PDF(Pubmed)
Abstract：
This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands of cancerous tissue samples from microarray slides of gliomas. These samples were then classified according to the guidelines developed by the World Health Organization, which define the subtypes and grades of diffuse gliomas. We explored a hyperspectral feature extraction model called SMLMER-ResNet using microscopic hyperspectral images of brain gliomas of different malignancy grades. The model combines the channel attention mechanism and multi-scale image features to automatically learn the pathological organization of gliomas and obtain hierarchical feature representations, effectively removing the interference of redundant information. It also completes multi-modal, multi-scale spatial-spectral feature extraction to improve the automatic classification of glioma subtypes. The proposed classification method demonstrated high average classification accuracy (>97.3%) and a Kappa coefficient (0.954), indicating its effectiveness in improving the automatic classification of hyperspectral gliomas. The method is readily applicable in a wide range of clinical settings, offering valuable assistance in alleviating the workload of clinical pathologists. Furthermore, the study contributes to the development of more personalized and refined treatment plans, as well as subsequent follow-up and treatment adjustment, by providing physicians with insights into the underlying pathological organization of gliomas.
摘要：
这项研究描述了一种对胶质瘤病理切片进行分级的新方法。我们自己的集成高光谱成像系统用于表征来自神经胶质瘤微阵列载玻片的270条带癌组织样本。然后根据世界卫生组织制定的指南对这些样本进行分类，定义了弥漫性神经胶质瘤的亚型和等级。我们使用不同恶性等级的脑胶质瘤的显微高光谱图像探索了一种称为SMLMER-ResNet的高光谱特征提取模型。该模型结合通道注意机制和多尺度图像特征，自动学习胶质瘤的病理组织，获得分层特征表示，有效去除冗余信息的干扰。它还完成了多模态，多尺度空间谱特征提取提高胶质瘤亚型的自动分类。所提出的分类方法具有较高的平均分类精度（>97.3％）和Kappa系数（0.954），表明其在提高高光谱胶质瘤自动分类方面的有效性。该方法很容易适用于广泛的临床环境。为减轻临床病理学家的工作量提供宝贵的帮助。此外，这项研究有助于制定更个性化和更精细的治疗计划，以及随后的随访和治疗调整，通过为医生提供对神经胶质瘤潜在病理组织的见解。
