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首页> 外文期刊>Frontiers in Bioengineering and Biotechnology >Glandular Segmentation of Prostate Cancer: An Illustration of how the Choice of Histopathological Stain is one Key to Success for Computational Pathology
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Glandular Segmentation of Prostate Cancer: An Illustration of how the Choice of Histopathological Stain is one Key to Success for Computational Pathology

机译：前列腺癌的腺节段：组织病理学染色选择如何成为计算病理学成功的关键的例证

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Digital pathology offers the potential for computer-aided diagnosis, significantly reducing the pathologists’ workload and paving the way for accurate prognostication with reduced inter-and intra-observer variations. But successful computer-based analysis requires careful tissue preparation and image acquisition to keep color and intensity variations to a minimum. While the human eye may recognize prostate glands with significant color and intensity variations, a computer algorithm may fail under such conditions. Since malignancy grading of prostate tissue according to Gleason or to the International Society of Urological Pathology (ISUP) grading system is based on architectural growth patterns of prostatic carcinoma, automatic methods must rely on accurate identification of the prostate glands. But due to poor color differentiation between stroma and epithelium from the common stain hematoxylin-eosin, no method is yet able to segment all types of glands, making automatic prognostication hard to attain. We address the effect of tissue preparation on glandular segmentation with an alternative stain, Picrosirius red and hematoxylin, which clearly delineates the stromal boundaries, and couple this stain with a color decomposition that removes intensity variation. In this manner we propose a segmentation algorithm that uses image analysis techniques based on mathematical morphology and can successfully determine the glandular boundaries. Accurate determination of the stromal and glandular morphology enables the identification of the architectural pattern that determine the malignancy grade and classify each gland into its appropriate Gleason grade or ISUP Grade Group. Segmentation of prostate tissue with the new stain and decomposition method has been successfully tested on more than 11000 objects including well-formed glands (Gleason grade 3), cribriform and fine caliber glands (grade 4), and single cells (grade 5) glands.
机译：数字病理学为计算机辅助诊断提供了潜力，大大减少了病理学家的工作量，并为减少观察者之间和观察者之间的差异提供了准确的预测方法。但是成功的基于计算机的分析需要仔细的组织准备和图像采集，以将颜色和强度变化保持在最低水平。虽然人眼可以识别出颜色和强度明显不同的前列腺，但在这种情况下，计算机算法可能会失败。由于根据格里森（Gleason）或国际泌尿外科病理学协会（ISUP）分级的前列腺组织恶性程度是基于前列腺癌的结构生长模式，因此自动方法必须依靠对前列腺的准确识别。但是，由于基质和上皮细胞与普通苏木精-曙红之间的颜色区分差，因此尚无方法能够分割所有类型的腺体，从而难以实现自动预后。我们用另一种染色剂Picrosirius红和苏木精处理组织准备对腺体分割的影响，该染色剂清楚地描绘了基质边界，并将该染色剂与颜色分解相结合，消除了强度变化。通过这种方式，我们提出了一种分割算法，该算法使用基于数学形态学的图像分析技术，并且可以成功确定腺体边界。基质和腺形态的准确确定可以确定确定恶性等级并将每个腺分类为合适的格里森等级或ISUP等级组的建筑模式。使用新的染色和分解方法对前列腺组织进行分割的方法已成功测试了11000多个对象，其中包括形态良好的腺体（格里森3级），筛状和细口径腺体（4级）以及单细胞腺体（5级）。

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