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Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology
IEEE Transactions on Visualization and Computer Graphics ( IF 5.2 ) Pub Date : 2020-04-27 , DOI: 10.1109/tvcg.2020.2990336
A. Corvo , H. S. Garcia Caballero , M. A. Westenberg , M. A. van Driel , J. J. van Wijk

Recent advances in computational and algorithmic power are evolving the field of medical imaging rapidly. In cancer research, many new directions are sought to characterize patients with additional imaging features derived from radiology and pathology images. The emerging field of Computational Pathology targets the high-throughput extraction and analysis of the spatial distribution of cells from digital histopathology images. The associated morphological and architectural features allow researchers to quantify and characterize new imaging biomarkers for cancer diagnosis, prognosis, and treatment decisions. However, while the image feature space grows, exploration and analysis become more difficult and ineffective. There is a need for dedicated interfaces for interactive data manipulation and visual analysis of computational pathology and clinical data. For this purpose, we present IIComPath , a visual analytics approach that enables clinical researchers to formulate hypotheses and create computational pathology pipelines involving cohort construction, spatial analysis of image-derived features, and cohort analysis. We demonstrate our approach through use cases that investigate the prognostic value of current diagnostic features and new computational pathology biomarkers.

中文翻译:

计算病理学中假设驱动探索的可视化分析

计算和算法能力的最新进展正在迅速发展医学成像领域。在癌症研究中,许多新方向被寻求用来自放射学和病理学图像的附加成像特征来表征患者。计算病理学的新兴领域的目标是从数字组织病理学图像中高通量提取和分析细胞的空间分布。相关的形态学和结构特征使研究人员能够量化和表征用于癌症诊断、预后和治疗决策的新成像生物标志物。然而,随着图像特征空间的增长,探索和分析变得更加困难和无效。需要用于交互式数据操作和计算病理学和临床数据的可视化分析的专用接口。为此,我们提出IIComPath 是一种可视化分析方法,使临床研究人员能够制定假设并创建涉及队列构建、图像衍生特征的空间分析和队列分析的计算病理学管道。我们通过使用案例来展示我们的方法,这些案例研究了当前诊断特征和新的计算病理生物标志物的预后价值。
更新日期:2020-04-27
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