James E, Caetano A J, Sharpe P T
Centre for Oral Immunobiology and Regenerative Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
Centre for Craniofacial and Regenerative Biology, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK.
J Dent Res. 2024 Dec;103(13):1340-1348. doi: 10.1177/00220345241265048. Epub 2024 Sep 13.
Observation is at the center of all biological sciences. Advances in imaging technologies are therefore essential to derive novel biological insights to better understand the complex workings of living systems. Recent high-throughput sequencing and imaging techniques are allowing researchers to simultaneously address complex molecular variations spatially and temporarily in tissues and organs. The availability of increasingly large dataset sizes has allowed for the evolution of robust deep learning models, designed to interrogate biomedical imaging data. These models are emerging as transformative tools in diagnostic medicine. Combined, these advances allow for dynamic, quantitative, and predictive observations of entire organisms and tissues. Here, we address 3 main tasks of bioimage analysis, image restoration, segmentation, and tracking and discuss new computational tools allowing for 3-dimensional spatial genomics maps. Finally, we demonstrate how these advances have been applied in studies of craniofacial development and oral disease pathogenesis.
观察是所有生物科学的核心。因此，成像技术的进步对于获得新的生物学见解以更好地理解生命系统的复杂运作至关重要。最近的高通量测序和成像技术使研究人员能够在组织和器官中同时在空间和时间上解决复杂的分子变异问题。越来越大的数据集规模使得强大的深度学习模型得以发展，这些模型旨在研究生物医学成像数据。这些模型正在成为诊断医学中的变革性工具。综合起来，这些进展使得对整个生物体和组织进行动态、定量和预测性观察成为可能。在这里，我们讨论生物图像分析的三个主要任务，即图像恢复、分割和跟踪，并讨论允许生成三维空间基因组图谱的新计算工具。最后，我们展示了这些进展如何应用于颅面发育和口腔疾病发病机制的研究。
