Schüffler Peter J, Fuchs Thomas J, Ong Cheng Soon, Wild Peter J, Rupp Niels J, Buhmann Joachim M
Institute for Computational Science, ETH Zurich, Switzerland ; Competence Center for Systems Physiology and Metabolic Diseases, ETH Zurich, Switzerland.
J Pathol Inform. 2013 Mar 30;4(Suppl):S2. doi: 10.4103/2153-3539.109804. Print 2013.
Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable.
We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated.
Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification.
We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.
组织学组织分析通常涉及癌细胞的手动细胞计数和染色评估。这些评估极其耗时、主观性强且容易出错，因为免疫组织化学染色的癌组织通常在细胞大小、形态结构和染色质量方面表现出高度变异性。为便于临床实践以及癌症研究中的可重复分析，客观的计算机辅助染色评估非常必要。
我们采用机器学习算法，如随机决策树和支持向量机进行细胞核检测和分类。通过超像素对组织图像进行分割，将其分为前景和背景，然后根据用户反馈分为恶性和良性。作为一种无需细胞核分类的快速替代方法，纳入了现有的颜色反卷积方法。
我们的程序TMARKER将计算病理学和免疫组织化学组织评分的现有工作流程与机器学习和计算机视觉中的现代主动学习算法相结合。在人类肾透明细胞癌和前列腺癌的测试数据集上，所用算法在细胞核检测和分类方面的性能与两位独立病理学家相当。
我们提出了一种新颖、免费且独立于操作系统的软件包，用于计算细胞计数和染色评估，支持临床和研究中的免疫组织化学染色组织分析。用于类似任务的专有工具箱价格昂贵，绑定到特定的商业硬件（如显微镜），并且在性能和可重复性方面大多未进行定量验证。我们相信所提出的软件包将对科学界证明有价值，并且由于有可能为新的图像类型交互式学习模型，我们预计其应用领域会更广泛。
