Computer Aided Medical Procedures, Technische Universität München, Germany.
Computer Aided Medical Procedures, Technische Universität München, Germany; Institute of Computational Biology, Helmholtz Zentrum München, Germany.
Med Image Anal. 2017 Jan;35:655-668. doi: 10.1016/j.media.2016.09.009. Epub 2016 Oct 5.
The examination of biopsy samples plays a central role in the diagnosis and staging of numerous diseases, including most cancer types. However, because of the large size of the acquired images, the localization and quantification of diseased portions of a tissue is usually time-consuming, as pathologists must scroll through the whole slide to look for objects of interest which are often only scarcely distributed. In this work, we introduce an approach to facilitate the visual inspection of large digital histopathological slides. Our method builds on a random forest classifier trained to segment the structures sought by the pathologist. However, moving beyond the pixelwise segmentation task, our main contribution is an interactive exploration framework including: (i) a region scoring function which is used to rank and sequentially display regions of interest to the user, and (ii) a relevance feedback capability which leverages human annotations collected on each suggested region. Thereby, an online domain adaptation of the learned pixelwise segmentation model is performed, so that the region scores adapt on-the-fly to possible discrepancies between the original training data and the slide at hand. Three real-time update strategies are compared, including a novel approach based on online gradient descent which supports faster user interaction than an accurate delineation of objects. Our method is evaluated on the task of extramedullary hematopoiesis quantification within mouse liver slides. We assess quantitatively the retrieval abilities of our approach and the benefit of the interactive adaptation scheme. Moreover, we demonstrate the possibility of extrapolating, after a partial exploration of the slide, the surface covered by hematopoietic cells within the whole tissue.
活检样本的检查在许多疾病的诊断和分期中起着核心作用，包括大多数癌症类型。然而，由于获取的图像较大，病理学家通常需要滚动整个幻灯片才能找到感兴趣的对象，而这些对象通常分布稀疏，因此定位和量化组织中的病变部分通常很耗时。在这项工作中，我们引入了一种方法来简化对大型数字组织病理学幻灯片的视觉检查。我们的方法基于随机森林分类器，该分类器经过训练可对病理学家寻求的结构进行分割。然而，除了像素级分割任务之外，我们的主要贡献是一个交互式探索框架，包括：（i）区域评分函数，用于对感兴趣的区域进行排名和顺序显示；（ii）相关性反馈功能，利用在每个建议区域上收集的人工注释。从而，对学习的像素级分割模型进行在线域自适应，以便区域得分实时适应原始训练数据和当前幻灯片之间可能存在的差异。比较了三种实时更新策略，包括一种基于在线梯度下降的新方法，该方法比准确描绘对象支持更快的用户交互。我们的方法在小鼠肝幻灯片中骨髓外造血量化任务上进行了评估。我们定量评估了我们的方法的检索能力和交互式自适应方案的好处。此外，我们证明了在部分探索幻灯片后，推断整个组织内造血细胞覆盖表面的可能性。
