Article

Automated image analysis in histopathology: A valuable tool in medical diagnostics

UCD School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland.
Expert Review of Molecular Diagnostics (Impact Factor: 4.27). 12/2008; 8(6):707-25. DOI: 10.1586/14737159.8.6.707
Source: PubMed

ABSTRACT Virtual pathology, the process of assessing digital images of histological slides, is gaining momentum in today's laboratory environment. Indeed, digital image acquisition systems are becoming commonplace, and associated image analysis solutions are viewed by most as the next critical step in automated histological analysis. Here, we document the advances in the technology, with reference to past and current techniques in histological assessment. In addition, the demand for these technologies is analyzed with major players profiled. As there are several image analysis software programs focusing on the quantification of immunohistochemical staining, particular attention is paid to this application in this review. Oncology has been a primary target area for these approaches, with example studies in this therapeutic area being covered here. Toxicology-based image analysis solutions are also profiled as these are steadily increasing in popularity, especially within the pharmaceutical industry. Reinforced by the phenomenal growth of the virtual pathology field, it is envisioned that the market for automated image analysis tools will greatly expand over the next 10 years.

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    • "Even though current systems are technically complex and not easily operated by the average practising pathologist, automated quantification will increasingly be a part of standard practice in the future (Leong and Leong, 2003; Park et al., 2012). Also, with the continuing rapid development of computer technologies and their reduced cost, it is suggested that successful computer-based automated image analysis will become commonplace and lie within the reach of individual pathologists , clinical researchers and other members of the wide-ranging histological community (Krenacs et al., 2010; Mulrane et al., 2008). At a time when automated morphological classification of histological images is increasingly prevalent in routine practice for other disciplines , including human anatomic and clinical pathology, the use of this methodology is still largely absent from fish health and veterinary applications. "
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    • "However, both solutions can incur significant man-hours to derive an interpretation. Automated feature detection in images is an active area of research in image processing, including many applications such as road extraction (Shao et al., 2011; Treash and Amaratunga, 2000) and medical applications (Den Hertog et al., 2010; Mulrane et al., 2008; Onkaew et al., 2011). Image analysis techniques provide an effective and fast method of lineament detection and these techniques can extract lineaments which are difficult to recognise using the human eye alone (Wang and Howarth, 1990). "
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    • "The presented approach differs from other commercially available packages and previously proposed supervised methods, since a molecular marker is used to objectively select data for training purposes. Additionally, previously described methods for use with digital pathology pattern recognition applications, have focused on laborious image object identification by skilled pathologists, followed by the measurement of texture and morphological features of single or group of pixels with similar texture or color properties [9], [10], [43]. Methods characterizing epithelial nuclear characteristics, such as size, color and spatial distribution in order to distinguish tumor and lymphocyte infiltration has shown to be effective in solid tumors such as breast cancer [11], [44]. "
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