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

Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow. The development of automated methods for nuclear segmentation and classification enables the quantitative analysis of tens of thousands of nuclei within a whole-slide pathology image, opening up possibilities of further analysis of large-scale nuclear morphometry. However, automated nuclear segmentation and classification is faced with a major challenge in that there are several different types of nuclei, some of them exhibiting large intra-class variability such as the nuclei of tumour cells. Additionally, some of the nuclei are often clustered together. To address these challenges, we present a novel convolutional neural network for simultaneous nuclear segmentation and classification that leverages the instance-rich information encoded within the vertical and horizontal distances of nuclear pixels to their centres of mass. These distances are then utilised to separate clustered nuclei, resulting in an accurate segmentation, particularly in areas with overlapping instances. Then, for each segmented instance the network predicts the type of nucleus via a devoted up-sampling branch. We demonstrate state-of-the-art performance compared to other methods on multiple independent multi-tissue histology image datasets. As part of this work, we introduce a new dataset of Haematoxylin & Eosin stained colorectal adenocarcinoma image tiles, containing 24,319 exhaustively annotated nuclei with associated class labels

BIOMEDICAL SEGMENTATION ON CELL AND BRAIN IMAGES

The biomedical imaging techniques grow rapidly and output big amount of data quickly in the recent years. But image segmentation, one of the most important and fundamental biomedical data analysis techniques, is still time-consuming for human annotators. Therefore, there is an urgent need for segmentation to be taken by machine automatically. Segmentation is essential for biomedical image analysis and could help researchers to gain further diagnostic insights. This paper has three topics under biomedical image segmentation scenario. For the first topic, we examine a popular deep learning structure for segmentation task, U-Net, and modify it for our task on bacteria cell images by using boundary label setting and weighted loss function. Compared to the MATLAB segmentation program used before, the new deep learning method improves the performance in terms of object-level evaluation metrics. For the second topic, we participate into a brain image segmentation challenge which aims for helping neuroscientists to segment the membrane from neurites in order to get the reconstruction of neurites circuit. Data augmentation tricks and multiple loss functions are examined for improving the test performance and finally using combined loss functions can out-perform the original U-Net result in terms of the official ranking metric. A new dice loss is designed to focus more on the hard to segment class. The third topic is to apply the unsupervised segmentation method which will not be restrained by human labelling speed and effort. This is meaningful under biomedical segmentation scenario where training data with expert labelling is always lacking. Without using any labelled data, the unsupervised method, Double DIP, performs better than the MATLAB program on the semantic level

Satellite Observations and Spatiotemporal Assessment of Salt Marsh /Dieback Along Coastal South Carolina (1990-2019)

Coastal wetland mapping is often difficult because of the heterogeneous vegetation compositions and associated tidal effects. Past studies in the Gulf/Atlantic coast states have reported acute marsh dieback events in which marsh rapidly browned and thinned, leaving stubble of dead stems or mudflad with damaged ecosystem services. Reported marsh dieback in South Carolina (SC), USA, however, have been limited. Previous studies have suggested a suite of possibly abiotic and biotic attributes responsible for salt marsh dieback. However, there are no consensus answers in current literature explaining what led to marsh dieback in past decades, especially from the spatiotemporal perspective. In this study, the U-Net was employed, and an adaptive deep learning approach was developed to map statewide salt marshes in estuarine emergent wetlands of SC from 20 Sentinel-2A&B images. Then all marsh dieback events were identified in the North Inlet-Winyah Bay (NIWB) estuary, SC, from 1990 to 2019. With 30 annually collected Landsat images, the Normalized Difference Vegetation Index (NDVI) series was extracted. A Stacked Denoising Autoencoder neural network was developed to identify the NDVI anomalies on the trajectories. All marsh dieback patches were extracted, and their inter-annual changes were examined. Among these were the five most severe marsh dieback events (1991, 1999, 2000, 2002, and 2013). The spatiotemporal relationships between the dieback series and the associated environmental variables in an intertidal marsh in the estuary were investigated. Daily Evaporative Demand Drought Index (EDDI), daily precipitation data from Parameter Elevation Regressions on Independent Slopes Model (PRISM), and station-based water quality observations (dissolved oxygen, specific conductivity, salinity, turbidity, pH, and temperature) in the estuary were retrieved. This study cogitates the environmental influence on coastal marsh from a spatiotemporal perspective using a long-term satellite time series analysis. The findings could provide insights into marsh ecological resilience and facilitate coastal ecosystem management