L2M NSERC - AI4Path for the Cancer Marker Ki-67

L2M NSERC - 癌症标记物 Ki-67 的 AI4Path

• 批准号: 580680-2023
• 负责人: Ling, CharlesCX
• 金额: $ 1.46万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Idea to Innovation
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749700
• 关键词: L2M; NSERC; AI4Path; Cancer; Marker

A nuclear protein called Ki-67 has been identified as one of the most important factors that play a role in the prediction and prognostication of cancer-related biopsies. As a consequence of the proliferation of cells, tumors with high Ki-67 indices are more likely to have proliferating cells, which have a greater tendency to grow more rapidly as a result of cell proliferation. When it comes to determining Ki-67, there are many different methods that pathologists use, and the most widely used methods are manual counting and visual estimation (eyeballing), both of which are widely used as methods to determine Ki-67 within a tissue sample. In spite of this, it is a very time consuming and laborious process on the part of the human being to achieve this goal. Our goal is to develop a generalist yet fully automated method of segmenting microscopy Ki-67 images for cell bodies using deep learning techniques, as well as to develop a web portal (AI4Path) that can be accessed from anywhere in the world and be able to be used for further analysis of the captured images. In addition to reducing human effort, AI4Path can also provide a number of benefits to our users, such as being able to handle large datasets with a greater degree of scalability, improving reproducibility, and allowing us to handle large datasets with a higher degree of reproducibility.

一种名为Ki-67的核蛋白已被确定为在癌症相关活检的预测和预后中发挥作用的最重要因素之一。由于细胞的增殖，Ki-67指数高的肿瘤更有可能存在增殖细胞，细胞增殖使细胞生长更迅速。当涉及到确定Ki-67时，病理学家使用许多不同的方法，最广泛使用的方法是人工计数和视觉估计（眼球），这两种方法都被广泛用作确定组织样本中Ki-67的方法。尽管如此，对人类来说，实现这一目标是一个非常耗时和费力的过程。我们的目标是使用深度学习技术开发一种通用但完全自动化的细胞体分割显微镜Ki-67图像的方法，以及开发一个门户网站（AI4Path），可以从世界任何地方访问，并能够用于进一步分析捕获的图像。除了减少人力之外，AI4Path还可以为我们的用户提供许多好处，例如能够以更大程度的可伸缩性处理大型数据集，提高再现性，并允许我们以更高程度的再现性处理大型数据集。