肝细胞癌的数字化病理诊断关键技术

数字化病理诊断技术将依赖医生的定性或半定量病理诊断模式转变为基于图像处理与识别的定量化病理诊断模式，为病理学诊断带来了划时代的变革。本项目将难以早期诊断的肝细胞癌病理诊断与国际前沿的数字化病理诊断技术相结合，基于肝组织的全视野数字切片（WSI），研究和开发肝细胞癌的数字化病理诊断关键技术，具有重要的科学价值和实用价值。 本项目针对肝细胞癌数字化病理诊断的各项难题，基于计算解剖学、数字图像处理、机器学习、模式识别、病理学等多学科理论，提出一系列新方法，重点研究细胞与黏连细胞的分割、肝细胞的生长状态与脂肪化程度的计算机表示、肝细胞癌病理特征定量化与有效特征选择、高泛化能力的肝细胞癌分类模型构建等关键问题，发现正常细胞和肿瘤细胞的差异，揭示潜在的癌变规律，形成我国具有自主知识产权的高性能肝细胞癌数字化病理诊断关键技术，提高我国肝细胞癌病理诊断的精度和效率，丰富和发展数字化病理诊断理论。

Digital pathological diagnosis technology made the doctor's qualitative or semi-quantitative pathology model into a quantitative model of pathological diagnosis based on image processing and recognition, it brought epoch-making reform for pathological diagnosis.The project will combine pathologic diagnosis of hepatocellular carcinoma, which is difficult to be early diagnosis, with the international emerging digital pathology technology, and investigate and develope the key technologies of digital pathology on hepatocellular carcinoma based on whole slide imaging (WSI),This project has important scientific and practical value. Focused on the challenges of digital pathology diagnosis of hepatocellular carcinoma, the project intends to make full use of the multi-disciplinary theory of computing anatomy, digital image processing, machine learning, pattern recognition, pathology, put forward a series of new methods, investigate the segmentation problem of cells and adhesion between cells, the computer representation problem of the growth state and the pimelosis level of liver cells, the quantification problem of HCC pathological characteristics and the selection problem of effective feature, the construction problem of HCC classification model with high generalization ability, and find the differences between normal cells and tumor cells to reveal the potential regularity of cancerization. and construct a high-performance key technologies of digital pathology diagnosis for hepatocellular carcinoma with independent intellectual property rights in our country, and improve the accuracy and efficiency of HCC pathological diagnosis, enrich and develop the theory and method of digital pathologic diagnosis.