Institute of Computer Science, Leipzig University, Leipzig 04107, Germany.
IfADo-Leibniz Research Centre for Working Environment and Human Factors, Dortmund 44139, Germany.
Bioinformatics. 2022 Sep 30;38(19):4622-4628. doi: 10.1093/bioinformatics/btac547.
Over the last decades, image processing and analysis have become one of the key technologies in systems biology and medicine. The quantification of anatomical structures and dynamic processes in living systems is essential for understanding the complex underlying mechanisms and allows, i.e. the construction of spatio-temporal models that illuminate the interplay between architecture and function. Recently, deep learning significantly improved the performance of traditional image analysis in cases where imaging techniques provide large amounts of data. However, if only a few images are available or qualified annotations are expensive to produce, the applicability of deep learning is still limited.
We present a novel approach that combines machine learning-based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large image sets which enables a guided reuse of interactively trained classifiers. Our approach solves the problem of deteriorated segmentation and quantification accuracy when reusing trained classifiers which is due to significant color variability prevalent and often unavoidable in biological and medical images. This increase in efficiency improves the suitability of interactive segmentation for larger image sets, enabling efficient quantification or the rapid generation of training data for deep learning with minimal effort. The presented methods are applicable for almost any image type and represent a useful tool for image analysis tasks in general.
The presented methods are implemented in our image processing software TiQuant which is freely available at tiquant.hoehme.com.
Supplementary data are available at Bioinformatics online.
在过去的几十年中，图像处理和分析已成为系统生物学和医学中的关键技术之一。对活体系统中解剖结构和动态过程的定量分析对于理解复杂的潜在机制至关重要，并且允许构建阐明结构与功能之间相互作用的时空模型。最近，深度学习在成像技术提供大量数据的情况下，极大地提高了传统图像分析的性能。但是，如果可用的图像很少，或者生成合格的注释很昂贵，则深度学习的适用性仍然有限。
我们提出了一种新方法，该方法结合了基于机器学习的超像素交互式图像分割和聚类方法，用于自动识别大型图像集中颜色相似的图像，从而可以引导重用交互式训练的分类器。我们的方法解决了由于生物和医学图像中普遍存在且通常不可避免的显着颜色变化而导致重新使用训练有素的分类器时分割和量化精度降低的问题。这种效率的提高提高了交互式分割对于更大图像集的适用性，能够以最小的工作量对深度学习进行高效的定量或快速生成训练数据。所提出的方法几乎适用于任何图像类型，并且是一般图像分析任务的有用工具。
所提出的方法已在我们的图像处理软件 TiQuant 中实现，该软件可在 tiquant.hoehme.com 上免费获得。
补充数据可在“生物信息学”在线获得。
