Avenel Christophe, Tolf Anna, Dragomir Anca, Carlbom Ingrid B
CADESS Medical AB, Uppsala, Sweden.
Department of Pathology, Uppsala University Hospital, Uppsala, Sweden.
Front Bioeng Biotechnol. 2019 Jul 5;7:125. doi: 10.3389/fbioe.2019.00125. eCollection 2019.
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-hematoxylin, which clearly delineates the stromal boundaries, and couple this stain with a color decomposition that removes intensity variation. In this paper we propose a segmentation algorithm that uses image analysis techniques based on mathematical morphology and that 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.
数字病理学为计算机辅助诊断提供了可能，显著减轻了病理学家的工作量，并为准确的预后评估铺平了道路，减少了观察者间和观察者内的差异。但基于计算机的成功分析需要精心的组织制备和图像采集，以使颜色和强度变化降至最低。虽然人眼可能能够识别颜色和强度有显著变化的前列腺腺体，但计算机算法在这种情况下可能会失败。由于根据Gleason或国际泌尿病理学会（ISUP）分级系统对前列腺组织进行恶性分级是基于前列腺癌的结构生长模式，自动方法必须依赖于前列腺腺体的准确识别。但由于苏木精-伊红常规染色中基质和上皮之间的颜色差异不佳，尚无方法能够分割所有类型的腺体，使得自动预后评估难以实现。我们使用一种替代染色剂苦味酸天狼星红-苏木精来研究组织制备对腺体分割的影响，该染色剂能清晰地勾勒出基质边界，并将这种染色与消除强度变化的颜色分解相结合。在本文中，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，能够成功地确定腺体边界。准确确定基质和腺体形态能够识别决定恶性分级的结构模式，并将每个腺体分类到其相应的Gleason分级或ISUP分级组。使用新的染色剂和分解方法对前列腺组织进行分割已在超过11000个对象上成功测试，这些对象包括形态良好的腺体（Gleason 3级）、筛状和细口径腺体（4级）以及单细胞（5级）腺体。
