Fanous Michael John, Casteleiro Costa Paloma, Işıl Çağatay, Huang Luzhe, Ozcan Aydogan
Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
Bioengineering Department, University of California, Los Angeles, CA, USA.
Light Sci Appl. 2024 Sep 4;13(1):231. doi: 10.1038/s41377-024-01544-9.
In recent years, the integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging. A compelling trend in this field involves deliberately compromising certain measurement metrics to engineer better bioimaging tools in terms of e.g., cost, speed, and form-factor, followed by compensating for the resulting defects through the utilization of deep learning models trained on a large amount of ideal, superior or alternative data. This strategic approach has found increasing popularity due to its potential to enhance various aspects of biophotonic imaging. One of the primary motivations for employing this strategy is the pursuit of higher temporal resolution or increased imaging speed, critical for capturing fine dynamic biological processes. Additionally, this approach offers the prospect of simplifying hardware requirements and complexities, thereby making advanced imaging standards more accessible in terms of cost and/or size. This article provides an in-depth review of the diverse measurement aspects that researchers intentionally impair in their biophotonic setups, including the point spread function (PSF), signal-to-noise ratio (SNR), sampling density, and pixel resolution. By deliberately compromising these metrics, researchers aim to not only recuperate them through the application of deep learning networks, but also bolster in return other crucial parameters, such as the field of view (FOV), depth of field (DOF), and space-bandwidth product (SBP). Throughout this article, we discuss various biophotonic methods that have successfully employed this strategic approach. These techniques span a wide range of applications and showcase the versatility and effectiveness of deep learning in the context of compromised biophotonic data. Finally, by offering our perspectives on the exciting future possibilities of this rapidly evolving concept, we hope to motivate our readers from various disciplines to explore novel ways of balancing hardware compromises with compensation via artificial intelligence (AI).
近年来，深度学习技术与生物光子学装置的整合为生物成像开辟了新的视野。该领域一个引人注目的趋势是，故意在某些测量指标上做出妥协，以便在成本、速度和外形尺寸等方面设计出更好的生物成像工具，然后通过利用在大量理想、优质或替代数据上训练的深度学习模型来补偿由此产生的缺陷。这种策略性方法因其在增强生物光子成像各个方面的潜力而越来越受欢迎。采用这种策略的主要动机之一是追求更高的时间分辨率或更快的成像速度，这对于捕捉精细的动态生物过程至关重要。此外，这种方法有望简化硬件要求和复杂性，从而在成本和/或尺寸方面使先进的成像标准更容易实现。本文深入综述了研究人员在其生物光子学装置中有意损害的各种测量方面，包括点扩散函数（PSF）、信噪比（SNR）、采样密度和像素分辨率。通过故意在这些指标上做出妥协，研究人员不仅旨在通过应用深度学习网络来恢复这些指标，还旨在反过来增强其他关键参数，如视野（FOV）、景深（DOF）和空间带宽积（SBP）。在本文中，我们讨论了成功采用这种策略性方法的各种生物光子学方法。这些技术涵盖了广泛的应用，并展示了深度学习在受损生物光子数据背景下的多功能性和有效性。最后，通过阐述我们对这一快速发展概念令人兴奋的未来可能性的看法，我们希望激励来自各个学科的读者探索通过人工智能（AI）在硬件妥协与补偿之间取得平衡的新方法。
