Evaluation of histological tissue sections is a critical tool for diagnosis and understanding of disease, and an indispensable tool for assessment of therapy in drug discovery and development. It is well known that there is fundamental and important prognostic data within images obtained from histological sections. The ability to quantitatively extract and analyze image features from digital pathology slide images that may not be visually discernible by a pathologist offers the opportunity for better modeling of disease and potentially improved prediction of disease aggressiveness and patient outcome.

With significant advances in computer software and hardware in the last decade, machine learning has become a pivotal tool used in a huge variety of applications. Major advances in computer vision and image processing techniques have contributed significantly to bio-imaging applications. Machine learning is especially well adapted to the classification and categorization of images based on the image content.

For classification and categorization, there are two classes of machine learning: supervised and unsupervised. Supervised machine learning involves annotation of image data by a human operator to build a dataset utilized to “train” the machine learning algorithm to recognize the important structures, patterns, and features of each category. Supervised machine learning is especially useful when such a training set exists, and the features used to identify and classify the image data are known. Deep learning and neural networks fall into the category of supervised machine learning, as well as Random Forest, Naive Bayes, and Support Vector Machine algorithms. Supervised machine learning techniques are essentially probability models that indicate the likelihood of a sample image falling into one of the trained categories. This approach is particularly valuable in the classification of image data into well defined categories identified by human operators.

Unsupervised machine learning is utilized when no training set or parameters exist, and is used to categorize image data based on features extracted the image data. Unsupervised machine learning requires no training data, rather it requires careful selection of features to be utilized to classify images (e.g. nuclear morphology). This approach is specifically useful when one wishes to examine the underlying structure of data without explicitly defining categories and training sets for the model.  Unsupervised machine learning techniques include clustering analysis (K-means and hierarchical agglomerative clustering), dimensionality reduction (e.g. principal component analysis).  Unsupervised approaches are particularly valuable for exploratory analysis when one seeks to identify the underlying structure of data gathered from image sets.

Visikol offers machine learning-based classification of histological sections as a service to clients using supervised or unsupervised techniques depending on the specific requirements of the project.

Figure 1. Results of supervised machine learning classification of potential tumor regions in sentinel lymph node section.

Figure 2. Representation of process of extraction of features for use in unsupervised machine learning classification. In this example, the nuclear channel is extracted and segmented to measure nuclear morphology, and the resultant data is utilized to classify tissue samples according to variations in nuclear morphology.