Histological image classification using biologically interpretable shape-based features.
Histological image classification using biologically interpretable shape-based features.
复制标题
DOI：
10.1186/1471-2342-13-9
复制
发表时间：
2013-03-13
影响因子：
2.7
通讯作者：
Wang MD
中科院分区：
文献类型：
--
作者：
Kothari S;Phan JH;Young AN;Wang MD
基于组织学图像分类的自动癌症诊断系统对于改善治疗决策是重要的。以前的研究提出了这样的系统的纹理和形态特征。这些特征捕获组织学图像中的模式，这些模式对于癌症分级和亚型都是有用的。然而，由于这些特征中的许多缺乏明确的生物学解释，病理学家可能不愿意采用这些特征进行临床诊断。我们研究了生物学可解释的形状为基础的功能分类的组织学肾肿瘤图像的效用。使用傅立叶形状描述符，我们提取基于形状的功能，捕捉染色增强的细胞和组织结构在每个图像中的分布，并使用多类预测模型评估这些功能。我们比较了基于形状的诊断模型与传统模型的预测性能，即，使用纹理、形态和拓扑特征。基于形状的模型平均准确率为77%，优于或补充了传统模型。我们从顶部选择的特征中识别出每个肾肿瘤亚型的最具信息量的形状。结果表明，这些形状不仅是准确的诊断特征，而且与已知的生物学特性的肾肿瘤。组织学肾肿瘤图像的基于形状的分析准确地将疾病亚型分类，并揭示生物学上有洞察力的区分特征。这种基于形状的分析方法可以扩展到其他组织学数据集，以帮助病理学家做出诊断和治疗决策。
Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions.