Instance Segmentation of Dense and Overlapping Objects via Layering

Instance segmentation aims to delineate each individual object of interest in an image. State-of-the-art approaches achieve this goal by either partitioning semantic segmentations or refining coarse representations of detected objects. In this work, we propose a novel approach to solve the problem via object layering, i.e. by distributing crowded, even overlapping objects into different layers. By grouping spatially separated objects in the same layer, instances can be effortlessly isolated by extracting connected components in each layer. In comparison to previous methods, our approach is not affected by complex object shapes or object overlaps. With minimal post-processing, our method yields very competitive results on a diverse line of datasets: C. elegans (BBBC), Overlapping Cervical Cells (OCC) and cultured neuroblastoma cells (CCDB). The source code is publicly available

SortedAP: Rethinking evaluation metrics for instance segmentation

Designing metrics for evaluating instance segmentation revolves around comprehensively considering object detection and segmentation accuracy. However, other important properties, such as sensitivity, continuity, and equality, are overlooked in the current study. In this paper, we reveal that most existing metrics have a limited resolution of segmentation quality. They are only conditionally sensitive to the change of masks or false predictions. For certain metrics, the score can change drastically in a narrow range which could provide a misleading indication of the quality gap between results. Therefore, we propose a new metric called sortedAP, which strictly decreases with both object- and pixel-level imperfections and has an uninterrupted penalization scale over the entire domain. We provide the evaluation toolkit and experiment code at https://www.github.com/looooongChen/sortedAP

Semi-supervised Instance Segmentation with a Learned Shape Prior

To date, most instance segmentation approaches are based on supervised learning that requires a considerable amount of annotated object contours as training ground truth. Here, we propose a framework that searches for the target object based on a shape prior. The shape prior model is learned with a variational autoencoder that requires only a very limited amount of training data: In our experiments, a few dozens of object shape patches from the target dataset, as well as purely synthetic shapes, were sufficient to achieve results en par with supervised methods with full access to training data on two out of three cell segmentation datasets. Our method with a synthetic shape prior was superior to pre-trained supervised models with access to limited domain-specific training data on all three datasets. Since the learning of prior models requires shape patches, whether real or synthetic data, we call this framework semi-supervised learning

Mapping the water-economic cascading risks within a multilayer network of supply chains

Trade linkages within the supply chain can be mapped onto a complex network. Disruptions in regional resource supplies (i.e., water scarcity) have the potential to generate industrial losses in remote areas due to the interconnected flow of goods and services. While numerous studies have assessed the economic and virtual water supply networks, they assumed rapid and linear transmission of industrial risks in the network, without modeling the transmission process and vulnerability between nodes. Such oversights can lead to the misestimation of risks, especially in the context of climate change. Therefore, it is urgent to construct a cascading model for the supply economic and water supply network that consider step-by-step avalanche in nodes, to help identify vulnerable sectors and mitigate economic risks.In this research, we utilize the 2017 multi-region environmental multiregional input-output (E-MRIO) table in China to construct a comprehensive multilayer network. Each province is represented as a distinct layer within this network, incorporating 42 economic sectors(nodes). These layers and nodes are interconnected through trade linkages. To simulate the cascade process, we introduce the concept of net fragility for a node, calculated as the difference between the ratio of the sum of net inflows and net outflows of a node to its own total output and the threshold. Once a node fails (i.e., net fragility less than 1) the cascade process is triggered, then we quantify the total number of collapsed adjacent nodes, i.e., avalanche size. The bigger avalanche size refers to the province-sector higher vulnerability to economic shocks. Furthermore, we use the risk probability of province-sectors suffering from water scarcity as external shocks to describe the supply network response process under different water quantity and quality constraints. By comparing with the economic impact results, we can further identify vulnerable nodes affected by the dual restraints of water scarcity and economic shocks

Lake ice simulation using a 3D unstructured grid model

We develop a single-class ice and snow model embedded inside a 3D hydrodynamic model on unstructured grids and apply it to lake studies using highly variable mesh resolution. The model is able to reasonably capture the ice fields observed in both small and large lakes. For the first time, we attempt simulation of ice processes on very small scales (~ 1 m). Physically sound results are obtained at the expense of moderately increased computational cost, although more rigorous validation nearshore is needed due to lack of observation. We also outline challenges on developing new process-based capabilities for accurately simulating nearshore ice