Authors: Nuh Hatipoglu Gokhan Bilgin
Publish Date: 2017/02/28
Volume: 55, Issue: 10, Pages: 1829-1848
Abstract
In many computerized methods for cell detection segmentation and classification in digital histopathology that have recently emerged the task of cell segmentation remains a chief problem for image processing in designing computeraided diagnosis CAD systems In research and diagnostic studies on cancer pathologists can use CAD systems as second readers to analyze highresolution histopathological images Since cell detection and segmentation are critical for cancer grade assessments cellular and extracellular structures should primarily be extracted from histopathological images In response we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms ie convolutional neural networks stacked autoencoders and deep belief networks but also spatial relationships information of which is critical for achieving better cell segmentation results To that end we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes In experiments the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information Once we compared the effects of training sample size and influence of window size results revealed that the deep learning algorithms especially convolutional neural networks and partly stacked autoencoders performed better than conventional methods in cell segmentation
Keywords: