AI, machine learning, and deep learning tools for cell image analysis

Artificial Intelligence in automated image analysis workflows

Machine learning improves image segmentation and objects classification

Automated image analysis is an integral part of most high-content imaging platforms. The ability to monitor cells and organoids in real-time and then extract meaningful information depends on robust image analysis of label-free transmitted light images. Challenges associated with the analysis of brightfield images include low contrast, uneven background, and imaging artifact. A defined set of global parameters would rarely be successful in segmenting objects imaged in brightfield. Recent advances in machine learning improve image analysis workflow and enable more robust image segmentation in complex datasets.

In bioimage analysis, deep learning provides a powerful toolbox to tackle challenging image segmentation and object tracking. Conventional image analysis typically involves defining a fixed set of parameters to segment objects of interest for downstream quantification. However, these predefined parameters do not work for all experiments due to the high variability in experimental conditions. Manual adjustments to the analysis protocol are impractical because of the sheer volume of imaging data in a high-throughput environment.

To overcome these challenges, machine learning tools can be used for image segmentation and object classification to automate the image analysis workflow.

IN Carta Image Analysis Software for machine learning-based, high-throughput analysis

The IN Carta® Image Analysis Software provides an intuitive user interface that includes AI tools in the image analysis workflow. Two key components of IN Carta software that leverage machine learning are the software modules SINAP and Phenoglyphs. Deep-learning-based SINAP enables robust detection of complex objects of interest (e.g., stem cell colonies or organoids) with minimal human intervention to improve accuracy and reliability in the first step of image analysis. The analysis output includes morphological, intensity, and texture measurements.

Classification of data can also be used with machine-learning-based Phenoglyphs. Phenoglyphs takes hundreds of image descriptors extracted by SINAP and creates an optimal set of rules for grouping objects with a similar visual appearance. Both modules utilize unsupervised decisions to generate an initial result that is iteratively optimized through user input. Together, they improve the integrity and accuracy of findings through an easy-to-use, end-to-end workflow.

Apply deep learning to object segmentation with IN Carta SINAP

Automatic object segmentation of microscopy images can be challenging due to the diverse nature of the datasets. IN Carta Image Analysis Software uses SINAP, a trainable segmentation module utilizing a deep convolutional neural network learning algorithm to tackle these challenging issues.

Because SINAP uses deep learning, it can account for significant variability in sample appearance that arises from the test treatments under investigation. By ensuring that each treatment is segmented with an equivalent level of accuracy, the information extracted in this step is reliable and useful to compare treatments in subsequent analysis steps.

Overcome challenges in image segmentation with machine learning-based models:

A) Examples of different biological models are presented that are challenging for quantitative analysis. 3D spheroids grown in microcavity plates produce a shadow around each microcavity that interferes with object segmentation (arrow). 3D organoids are grown in Matrigel, which often produces non-homogenous background due to distortion from the Matrigel dome and objects beyond the imaging planes (box). iPSC grows as relatively flat cultures; as a result, the low contrast (blue arrow) and debris (yellow arrow) hampers robust image segmentation of iPSC colonies.

B) Overview of the model training workflow: Generate images for training > Train model > Test model > Repeat.

C) Main steps to create a model in IN Carta software using SINAP with example images shown. Images are annotated using labeling tools to indicate the objects of interest and background. The annotated image representing ground truth is added to the training set. In the training step, a model is created based on the most-suitable existing model and user-specified annotations. In the example shown, it is necessary to correct the segmentation mask (step 3), repeating steps 1–3.

Apply machine learning to object classification with IN Carta Phenoglyphs