Segmentation and localisation of whole slide images using unsupervised learning

Abstract

Digital pathology has been clinically approved for over a decade to replace traditional methods of diagnosis. Many challenges appear when digitising the whole slide scan into high resolution images including memory and time management. Whole slide images require huge memory space if the tissue is not pre-localised for the scanner. The authors propose a set of clinically motivated features representing colour, intensity, texture and location to segment and localise the tissue from the whole slide image. This step saves both the scanning time and the required memory space. On average, it reduces scanning time up to 40% depending on the tissue type. The authors propose, using unsupervised learning, to segment and localise tissue by clustering. Unlike supervised methods, this method does not require the ground truth which is time consuming for domain experts. The authors proposed method achieves an average of 96% localisation accuracy on a large dataset. Moreover, the authors outperform the previously proposed supervised learning results on the same data.