Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA.
Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA.
Neurotoxicol Teratol. 2024 Mar-Apr;102:107336. doi: 10.1016/j.ntt.2024.107336. Epub 2024 Feb 23.
Microglial cells mediate diverse homeostatic, inflammatory, and immune processes during normal development and in response to cytotoxic challenges. During these functional activities, microglial cells undergo distinct numerical and morphological changes in different tissue volumes in both rodent and human brains. However, it remains unclear how these cytostructural changes in microglia correlate with region-specific neurochemical functions. To better understand these relationships, neuroscientists need accurate, reproducible, and efficient methods for quantifying microglial cell number and morphologies in histological sections. To address this deficit, we developed a novel deep learning (DL)-based classification, stereology approach that links the appearance of Iba1 immunostained microglial cells at low magnification (20×) with the total number of cells in the same brain region based on unbiased stereology counts as ground truth. Once DL models are trained, total microglial cell numbers in specific regions of interest can be estimated and treatment groups predicted in a high-throughput manner (<1 min) using only low-power images from test cases, without the need for time and labor-intensive stereology counts or morphology ratings in test cases. Results for this DL-based automatic stereology approach on two datasets (total 39 mouse brains) showed >90% accuracy, 100% percent repeatability (Test-Retest) and 60× greater efficiency than manual stereology (<1 min vs. ∼ 60 min) using the same tissue sections. Ongoing and future work includes use of this DL-based approach to establish clear neurodegeneration profiles in age-related human neurological diseases and related animal models.
小胶质细胞在正常发育和应对细胞毒性挑战过程中介导多种稳态、炎症和免疫过程。在这些功能活动中，小胶质细胞在啮齿动物和人类大脑的不同组织体积中经历不同的数量和形态变化。然而，小胶质细胞的这些细胞结构变化与特定区域的神经化学功能如何相关仍不清楚。为了更好地理解这些关系，神经科学家需要准确、可重复和高效的方法来量化组织学切片中小胶质细胞的数量和形态。为了解决这个问题，我们开发了一种新的基于深度学习（DL）的分类、体视学方法，该方法将 Iba1 免疫染色小胶质细胞在低倍镜（20×）下的外观与同一脑区的细胞总数联系起来，其依据是基于无偏体视学计数的真实值。一旦 DL 模型被训练，特定感兴趣区域的总小胶质细胞数量可以通过仅使用测试用例的低功率图像以高通量方式进行估计和预测治疗组，而无需在测试用例中进行耗时且费力的体视学计数或形态评分。在两个数据集（总共 39 只老鼠的大脑）上，这种基于 DL 的自动体视学方法的结果显示出>90%的准确率、100%的可重复性（测试-再测试）和 60 倍的效率，优于手动体视学（<1 分钟与 ∼60 分钟），同时使用相同的组织切片。正在进行和未来的工作包括使用这种基于 DL 的方法来建立与年龄相关的人类神经疾病和相关动物模型的明确神经退行性变图谱。
