Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Multi Resolution Fuzzy Segmentation of Satellite Images

Segmentation and shape classification of nuclei in DAPI images

This paper addresses issues of analysis of DAPI-stained microscopy images of cell samples, particularly classification of objects as single nuclei, nuclei clusters or nonnuclear material. First, segmentation is significantly improved compared to Otsu’s method [5] by choosing a more appropriate threshold, using a cost-function that explicitly relates to the quality of resulting boundary, rather ...

Automatic segmentation of arteries in multi-stain histology images

Atherosclerosis is a very common disease that affects millions of people around the world. Currently most of the studies conducted on this disease use Ultrasound Imaging (IVUS) to observe plaque formation, but these images cannot provide any detailed information of the specific morphological features of the plaque. Microscopic imaging using a variety of stains can provide much more information ...

A Multi-resolution Approach for Combining Visual Information using Nuclei Segmentation and Classification in Histopathological Images

This paper describes a multi-resolution technique to combine diagnostically important visual information at different magnifications in H&E whole slide images (WSI) of gastric cancer. The primary goal is to improve the results of nuclei segmentation method for heterogeneous histopathological datasets with variations in stain intensity and malignancy levels. A minimum-model nuclei segmentation m...

Representation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Images

Convolutional Neural Network (CNN) models have become the state-of-the-art for most computer vision tasks with natural images. However, these are not best suited for multi-gigapixel resolution Whole Slide Images (WSIs) of histology slides due to large size of these images. Current approaches construct smaller patches from WSIs which results in the loss of contextual information. We propose to c...