Deep learning and pathomics augmented nephropathology

深度学习和病理组学增强了肾病理学

• 批准号: 459599325
• 负责人: Professor Dr. Peter Boor, Ph.D.
• 金额: --
• 依托单位: Institut für Pathologie
• 依托单位国家: 德国
• 项目类别: Clinical Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/459599325?language=en
• 关键词: Deep; learning; pathomics; augmented; nephropathology

Nephropathology is essential for the diagnosis of renal diseases and a major read-out of experimental pre-clinical kidney studies. The advancement in digital pathology , i.e. digitalization of histological slides, and the application of artificial intelligence, especially deep learning, has opened new research perspectives that have the potential to transform diagnostic pathology into quantitative "computational" pathology. We aim to develop and apply deep learning in both clinical and experimental nephropathology. We have established a state-of-the-art high-throughput digital pathology infrastructure and platform which is further supported by our highly complementary, interdisciplinary expertise and long-term cooperation in this field. We aim to develop deep learning approaches to automatically segment various kidney compartments and perform classification among different domains including various animal species, human samples, stains, and diseases. We will address various challenges and limitations prevalent in digital pathology, including the unavailability of manually annotated data and staining variations, by developing semi- or un-supervised and stain-independent approaches. Additionally, we will perform exhaustive analyses to further our understanding of renal histopathology by the development of pathomics, i.e. large-scale extraction of quantitative image features that might detect previously unrecognized morphological attributes in each kidney compartment for different domains. As a proof-of-concept, we will use our trained networks to generate pathomics data on our experiments studying the role of desmosomes as biomarkers of kidney injury. Besides, we will provide our nephropathology and image analyses expertise for the whole consortium, strongly enhancing the comparability of the data generated while at the same time substantially increasing the datasets for deep learning developments. In conclusion, we aim to develop and apply deep learning and pathomics approaches to facilitate innovative quantitative pathology diagnostics towards a more precise and personalized nephropathology.

肾脏病理学对于肾脏疾病的诊断和实验性临床前肾脏研究的主要结果至关重要。数字病理学的进步，即组织学切片的数字化，以及人工智能，特别是深度学习的应用，开辟了新的研究视角，有可能将诊断病理学转化为定量“计算”病理学。我们的目标是在临床和实验肾脏病理学中开发和应用深度学习。我们已经建立了最先进的高通量数字病理学基础设施和平台，这进一步得到了我们在该领域高度互补的跨学科专业知识和长期合作的支持。我们的目标是开发深度学习方法来自动分割各种肾脏隔室，并在不同的领域（包括各种动物物种、人类样本、染色和疾病）之间进行分类。我们将通过开发半监督或无监督和染色独立的方法来解决数字病理学中普遍存在的各种挑战和限制，包括手动注释数据和染色变化的不可用性。此外，我们将进行详尽的分析，以进一步了解肾脏组织病理学的病理组学的发展，即大规模提取定量图像特征，可能会检测以前未识别的形态属性在每个肾脏隔室的不同领域。作为概念验证，我们将使用经过训练的网络来生成我们研究桥粒作为肾损伤生物标志物的实验的病理组学数据。此外，我们将为整个联盟提供我们的肾脏病理学和图像分析专业知识，大大提高所生成数据的可比性，同时大幅增加深度学习开发的数据集。总之，我们的目标是开发和应用深度学习和病理组学方法，以促进创新的定量病理诊断，实现更精确和个性化的肾脏病理学。