Image and Video Segmentation of Appearance-Volatile Objects

Segmentation is a process of partitioning a digital image or frame into multiple regions or objects. The goal of segmentation is to identify and locate the objects of interest with their boundaries. Recent segmentation approaches often follow such a pipeline: they first train the model on a collected dataset and then evaluate the trained model on a given image or video. They assume that the appearance of object is consistent in training and testing sets. However, the appearance of object may change in different photography conditions. How to effectively segment the objects with volatile appearance remains under-explored. In this work, we present a framework for image and video segmentation of appearance-volatile objects, including two novel modules, uncertain region refinement and feature bank. For image segmentation, we designed a new confidence loss and a fine-grained segmentation module to enhance the segmentation accuracy in uncertain regions. For video segmentation, we proposed a matching-based algorithm which feature banks are created to store features for region matching and classification. We introduced an adaptive feature bank update scheme to dynamically absorb new features and discard obsolete features. We compared our algorithm and the state-of-the-art methods on the public benchmarks. Our algorithm outperforms the existing methods and can produce more reliable and accurate segmentation results

Deep learning for digitized histology image analysis

“Cervical cancer is the fourth most frequent cancer that affects women worldwide. Assessment of cervical intraepithelial neoplasia (CIN) through histopathology remains as the standard for absolute determination of cancer. The examination of tissue samples under a microscope requires considerable time and effort from expert pathologists. There is a need to design an automated tool to assist pathologists for digitized histology slide analysis. Pre-cervical cancer is generally determined by examining the CIN which is the growth of atypical cells from the basement membrane (bottom) to the top of the epithelium. It has four grades, including: Normal, CIN1, CIN2, and CIN3. In this research, different facets of an automated digitized histology epithelium assessment pipeline have been explored to mimic the pathologist diagnostic approach. The entire pipeline from slide to epithelium CIN grade has been designed and developed using deep learning models and imaging techniques to analyze the whole slide image (WSI). The process is as follows: 1) identification of epithelium by filtering the regions extracted from a low-resolution image with a binary classifier network; 2) epithelium segmentation; 3) deep regression for pixel-wise segmentation of epithelium by patch-based image analysis; 4) attention-based CIN classification with localized sequential feature modeling. Deep learning-based nuclei detection by superpixels was performed as an extension of our research. Results from this research indicate an improved performance of CIN assessment over state-of-the-art methods for nuclei segmentation, epithelium segmentation, and CIN classification, as well as the development of a prototype WSI-level tool”--Abstract, page iv

Medical image synthesis using generative adversarial networks: towards photo-realistic image synthesis

This proposed work addresses the photo-realism for synthetic images. We introduced a modified generative adversarial network: StencilGAN. It is a perceptually-aware generative adversarial network that synthesizes images based on overlaid labelled masks. This technique can be a prominent solution for the scarcity of the resources in the healthcare sector