Review of In Situ Hybridization (ISH) Stain Images Using Computational Techniques

Abstract

Recent advancements in medical imaging have greatly enhanced the application of computational techniques in digital pathology, particularly for the classification of breast cancer using in situ hybridization (ISH) imaging. HER2 amplification, a key prognostic marker in 20-25% of breast cancers, can be assessed through alterations in gene copy number or protein expression. However, challenges persist due to the heterogeneity of nuclear regions and complexities in cancer biomarker detection. This review examines semi-automated and fully automated computational methods for analyzing ISH images with a focus on HER2 gene amplification. Literature from 1997 to 2023 is analyzed, emphasizing silver-enhanced in situ hybridization (SISH) and its integration with image processing and machine learning techniques. Both conventional machine learning approaches and recent advances in deep learning are compared. The review reveals that automated ISH analysis in combination with bright-field microscopy provides a cost-effective and scalable solution for routine pathology. The integration of deep learning techniques shows promise in improving accuracy over conventional methods, although there are limitations related to data variability and computational demands. Automated ISH analysis can reduce manual labor and increase diagnostic accuracy. Future research should focus on refining these computational methods, particularly in handling the complex nature of HER2 status evaluation, and integrate best practices to further enhance clinical adoption of these techniques.

Keywords: deep learning; fluorescent in situ hybridization (FISH); human epidermal growth factor receptor 2 (HER2); pathologies; silver-enhanced in situ hybridization (SISH).

Figure 1

Differenttypes of cytogenic images resulting from ISH: (a) FISH at 20× magnification, (b) CISH at 20× magnification, and (c) SISH at 40× magnification.

Figure 2

Breakdown of computational methods commonly used for histopathology image analysis.

Figure 3

A machine vision-based approach used in digital pathology image analysis. The red squares in subfigure (A) indicate selected regions for machine vision analysis. The whole slide image (WSI) is at a magnification level of 40×.

Figure 4

Examples of how digital photographs have been altered using grayscale-based contrast enhancement and thresholding for different cytogenetic types of ISH: (a) scale variation in CISH at 20× magnification, (b) scale variation in FISH at 20× magnification, and (c) scale variation in SISH at 40× magnification.

Figure 5

Automated image processing-based system demonstration at 40× magnification: (a) original SISH images, (b) preprocessed for ground truth generation, (c) nuclei-labeled ground truth images, (d) marked labeled nuclei on the original image, and (e) marked labeled nuclei and HER2 signals. More precise signal detection refines nuclei segmentation.