Karthik R, Radhakrishnan Menaka, Rajalakshmi R, Raymann Joel
Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India.
School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, Chennai, India.
Biomed Eng Lett. 2020 Nov 5;11(1):3-13. doi: 10.1007/s13534-020-00178-1. eCollection 2021 Feb.
Precise delineation of the ischemic lesion from unimodal Magnetic Resonance Imaging (MRI) is a challenging task due to the subtle intensity difference between the lesion and normal tissues. Hence, multispectral MRI modalities are used for characterizing the properties of brain tissues. Traditional lesion detection methods rely on extracting significant hand-engineered features to differentiate normal and abnormal brain tissues. But the identification of those discriminating features is quite complex, as the degree of differentiation varies according to each modality. This can be addressed well by Convolutional Neural Networks (CNN) which supports automatic feature extraction. It is capable of learning the global features from images effectively for image classification. But it loses the context of local information among the pixels that need to be retained for segmentation. Also, it must provide more emphasis on the features of the lesion region for precise reconstruction. The major contribution of this work is the integration of attention mechanism with a Fully Convolutional Network (FCN) to segment ischemic lesion. This attention model is applied to learn and concentrate only on salient features of the lesion region by suppressing the details of other regions. Hence the proposed FCN with attention mechanism was able to segment ischemic lesion of varying size and shape. To study the effectiveness of attention mechanism, various experiments were carried out on ISLES 2015 dataset and a mean dice coefficient of 0.7535 was obtained. Experimental results indicate that there is an improvement of 5% compared to the existing works.
由于病变组织与正常组织之间的强度差异细微，通过单模态磁共振成像（MRI）精确描绘缺血性病变是一项具有挑战性的任务。因此，多光谱MRI模态被用于表征脑组织的特性。传统的病变检测方法依赖于提取显著的手工设计特征来区分正常和异常脑组织。但是，由于每种模态的分化程度不同，识别这些区分性特征相当复杂。卷积神经网络（CNN）支持自动特征提取，可以很好地解决这个问题。它能够有效地从图像中学习全局特征以进行图像分类。但它丢失了分割所需保留的像素之间的局部信息上下文。此外，为了精确重建，它必须更加强调病变区域的特征。这项工作的主要贡献是将注意力机制与全卷积网络（FCN）集成以分割缺血性病变。这种注意力模型通过抑制其他区域的细节，应用于仅学习和关注病变区域的显著特征。因此，所提出的带有注意力机制的FCN能够分割不同大小和形状的缺血性病变。为了研究注意力机制的有效性，在ISLES 2015数据集上进行了各种实验，获得了0.7535的平均骰子系数。实验结果表明，与现有工作相比有5%的提升。
