Microscopic Image Analysis (MIA) is a branch of Digital Image Analysis (DIA). In generalized DIA, an image is analysed by perceptual properties of its content rather than its metadata. Here, ‘content’ means all information that is able to be extracted automatically from the image itself, e.g., colours, textures, and shape; and ‘metadata’ means the individual information which describes the ‘contents’ of the image, e.g. tags, labels and keywords. DIA is an approach which extracts meaningful information from images and represents them by numerical feature vectors for different special tasks, such as image denoising, image segmentation, image classification, and image retrieval. Especially, MIA concentrates on the information extraction of ‘content’ of microscopic images.

Because MIA systems are usually semi- or fully-automatic, they are effective and can save a lot of human resources. Furthermore, because MIA approaches only need some cheap equipment, like microscopes and computers, the above analysis work can reduce a lot of financial investment. Hence, MIA can help people to obtain useful microcosmic information effectively, and it is widely used in many scientific and industrial fields, such as microoperation, material structure analysis, plant tissue analysis, histopathological analysis, cytopathological analysis and microbiological analysis.

In this Special Issue, we focus on the research work of “Microscopic Image Analysis in Histopathology”. This topic is related to histopathological image analysis, including (but not limited to) histopathological image denoising, segmentation, classification, clustering, retrieval, and detection. Both researchers and practitioners are welcome to submit their original papers and reviews. In particular, we hope that interdisciplinary researchers can contribute to this Special Issue from medical, biological, and engineering domains.

Potential topics include but are not limited to the following:

• Computational pathology
• Computer-aided prevention, diagnosis, prognosis, and treatment response
• Medical image analysis
• Digital histological image analysis
• Stain normalization/standardization
• Detection, segmentation, and classification of histology primitives (nuclei, epithelial region, glands, etc.)
• Diagnostic/prognostic/predictive biomarkers discovery from histology images
• Tissue-microarray/Whole-slide image registration
• Multiplexed staining and multimodel image registration
• Immunuhistology scoring
• Construction of diagnosis/prognosis/predictive model using histology images
• Applications of digtial histology image analysis