What Techniques are Used for Data Segmentation in Histology?

Various techniques are employed for data segmentation in histology, ranging from manual to fully automated methods:

Manual Segmentation: Involves human experts manually delineating regions of interest. Although accurate, it is time-consuming and prone to subjective bias.
Thresholding: A simple method that separates objects based on intensity values. It is effective for images with high contrast but may fail in complex scenarios.
Edge Detection: Detects boundaries within an image by identifying discontinuities in intensity. Common algorithms include Canny and Sobel filters.
Region-Based Segmentation: Groups pixels with similar attributes. Techniques include region growing and watershed segmentation.
Machine Learning: Utilizes algorithms like Random Forests and Support Vector Machines to classify pixels based on learned patterns.
Deep Learning: Employs neural networks, particularly Convolutional Neural Networks (CNNs), to segment images. These methods have shown remarkable accuracy and robustness.