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Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and
IEEE Reviews in Biomedical Engineering
|May 8, 2014
Summary
Digital pathology enhances cancer diagnosis through automated nuclei analysis. This review explores computational techniques for nuclei detection, segmentation, and classification in histopathology images, aiming for improved accuracy and efficiency in cancer grading.
Area of Science:
- Digital pathology and computational image analysis in oncology.
- Histopathology, hematoxylin-eosin (H&E), and immunohistochemistry (IHC) staining protocols.
- Biomedical engineering and medical informatics applications in cancer research.
Background:
- Pathological examinations are crucial for cancer diagnosis and prognosis, relying on manual analysis of cell morphology and tissue architecture.
- Digital pathology and computerized methods are rapidly advancing, offering new tools for analyzing histopathology images.
- Current computational approaches focus on nuclei detection, segmentation, and classification to aid pathologists.
Purpose of the Study:
- To provide an exhaustive overview and identify major trends in nuclei detection, segmentation, feature computation, and classification techniques for histopathology.
- To analyze methods used with hematoxylin-eosin and immunohistochemical staining protocols.
- To assess remaining challenges in achieving robust whole slide image analysis for diagnostic support.
Main Methods:
- Comprehensive literature review of computational techniques in digital pathology.
- Focus on nuclei detection, segmentation, feature extraction, and classification algorithms.
- Analysis of methods applied to H&E and IHC stained histopathology images.
Main Results:
- Identification of key trends in computational nuclei analysis for cancer diagnosis and grading.
- Overview of diverse techniques applied to various cancer types (e.g., brain, breast, lung, prostate).
- Highlighting the role of these computational approaches in reducing human intervention and improving diagnostic traceability.
Conclusions:
- Computational methods are integral to the evolution of digital pathology, offering significant potential for cancer research and clinical practice.
- Further research is needed to overcome challenges in robust whole slide image analysis for diagnostic biomarkers and prognosis.
- The study provides a foundation for understanding current capabilities and future directions in automated histopathology image analysis.
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