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: 3.52).
12/2008; 8(6):707-25. DOI: 10.1586/14737126.96.36.1997
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.
Available from: PubMed Central
- "and an accuracy of the diagnostic process  . While classical objective morphological denominators like areas and diameters have proved insufficient to describe highly variable and complex pathological processes, scale-invariant parameters like fractal dimension (FD) have been very useful in characterizing complex and nonregular objects . "
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ABSTRACT: Pathological diagnosis of prostate adenocarcinoma often requires complementary methods. On prostate biopsy tissue from 39 patients including benign nodular hyperplasia (BNH), atypical adenomatous hyperplasia (AAH), and adenocarcinomas, we have performed combined histochemical-immunohistochemical stainings for argyrophilic nucleolar organizer regions (AgNORs) and glandular basal cells. After ascertaining the pathology, we have analyzed the number, roundness, area, and fractal dimension of individual AgNORs or of their skeleton-filtered maps. We have optimized here for the first time a combination of AgNOR morphological denominators that would reflect best the differences between these pathologies. The analysis of AgNORs' roundness, averaged from large composite images, revealed clear-cut lower values in adenocarcinomas compared to benign and atypical lesions but with no differences between different Gleason scores. Fractal dimension (FD) of AgNOR silhouettes not only revealed significant lower values for global cancer images compared to AAH and BNH images, but was also able to differentiate between Gleason pattern 2 and Gleason patterns 3-5 adenocarcinomas. Plotting the frequency distribution of the FDs for different pathologies showed clear differences between all Gleason patterns and BNH. Together with existing morphological classifiers, AgNOR analysis might contribute to a faster and more reliable machine-assisted screening of prostatic adenocarcinoma, as an essential aid for pathologists.
Analytical cellular pathology (Amsterdam) 09/2015; 2015(6):250265. DOI:10.1155/2015/250265 · 0.85 Impact Factor
Available from: P.F. Silva
- "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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ABSTRACT: Virtual histology, the process of assessing digital images of histological slides, is gaining momentum in modern histopathology and digital image acquisition systems are becoming commonplace. Associated image processing and analysis methods can potentially complement traditional histological assessment methodologies.Image analysis of digitised histological sections, can provide a practical means for quantifying and helping interpretation of functional alterations in an objective and reproducible fashion. This study focused on the development of a practical and time-efficient image capture, processing and analysis pipeline, employing advanced image analysis that was able to identify features of salmon intestine histological sections in a quantitative manner. Through standardisation of the sampling and preparation methodologies, staining protocols and digital capture thresholds and techniques, this assessment system has proven to be an efficient, accurate and objective method, having consistent data outputs when the analysis is performed by different observers with varying levels of expertise in histopathological assessment.
Aquaculture 03/2015; 442. DOI:10.1016/j.aquaculture.2015.02.034 · 1.88 Impact Factor
Available from: Steven Micklethwaite
- "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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ABSTRACT: Recent advances in data acquisition technologies, such as Unmanned Aerial Vehicles (UAVs), have led to a growing interest in capturing high-resolution rock surface images. However, due to the large volumes of data that can be captured in a short flight, efficient analysis of this data brings new challenges, especially the time it takes to digitise maps and extract orientation data.
We outline a semi-automated method that allows efficient mapping of geological faults using photogrammetric data of rock surfaces, which was generated from aerial photographs collected by a UAV. Our method harnesses advanced automated image analysis techniques and human data interaction to rapidly map structures and then calculate their dip and dip directions. Geological structures (faults, joints and fractures) are first detected from the primary photographic dataset and the equivalent three dimensional (3D) structures are then identified within a 3D surface model generated by structure from motion (SfM). From this information the location, dip and dip direction of the geological structures are calculated.
A structure map generated by our semi-automated method obtained a recall rate of 79.8% when compared against a fault map produced using expert manual digitising and interpretation methods. The semi-automated structure map was produced in 10 minutes whereas the manual method took approximately 7 hours. In addition, the dip and dip direction calculation, using our automated method, shows a mean±standard error of 1.9°±2.2° and 4.4°±2.6° respectively with field measurements. This shows the potential of using our semi-automated method for accurate and efficient mapping of geological structures, particularly from remote, inaccessible or hazardous sites.
Computers & Geosciences 08/2014; 69. DOI:10.1016/j.cageo.2014.04.012 · 2.05 Impact Factor
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