研究(双语版)

2018-05-21 19:27:31 admin
标签:   人工智能 影像组学 器官自动分割 CT

Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study.

发表日期: 2017.05.12   来源:Phys Med Biol. 2016 Sep 7;61(17):6553-69.

作者:

Polan DF1, Brady SL, Kaufman RA.

作者介绍:

1. Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, USA. Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis TN, USA.

摘要

诊断性CT需要稳定的、全自动的全身器官分割。这项研究调查并优化自动器官分割的随机森林算法;探索应用于CT环境的随机森林算法的局限性;并在儿科和成人患者的可行性研究中展示分割准确性。就我们所知,这是第一个使用随机森林机器学习作为专门为儿科和成人检查诊断CT环境而开发的全自动组织分割工具的手段,来实现可训练的Weka分割(TWS)的研究。计算机断层扫描(CT)的当前创新主要集中在影像组学,即特定患者的辐射剂量计算,和使用迭代重建的图像质量改进方面,所有这些都需要CT图像中组织和器官系统的特定知识。本研究的目的是开发一种全自动化的随机森林分类器算法,用于分割基于儿科和成人CT方案的颈-胸-腹-盆腔CT检查。使用Matlab和FIJI的TWS插件对七种材料进行分类:背景,肺/内部空气或气体,脂肪,肌肉,实体器官实质,血液/造影增强液和骨组织。对TWS的以下分类器特征滤波器进行了研究:在2 n的体素半径(n从0到4)上评估的最小值,最大值,均值和方差以及降噪和边缘保留滤波器:高斯,双边,桑原,和各向异性扩散。随机森林算法使用了200棵树,每个节点随机选择2个特征。优化的自动分割算法产生了16个图像特征,包括来自最大值,均值,方差高斯和桑原滤波器的特征。分析了来自21个患者图像切片的手动分割和随机森林算法分割图像之间的切片相似性系数(DSC)计算。自动化算法产生七个材料类别的分割,儿童患者方案的中值DSC为0.86±0.03,成人患者方案为0.85±0.04。此外,对100例随机选择的患者检查进行分割和分析,平均敏感度为0.91(范围:0.82-0.98),特异性为0.89(范围:0.70-0.98),准确度为0.90(范围:0.76-0.98)。在这项研究中,我们证明这个完全自动分割工具能够在广泛的患者习惯和扫描参数上产生对身体颈部和躯干的快速准确分割。

Abstact

There is a need for robust, fully automated whole body organ segmentation for diagnostic CT. This study investigates and optimizes a Random Forest algorithm for automated organ segmentation; explores the limitations of a Random Forest algorithm applied to the CT environment; and demonstrates segmentation accuracy in a feasibility study of pediatric and adult patients. To the best of our knowledge, this is the first study to investigate a trainable Weka segmentation (TWS) implementation using Random Forest machine-learning as a means to develop a fully automated tissue segmentation tool developed specifically for pediatric and adult examinations in a diagnostic CT environment. Current innovation in computed tomography (CT) is focused on radiomics, patient-specific radiation dose calculation, and image quality improvement using iterative reconstruction, all of which require specific knowledge of tissue and organ systems within a CT image. The purpose of this study was to develop a fully automated Random Forest classifier algorithm for segmentation of neck-chest-abdomen-pelvis CT examinations based on pediatric and adult CT protocols. Seven materials were classified: background, lung/internal air or gas, fat, muscle, solid organ parenchyma, blood/contrast enhanced fluid, and bone tissue using Matlab and the TWS plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance evaluated over a voxel radius of 2 (n) , (n from 0 to 4), along with noise reduction and edge preserving filters: Gaussian, bilateral, Kuwahara, and anisotropic diffusion. The Random Forest algorithm used 200 trees with 2 features randomly selected per node. The optimized auto-segmentation algorithm resulted in 16 image features including features derived from maximum, mean, variance Gaussian and Kuwahara filters. Dice similarity coefficient (DSC) calculations between manually segmented and Random Forest algorithm segmented images from 21 patient image sections, were analyzed. The automated algorithm produced segmentation of seven material classes with a median DSC of 0.86  ±  0.03 for pediatric patient protocols, and 0.85  ±  0.04 for adult patient protocols. Additionally, 100 randomly selected patient examinations were segmented and analyzed, and a mean sensitivity of 0.91 (range: 0.82-0.98), specificity of 0.89 (range: 0.70-0.98), and accuracy of 0.90 (range: 0.76-0.98) were demonstrated. In this study, we demonstrate that this fully automated segmentation tool was able to produce fast and accurate segmentation of the neck and trunk of the body over a wide range of patient habitus and scan parameters.

阅读原文:PMID: 27530679  PMCID: PMC5039942  DOI: 10.1088/0031-9155/61/17/6553


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