Ideally, our choice would be driven by the precise question we want to answer from the image. The richness of histopathology imaging data means that a single whole‐slide scan affords a plethora of possibilities for analysis. Even a small study containing tens of images requires us to grapple with trillions of pixels, from which we often want to extract at most a few actionable insights per image. The challenge of analysis is to identify and interpret meaningful patterns within these numbers-and to do so in a way that is robust to variation from multifarious sources, including biology, tissue processing, staining, and scanning. A typical whole‐slide image can therefore be thought of as a Width × Height × 3 array the width and height often exceed 100,000 pixels each, so the raw data comprises billions of numbers. In pathology, most images are brightfield whole‐slide scans in RGB format: this means that each pixel comprises three numbers-usually 8‐bit integers in the range 0–255-that together represent the red, green, and blue components of the colour used to display the pixel. The core ideas of image analysis are quite straightforward, although applying them is not. However, despite considerable progress over the last decade, digital pathology analysis remains difficult to employ in practice and much of its promise remains to be fulfilled. The growth of digital pathology and whole‐slide imaging have created the opportunity to extract more information from histological samples through image analysis. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. The review ends with a discussion about how digital pathology could benefit from interacting with and learning from the wider bioimage analysis community, particularly with regard to sharing data, software, and ideas. I describe the need for a collaborative and multidisciplinary approach to developing and validating meaningful new algorithms, and argue that openness, implementation, and usability deserve more attention among digital pathology researchers. I then examine the practical challenges inherent in taking algorithms beyond proof‐of‐concept, from both a user and developer perspective. This review begins by introducing the main approaches and techniques involved in analysing pathology images. The result is a disconnect between what seems already possible in digital pathology based upon the literature, and what actually is possible for anyone wishing to apply it using currently available software. #Cellprofiler identify postive cells softwareThe explanation is often straightforward: software implementing the method is simply not available, or is too complex, incomplete, or dataset‐dependent for others to use. However, despite novel image analysis methods for pathology being described across many publications, few become widely adopted and many are not applied in more than a single study. The potential to use quantitative image analysis and artificial intelligence is one of the driving forces behind digital pathology.
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