Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno University of Technology, Brno, Technicka 3082/12, 616 00, Czechia.
Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno University of Technology, Brno, Technicka 3082/12, 616 00, Czechia.
Med Image Anal. 2018 Oct;49:76-88. doi: 10.1016/j.media.2018.07.008. Epub 2018 Aug 3.
This paper aims to address the segmentation and classification of lytic and sclerotic metastatic lesions that are difficult to define by using spinal 3D Computed Tomography (CT) images obtained from highly pathologically affected cases. As the lesions are ill-defined and consequently it is difficult to find relevant image features that would enable detection and classification of lesions by classical methods of texture and shape analysis, the problem is solved by automatic feature extraction provided by a deep Convolutional Neural Network (CNN). Our main contributions are: (i) individual CNN architecture, and pre-processing steps that are dependent on a patient data and a scan protocol - it enables work with different types of CT scans; (ii) medial axis transform (MAT) post-processing for shape simplification of segmented lesion candidates with Random Forest (RF) based meta-analysis; and (iii) usability of the proposed method on whole-spine CTs (cervical, thoracic, lumbar), which is not treated in other published methods (they work with thoracolumbar segments of spine only). Our proposed method has been tested on our own dataset annotated by two mutually independent radiologists and has been compared to other published methods. This work is part of the ongoing complex project dealing with spine analysis and spine lesion longitudinal studies.
本文旨在解决利用高度病变病例获得的脊柱三维计算机断层扫描（CT）图像难以定义的溶骨性和硬化性转移性病变的分割和分类问题。由于病变定义不明确，因此很难找到相关的图像特征，从而无法通过纹理和形状分析的经典方法来检测和分类病变，因此通过深度卷积神经网络（CNN）提供的自动特征提取来解决该问题。我们的主要贡献是：（i）依赖于患者数据和扫描协议的个体 CNN 架构和预处理步骤-它能够处理不同类型的 CT 扫描；（ii）基于随机森林（RF）的基于中轴线变换（MAT）的后处理，用于简化基于 RF 的元分析的分割病变候选者的形状；以及（iii）所提出的方法在整个脊柱 CT（颈椎，胸椎，腰椎）上的可用性，这在其他已发表的方法中未得到处理（它们仅用于胸腰椎段的脊柱）。我们的方法已在由两位相互独立的放射科医生注释的自有数据集上进行了测试，并与其他已发表的方法进行了比较。这项工作是正在进行的脊柱分析和脊柱病变纵向研究的复杂项目的一部分。
