MICROSCOPY & IMAGING
FIG. 3. Microscopes for high-content screening, such as Olympus’ scanR, benefit considerably from deep-learning-based image analysis
Deep learning gives researchers more options for quantifying images
easier sample preparation, faster imaging and improved cell viability, making it well suited to long-term live cell imaging studies. Yet brightfield microscopy presents inherent image analysis and segmentation challenges and is constrained by low contrast and poorer image quality compared to fluorescence microscopy. As such, its capacity for label-free analysis is relatively limited using conventional methods.
HOW DOES DEEP LEARNING ADDRESS THESE CHALLENGES? Deep learning represents an ideal solution to these microscopy challenges. Its algorithms can rapidly learn to predict multiple parameters autonomously by acquiring reference images during a training phase – a process that requires little human interaction and eliminates the need for time- consuming, manual annotation of object masks. Tis translates to efficient, reliable
and unbiased analyses that are founded on high-precision detection and segmentation. Low signal segmentation training enables experiments to be performed at ultra-low light exposure, allowing quantitative image analysis with minimal influence on cell viability. For example, by training a deep neural network with image pairs (where one image is taken under optimal lighting conditions and the other is underexposed), it is possible to achieve robust results with as little as 0.2% of the light usually required (Fig. 1). Deep learning has demonstrated
proven utility to analyse brightfield transmission images, competing with or even outperforming the classical approach with a fluorescence label (Fig. 2). As well as improving the viability of living cells by avoiding stress from transfection or chemical markers, brightfield imaging saves fluorescence channels to permit other markers to be used in downstream experiments; this greatly increases the depth of information obtained from sample material.
FIG. 2. Fluorescence-based methods often detect overlapping nuclei as a single object (a). Using brightfield images (b) alone, TruAI was able to segment these objects more accurately (c)
MICROSCOPY ACCESSIBLE AI holds huge potential for almost any scientific discipline, not least the field of microscopy where it has removed many common barriers previously limiting the technique’s success. Now, with recognition growing that setting up the necessary software no longer requires significant time and expertise, deep learning microscopy is pushing the boundaries of scientific understanding. Modern microscopy platforms featuring integrated deep learning software require just a brief training stage before being deployed to capture, quantify and analyse large numbers of images. For instance, Olympus’ cellSens software, and the software for the scanR high-content screening station (Fig. 3) and VS200 slide scanner now include the TruAI module, a deep learning approach based on convolutional neural networks. With deep learning, analysing vast datasets is no longer a bottleneck, and tasks that were previously impossible using manual thresholding methods are fast becoming mainstream. In addition, deep learning has provided researchers with many more options for quantifying microscopy images, including the use of ultra-low light exposure or brightfield images alone, highlighting the power of AI to benefit life science.
MAKING DEEP LEARNING
Mike Woerdemann is with Olympus.
www.olympus-lifescience.com
www.scientistlive.com 57
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