数字病理学图像的核结构量化跨癌症使用基于运输的形态学
Mohammad Shifat-E-Rabbi1, Natasha Ironside2,3, Naqib Sad Pathan2,4
1Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.
概括
一个新的基于运输的形态学 (TBM) 框架从图像中量化核染色质结构. 这种方法可以在各种癌症类型中区分良性和恶性瘤,在癌症研究中推进定量核形态学.
科学领域:
- 计算病理学计算病理学
- 定量生物学的定量生物学.
- 癌症成像分析分析 癌症成像分析
背景情况:
- 核形态对于癌症的诊断和分级至关重要.
- 机器学习和大型数据集为从核图像中提取见解提供了新的途径.
- 现有的方法可能无法完全捕捉到核结构的信息内容.
研究的目的:
- 引入一种新的基于运输的形态测量 (TBM) 框架来建模核染色体结构.
- 通过各种数据集和癌症类型来证明TBM框架的稳定性和可解释性.
- 建立TBM作为用于比较癌症研究的定量工具.
主要方法:
- 开发了一个使用最佳运输数学来建模核信息内容的TBM框架.
- 每个核的信息内容与模板核相对表示.
- 将该方法应用于各种癌症成像数据,包括肝脏,甲状腺,肺部和皮肤瘤.
主要成果:
- TBM模型有效地捕获了核染色体结构信息.
- 该框架对染色和成像协议的变化具有强大耐用性.
- 在多种癌症类型中,有能力区分良性和恶性核特征.
结论:
- TBM框架为核形态学提供了一种定量方法.
- 这种方法可以在不同的数据集和癌症类型之间进行有意义的比较.
- TBM有可能提高癌症研究,技术和临床应用.
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