Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK.
Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK.
Artif Intell Med. 2022 Sep;131:102365. doi: 10.1016/j.artmed.2022.102365. Epub 2022 Jul 14.
The segmentation of magnetic resonance (MR) images is a crucial task for creating pseudo computed tomography (CT) images which are used to achieve positron emission tomography (PET) attenuation correction. One of the main challenges of creating pseudo CT images is the difficulty to obtain an accurate segmentation of the bone tissue in brain MR images. Deep convolutional neural networks (CNNs) have been widely and efficiently applied to perform MR image segmentation. The aim of this work is to propose a segmentation approach that combines multiresolution handcrafted features with CNN-based features to add directional properties and enrich the set of features to perform segmentation. The main objective is to efficiently segment the brain into three tissue classes: bone, soft tissue, and air. The proposed method combines non subsampled Contourlet (NSCT) and non subsampled Shearlet (NSST) coefficients with CNN's features using different mechanisms. The entropy value is calculated to select the most useful coefficients and reduce the input's dimensionality. The segmentation results are evaluated using fifty clinical brain MR and CT images by calculating the precision, recall, dice similarity coefficient (DSC), and Jaccard similarity coefficient (JSC). The results are also compared to other methods reported in the literature. The DSC of the bone class is improved from 0.6179 ± 0.0006 to 0.6416 ± 0.0006. The addition of multiresolution features of NSCT and NSST with CNN's features demonstrates promising results. Moreover, NSST coefficients provide more useful information than NSCT coefficients.
磁共振（MR）图像的分割是创建用于实现正电子发射断层扫描（PET）衰减校正的伪 CT 图像的关键任务。创建伪 CT 图像的主要挑战之一是难以准确分割脑 MR 图像中的骨组织。深度卷积神经网络（CNN）已广泛有效地应用于进行 MR 图像分割。本工作旨在提出一种分割方法，该方法将多分辨率手工制作的特征与基于 CNN 的特征相结合，以添加方向特性并丰富执行分割的特征集。主要目标是有效地将大脑分割为三个组织类别：骨、软组织和空气。所提出的方法使用不同的机制将非下采样轮廓波（NSCT）和非下采样剪切波（NSST）系数与 CNN 的特征相结合。通过计算熵值来选择最有用的系数并降低输入的维度。通过计算精度、召回率、骰子相似系数（DSC）和 Jaccard 相似系数（JSC），使用五十个临床脑 MR 和 CT 图像评估分割结果。还将结果与文献中报道的其他方法进行了比较。骨类的 DSC 从 0.6179 ± 0.0006 提高到 0.6416 ± 0.0006。使用 CNN 的特征添加 NSCT 和 NSST 的多分辨率特征显示出有前途的结果。此外，NSST 系数比 NSCT 系数提供了更多有用的信息。
