Content aware multi-focus image fusion for high-magnification blood film microscopy.
Content aware multi-focus image fusion for high-magnification blood film microscopy.
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DOI：
10.1364/boe.448280
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发表时间：
2022-02-01
影响因子：
3.4
通讯作者：
Fernandez-Reyes D
中科院分区：
文献类型：
--
作者：
Manescu P;Shaw M;Zajiczek LN;Bendkowski C;Claveau R;Elmi M;Brown BJ;Fernandez-Reyes D
自动化数字高倍率光学显微镜是加速生物学研究和改善病理学临床途径的关键。具有大数值孔径的高放大倍数物镜通常优选用于解析生物样品的精细结构细节，但它们具有非常有限的景深。根据样品的厚度，样本的分析通常需要在每个视场的不同焦平面处采集多个图像，然后将这些平面融合成扩展的景深图像。这意味着低扫描速度、增加的存储空间和不适合高通量临床使用的处理时间。提出了一种基于深度学习的内容感知多聚焦图像融合方法，有效地扩展了高倍率物镜的景深。我们演示了三个例子的方法，显示出高精度，详细的，扩展的景深图像可以在较低的轴向采样率，使用2倍少的焦平面比通常需要的。
Automated digital high-magnification optical microscopy is key to accelerating biology research and improving pathology clinical pathways. High magnification objectives with large numerical apertures are usually preferred to resolve the fine structural details of biological samples, but they have a very limited depth-of-field. Depending on the thickness of the sample, analysis of specimens typically requires the acquisition of multiple images at different focal planes for each field-of-view, followed by the fusion of these planes into an extended depth-of-field image. This translates into low scanning speeds, increased storage space, and processing time not suitable for high-throughput clinical use. We introduce a novel content-aware multi-focus image fusion approach based on deep learning which extends the depth-of-field of high magnification objectives effectively. We demonstrate the method with three examples, showing that highly accurate, detailed, extended depth of field images can be obtained at a lower axial sampling rate, using 2-fold fewer focal planes than normally required.