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Interactive Segmentation Relabeling for Classification of Whole-Slide Histopathology Imagery

机译:交互式分割重新标记,用于全幻灯片组织病理学图像分类

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Collecting ground-truth or gold standard annotations from expert pathologists for developing histopathology analytic algorithms and computer-aided diagnosis for cancer grading is an expensive and time consuming process. Efficient visualization and annotation tools are needed to enable ground-truthing large whole-slide imagery. KOLAM is our scalable, cross-platform framework for interactive visualization of 2D, 2D+t and 3D imagery of high spatial, temporal and spectral resolution. In the current work KOLAM has been extended to support rapid interactive labelling and correction of automatic image classifier-based region labels of the tissue microenvironment by pathologists. Besides annotating regions-of-interest (ROIs), KOLAM enables extraction of the corresponding large polygonal image subregions for input into automatic segmentation algorithms, single-click region label reassignment and maintaining hierarchical image subregions. Experience indicates that clinicians prefer simple-to-use interfaces that support rapid labelling of large image regions with minimal effort. The incorporation of easy-to-use tissue annotation features in KOLAM makes it an attractive candidate for integration within a multi-stage histopathology image analysis pipeline supporting assisted segmentation and labelling to improve whole-slide imagery (WSI) analytics.
机译:从专家病理学家那里收集真相或金标准注释,以开发组织病理学分析算法和癌症分级的计算机辅助诊断是一个昂贵且耗时的过程。需要有效的可视化和注释工具来实现地面真实的大型全幻灯片图像。 KOLAM是我们的可扩展跨平台框架,用于交互式可视化具有高空间,时间和光谱分辨率的2D,2D + t和3D图像。在当前工作中,KOLAM已扩展为支持病理学家对组织微环境中基于图像分类器的自动区域标签进行快速交互式标记和校正。除了注释感兴趣区域(ROI),KOLAM还可以提取相应的大多边形图像子区域,以输入自动分割算法,单击区域标签重新分配并维护分层图像子区域。经验表明,临床医生更喜欢简单易用的界面,该界面支持以最小的努力对大图像区域进行快速标记。在KOLAM中结合了易于使用的组织注释功能,使其成为集成在多阶段组织病理学图像分析管道中的有吸引力的候选者,该管道支持辅助的分割和标记,以改善整个幻灯片图像(WSI)的分析。

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