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.

中文翻译：

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颅面发育和疾病图像分析的计算方法


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同时解决组织和器官中空间和暂时的复杂分子变化。数据集规模的不断扩大使得强大的深度学习模型得以发展，这些模型旨在询问生物医学成像数据。这些模型正在成为诊断医学的变革性工具。结合起来，这些进步允许对整个生物体和组织进行动态、定量和预测性观察。在这里，我们解决了生物图像分析、图像恢复、分割和跟踪 3 个主要任务，并讨论了允许 3 维空间基因组图的新计算工具。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。