An Automated Blur Detection Method for Histological Whole Slide Imaging

Abstract

Whole slide scanners are novel devices that enable high-resolution imaging of an entire histological slide. Furthermore, the imaging is achieved in only a few minutes, which enables image rendering of large-scale studies involving multiple immunohistochemistry biomarkers. Although whole slide imaging has improved considerably, locally poor focusing causes blurred regions of the image. These artifacts may strongly affect the quality of subsequent analyses, making a slide review process mandatory. This tedious and time-consuming task requires the scanner operator to carefully assess the virtual slide and to manually select new focus points. We propose a statistical learning method that provides early image quality feedback and automatically identifies regions of the image that require additional focus points.

Recent developments in multiplexing techniques for immunohistochemistry

“…Existing within that cohort are Caliper Vectra [77], Hamamatsu Nanozoomer [79], [78], [80]), Bacus TMAScore, Dako ACIS III, Genetix Ariol, Aperio Image Analysis, 3DHistech Mirax HistoQuant, Bioimagene Pathiam [81], all of which can scan slides affixed to whole tissue or TMA slices prior to image analysis. For these technologies, the slide scanning speed can be as quick as 20 slides/h [77].…”

“…Breast tissue is screened, with such tools, for one or more of the receptors known to be elevated in breast cancer cells, HER2, ER and PR [81]. Also, increasing system versatility, images from either fluorescent or chromogen-labeled tissue specimens can be acquired with Genetix Ariol, Mirax HistoQuant [81], Caliper Vectra [77], and Hamamatsu Nanozoomer [79], [78], [80]. …”

Teleophthalmopathologie mittels Whole Slide Imaging (WSI): klinisch-pathologische Korrelation auch ohne eigenes histologisches Labor

“…Aus den vorstehend beschriebenen Anwendungen können folgende Vorteile der Teleophthalmopathologie abgeleitet werden: ▪ schnelle Datenübertragung per Internet-Link ▪ Versand von Schnitten oder Gewebeproben entfällt und dadurch auch das Risiko von versandbedingtem Materialverlust ▪ Anfertigung von "Kopien" in unbegrenzter Zahl von rarem Material ▪ einfachere Handhabung bei Präsentationen ▪ weniger Geräte sind erforderlich, vor allem keine Mikroskope ▪ Beurteilung identischen Materials ▪ bei weitgehend digitaler Befundung entfallen Lagerkapazitäten für histologische Schnitte ▪ ergonomische und räumliche Flexibilität vollständig digital befundender Pathologen [8] Nachteile ▪ Bei großen Präparaten kann es bei nicht einheitlicher Fokussierung zu unscharfen Bereichen kommen, wodurch Details pathologischer Veränderungen übersehen werden könnten. Dies ist bei relativ dicken Schnitten trotz integrierter Autofokuseinrichtung modernerer Slide Scanner zu beobachten, wenn der Autofokus auf einen Punkt gelegt wurde, der sich unter-oder oberhalb der interessierenden Region befindet [30]. ▪ Sehr selten geht (etwas) Zeit verloren, wenn einzelne Präparate doch als Original versandt werden müssen, weil sich der Konsilpartner anhand der digitalen Datei nicht zu einer endgültigen Diagnose entschließen kann.…”

Methods for rapid and high quality acquisition of whole slide images

“…In (Hilsenstein 2005), outlier focus candidates are removed by smoothing the topgraphic of the focus map. A more sophisticated method is to image image every focus candidate at high resolution and find whether they contain real cells or contaimnations, such as methods developed in (Lahrmann et al 2013;Lopez et al 2013), where classfication methods were developed to distinguish cells from foreign objects based on image features of pre-classified images. In this way, one focus candidates containg cells are used for focus map interpolation to ensure the quality of it.In the absence of such techniques the interpolated focus map has the potential to be biased towards the artefacts (say) on top of the slide and hence the images acquired become de-focused and appear blurred.…”

“…Compared to the methods developed by (Lahrmann et al 2013;Lopez et al 2013), the algorithm proposed in this chapter acquires and analyses a low-resolution image of the entire microscope slide to produce both a specimen scan map and an ordered list of focus candidates. The tile-based method locates potential (i.e.…”