Track 20: Digital Pathology Image Analysis

sub track: -

 Fundamentals of Image Analysis. Image Segmentation. Quantitative Analysis. Pattern Recognition. Artificial Intelligence and Machine Learning. Annotation and Labelling. Visualization and Interpretation. Software and Tools. Clinical Applications, DigitalPathology, Image Analysis, PathologyTech, DigitalPathologyAnalysis, MedicalImagin, AIinPathology, Pathology Innovation, Digital Diagnostics, Image Processing, Pathology

Digital Pathology Image Analysis refers to the use of digital technology to examine and interpret images of tissue samples obtained from pathology slides. This process involves converting traditional glass slides into high-resolution digital images and applying various analytical techniques to these images to support diagnostic and research activities. Digital pathology image analysis is a process that uses computer workstations to view and analyze digital images of stained tissue sections from glass slides. The images are created by using a scanning device to create digital slides from glass slides, which can then be viewed on a computer or mobile device. The process is part of digital pathology, which is a sub-field of pathology that involves acquiring, managing, sharing, and interpreting pathology information in a digital environment.
Whole Slide Imaging (WSI):

Digital Slide Creation: WSI systems digitize entire glass slides, creating high-resolution digital images that cover the full extent of the tissue sample. These images serve as the basis for subsequent analysis.

Image Quality: High-resolution and high-quality imaging are essential for accurate analysis, allowing for detailed examination of tissue architecture and cellular features.

Image Processing and Enhancement:

Pre-Processing: Techniques such as noise reduction, colour normalization, and contrast enhancement are used to prepare images for analysis. This step ensures that artifacts are minimized and features of interest are clearly visible.

Segmentation: Image segmentation involves dividing an image into distinct regions or segments, such as separating tumor areas from normal tissue. This is a critical step for quantifying specific features and assessing tissue characteristics.

Feature Extraction:
Morphological Features: Analysing cell shape, size, and distribution to identify abnormalities or classify tissue types.

Textural Features: Assessing patterns and textures within the tissue to differentiate between various types of tissues or identify pathological changes.