Uhlmann Virginie, Singh Shantanu, Carpenter Anne E
Biomedical Imaging Group, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
BMC Bioinformatics. 2016 Jan 27;17:51. doi: 10.1186/s12859-016-0895-y.
Automated classification using machine learning often relies on features derived from segmenting individual objects, which can be difficult to automate. WND-CHARM is a previously developed classification algorithm in which features are computed on the whole image, thereby avoiding the need for segmentation. The algorithm obtained encouraging results but requires considerable computational expertise to execute. Furthermore, some benchmark sets have been shown to be subject to confounding artifacts that overestimate classification accuracy.
We developed CP-CHARM, a user-friendly image-based classification algorithm inspired by WND-CHARM in (i) its ability to capture a wide variety of morphological aspects of the image, and (ii) the absence of requirement for segmentation. In order to make such an image-based classification method easily accessible to the biological research community, CP-CHARM relies on the widely-used open-source image analysis software CellProfiler for feature extraction. To validate our method, we reproduced WND-CHARM's results and ensured that CP-CHARM obtained comparable performance. We then successfully applied our approach on cell-based assay data and on tissue images. We designed these new training and test sets to reduce the effect of batch-related artifacts.
The proposed method preserves the strengths of WND-CHARM - it extracts a wide variety of morphological features directly on whole images thereby avoiding the need for cell segmentation, but additionally, it makes the methods easily accessible for researchers without computational expertise by implementing them as a CellProfiler pipeline. It has been demonstrated to perform well on a wide range of bioimage classification problems, including on new datasets that have been carefully selected and annotated to minimize batch effects. This provides for the first time a realistic and reliable assessment of the whole image classification strategy.
使用机器学习的自动分类通常依赖于从单个对象分割中派生的特征，而这可能难以实现自动化。WND-CHARM是一种先前开发的分类算法，其特征是在整个图像上计算的，从而避免了分割的需要。该算法取得了令人鼓舞的结果，但需要相当多的计算专业知识才能执行。此外，一些基准集已被证明存在混淆伪影，会高估分类准确性。
我们开发了CP-CHARM，这是一种用户友好的基于图像的分类算法，其灵感来自于WND-CHARM的以下两点：（i）捕捉图像各种形态学方面的能力，以及（ii）无需分割的特点。为了使生物研究界能够轻松使用这种基于图像的分类方法，CP-CHARM依赖于广泛使用的开源图像分析软件CellProfiler进行特征提取。为了验证我们的方法，我们重现了WND-CHARM的结果，并确保CP-CHARM获得了可比的性能。然后，我们成功地将我们的方法应用于基于细胞的检测数据和组织图像。我们设计了这些新的训练集和测试集，以减少批次相关伪影的影响。
所提出的方法保留了WND-CHARM的优点——它直接在整个图像上提取各种形态特征，从而避免了细胞分割的需要，但此外，通过将其实现为CellProfiler管道，它使没有计算专业知识的研究人员也能轻松使用这些方法。已证明它在广泛的生物图像分类问题上表现良好，包括在经过精心挑选和注释以最小化批次效应的新数据集上。这首次为全图像分类策略提供了现实可靠的评估。
