Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, T6G 1H9, Canada.
Department of Medicine, University of Alberta, Edmonton, Alberta, T6G 2B7, Canada.
Comput Med Imaging Graph. 2021 Apr;89:101893. doi: 10.1016/j.compmedimag.2021.101893. Epub 2021 Mar 5.
The Proliferation Index (PI) is an important diagnostic, predictive and prognostic parameter used for evaluating different types of cancer. This paper presents an automated technique to measure the PI values for skin melanoma images using machine learning algorithms. The proposed technique first analyzes a Mart-1 stained histology image and generates a region of interest (ROI) mask for the tumor. The ROI mask is then used to locate the tumor regions in the corresponding Ki-67 stained image. The nuclei in the Ki-67 ROI are then segmented and classified using a Convolutional Neural Network (CNN), and the PI value is calculated based on the number of the active and the passive nuclei. Experimental results show that the proposed technique can robustly segment (with 94 % accuracy) and classify the nuclei with a low computational complexity and the calculated PI values have less than 4 % average error.
增殖指数（PI）是用于评估不同类型癌症的重要诊断、预测和预后参数。本文提出了一种使用机器学习算法测量皮肤黑色素瘤图像 PI 值的自动化技术。该技术首先分析 Mart-1 染色的组织学图像，并为肿瘤生成感兴趣区域（ROI）掩模。然后，ROI 掩模用于在相应的 Ki-67 染色图像中定位肿瘤区域。然后使用卷积神经网络（CNN）对 Ki-67 ROI 中的核进行分割和分类，并根据活跃核和被动核的数量计算 PI 值。实验结果表明，该技术可以稳健地分割（准确率为 94%）和分类核，具有较低的计算复杂度，并且计算出的 PI 值的平均误差小于 4%。
