Colton Center for Autoimmunity, NYU Grossman School of Medicine, New York, NY, United States of America.
Center for Data Science, New York University, New York, NY, United States of America.
J Immunol Methods. 2022 Jun;505:113233. doi: 10.1016/j.jim.2022.113233. Epub 2022 Feb 4.
Biopsies of inflammatory tissue contain a complex network of interacting cells, orchestrating the immune or autoimmune response. While standard histological examination can identify relationships, it is clear that a great amount of data on each slide is not quantitated or categorized in standard microscopic examinations. To deal with the huge amount of data present in biopsy tissue in an unbiased and comprehensive way, we have developed a deep learning algorithm to identify immune cells in biopsies of inflammatory lesions. We focused on T follicular helper (Tfh) cell subsets and B cells in dermatomyositis biopsy images. We achieved strong performance on detection and classification of cells, including the rare Tfh cell subsets present in the tissue. This algorithm could be used to perform distance mapping between cell types in tissue, and could be easily adapted to other disease states.
炎症组织的活检包含一个相互作用的细胞复杂网络，协调免疫或自身免疫反应。虽然标准的组织学检查可以识别这些关系，但很明显，标准显微镜检查并未对每张载玻片上的大量数据进行定量或分类。为了以无偏和全面的方式处理活检组织中存在的大量数据，我们开发了一种深度学习算法来识别炎症病变活检中的免疫细胞。我们专注于特发性炎症性肌病活检图像中的滤泡辅助性 T 细胞 (Tfh) 亚群和 B 细胞。我们在细胞的检测和分类方面取得了出色的性能，包括组织中存在的罕见 Tfh 细胞亚群。该算法可用于在组织中的细胞类型之间进行距离映射，并且可以轻松适应其他疾病状态。
