Digital Pathology, which can also be referred to as virtual microscopy or virtual pathology involves managing, analysing and interpreting digital information. In this field of technology the need to make inter-image comparisons and to train learning algorithms on large volumes of data represents a significant challenge. Whole slide microscope images may comprise large volumes of data. The applicants have recognised a need to automate upload and processing of such data without compromising the ability of such methods to provide objective comparisons between different tissue samples acquired using different systems,
Processes described herein may involve the generation of glass slides and converting these to digital pathology slides using digital pathology solutions. A digital slide scan is then generated which allows for high resolution viewing, interpretation and image analysis of digital pathology images. Growth in digital pathology solutions has totally transformed how research labs manage and interpret glass slides, with analysis and interpretation of histological images now conducted on a computer screen.
Gaining momentum globally, digital pathology is being used across health and pharmaceutical sectors, education and contract research organisations. With wide ranging applications the realised benefits of this sophisticated technology have encourage high growth in the market for digital pathology solutions, which by 2020 is estimated to be worth $5.7 billion.
The ability to provide accurate diagnosis is critical to the provision of healthcare. Biopsies to identify the presence of diseases such as cancer are a useful tool in such diagnosis. Accurate prognostic assessment is also critically important, because it enables action to be taken to counteract further development of disease. Microscope images of tissue samples have been used for these purposes for many years.
Large numbers of microscope imaging systems, each with their own particular characteristics, have been developed for this purpose. Whole slide imaging systems obtain digital images of entire microscope slides by scanning the field of view of a microscope across a macroscopic tissue sample to obtain a series of digital images. The resulting digital images can then be concatenated together to provide a single image, or image set, which describes the entire microscope slide. Partial images of slides can also be obtained by the same approach.
Pathologists involved in making diagnoses based on these kinds of images may rely on qualitative judgements. Such judgements may be based on their scientific knowledge and also on personal experience. This is necessarily a subjective process. As a result diagnoses, and prognostic assessments are not always reproducible—different pathologists may make different judgements based on identical images of tissue samples.
In making diagnostic judgements, the pathologist's task is made still more difficult because large tissue samples may be involved. In such cases many tens, or even hundreds of microscope images may need to be analysed from a single patient. This is particularly true where multiple tissue biopsies have been taken from a relatively large area of the body such as the prostate. These issues compound the problem of reproducibility because two different pathologists assessing the same patient's tissue sample may take into account features of different areas of different images of the same tissue sample.
The conditions under which a tissue sample was obtained, and the treatment of that sample before it was imaged (for example in terms of the concentration of stain applied to it), the imaging system used to acquire the image, and the presence of image artefacts may all cause variations between images. Although painstaking analysis is required, human pathologists are at least able intuitively to make allowances for such confounds. The subjective nature of assessment by human pathologists therefore, whilst problematic, at least provides one way to address these problems of inter-image variability. This need for intuitive judgement prevents straightforward automation of diagnostic and prognostic assessment of microscope images.
There are still further obstacles to overcome.