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首页> 外文会议>Conference on Medical Imaging 2008: Computer-Aided Diagnosis; 20080219-21; San Diego,CA(US) >Computer-aided prognosis of neuroblastoma: Classification of stromal development on whole-slide images
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Computer-aided prognosis of neuroblastoma: Classification of stromal development on whole-slide images

机译：神经母细胞瘤的计算机辅助预后：全幻灯片图像上基质发育的分类

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摘要

Neuroblastoma is a cancer of the nervous system and one of the most common tumors in children. In clinical practice, pathologists examine the haematoxylin and eosin (H&E) stained tissue slides under the microscope for the diagnosis. According to the International Neuroblastoma Classification System, neuroblastoma tumors are categorized into favorable and unfavorable histologies. The subsequent treatment planning is based on this classification. However, this qualitative evaluation is time consuming, prone to error and subject to inter- and intra-reader variations and sampling bias. To overcome these shortcomings, we are developing a computerized system for the quantitative analysis of neuroblastoma slides. In this study, we present a novel image analysis system to determine the degree of stromal development from digitized whole-slide neuroblastoma samples. The developed method uses a multi-resolution approach that works similar to how pathologists examine slides. Due to their very large resolutions, the whole-slide images are divided into non-overlapping image tiles and the proposed image analysis steps are applied to each image tile using a parallel computation infrastructure developed earlier by our group. The computerized system classifies image tiles as stroma-poor or stroma-rich subtypes using texture characteristics. The developed method has been independently tested on 20 whole-slide neuroblastoma slides and it has achieved 95% classification accuracy.
机译：神经母细胞瘤是神经系统的癌症，是儿童中最常见的肿瘤之一。在临床实践中，病理学家会在显微镜下检查苏木精和曙红（H＆E）染色的组织玻片，以进行诊断。根据国际神经母细胞瘤分类系统，神经母细胞瘤肿瘤分为有利和不利的组织学。随后的治疗计划基于此分类。但是，这种定性评估非常耗时，容易出错，而且阅读器之间和阅读器内部会发生变化，并且存在采样偏差。为了克服这些缺点，我们正在开发一种用于定量分析成神经细胞瘤玻片的计算机系统。在这项研究中，我们提出了一种新颖的图像分析系统，用于确定数字化的全滑动神经母细胞瘤样品的基质发育程度。所开发的方法使用了多分辨率方法，其工作原理类似于病理学家如何检查载玻片。由于它们的分辨率非常高，因此将整个幻灯片图像划分为不重叠的图像块，并使用我们小组先前开发的并行计算基础结构将建议的图像分析步骤应用于每个图像块。该计算机化系统使用纹理特征将图像图块分类为贫基质或富基质子类型。所开发的方法已在20个全滑膜神经母细胞瘤玻片上进行了独立测试，并且已达到95％的分类准确率。

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