IEEE Trans Image Process. 2015 Nov;24(11):4041-54. doi: 10.1109/TIP.2015.2460455. Epub 2015 Jul 23.
Histopathological grading of cancer not only offers an insight to the patients' prognosis but also helps in making individual treatment plans. Mitosis counts in histopathological slides play a crucial role for invasive breast cancer grading using the Nottingham grading system. Pathologists perform this grading by manual examinations of a few thousand images for each patient. Hence, finding the mitotic figures from these images is a tedious job and also prone to observer variability due to variations in the appearances of the mitotic cells. We propose a fast and accurate approach for automatic mitosis detection from histopathological images. We employ area morphological scale space for cell segmentation. The scale space is constructed in a novel manner by restricting the scales with the maximization of relative-entropy between the cells and the background. This results in precise cell segmentation. The segmented cells are classified in mitotic and non-mitotic category using the random forest classifier. Experiments show at least 12% improvement in F1 score on more than 450 histopathological images at 40× magnification.
癌症的组织病理学分级不仅可以深入了解患者的预后，还可以帮助制定个体化治疗计划。有丝分裂计数在组织病理学幻灯片中起着至关重要的作用，可用于使用诺丁汉分级系统对浸润性乳腺癌进行分级。病理学家通过对每个患者的几千张图像进行手动检查来进行这种分级。因此，从这些图像中找到有丝分裂图像是一项繁琐的工作，并且由于有丝分裂细胞的外观存在差异，因此容易出现观察者的变异性。我们提出了一种从组织病理学图像中自动检测有丝分裂的快速而准确的方法。我们采用区域形态学比例空间进行细胞分割。通过限制比例来最大化细胞和背景之间的相对熵，可以以新颖的方式构建比例空间。这导致了精确的细胞分割。使用随机森林分类器将分割后的细胞分类为有丝分裂和非有丝分裂类别。在 40×放大倍数下，对超过 450 张组织病理学图像进行的实验表明，F1 分数至少提高了 12％。
