DARC: Distribution-Aware Re-Coloring Model for Generalizable Nucleus Segmentation

Nucleus segmentation is usually the first step in pathological image analysis tasks. Generalizable nucleus segmentation refers to the problem of training a segmentation model that is robust to domain gaps between the source and target domains. The domain gaps are usually believed to be caused by the varied image acquisition conditions, e.g., different scanners, tissues, or staining protocols. In this paper, we argue that domain gaps can also be caused by different foreground (nucleus)-background ratios, as this ratio significantly affects feature statistics that are critical to normalization layers. We propose a Distribution-Aware Re-Coloring (DARC) model that handles the above challenges from two perspectives. First, we introduce a re-coloring method that relieves dramatic image color variations between different domains. Second, we propose a new instance normalization method that is robust to the variation in foreground-background ratios. We evaluate the proposed methods on two H$\&$E stained image datasets, named CoNSeP and CPM17, and two IHC stained image datasets, called DeepLIIF and BC-DeepLIIF. Extensive experimental results justify the effectiveness of our proposed DARC model. Codes are available at \url{https://github.com/csccsccsccsc/DARCComment: Accepted by MICCAI 202

CPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentation

Nucleus segmentation is a challenging task due to the crowded distribution and blurry boundaries of nuclei. Recent approaches represent nuclei by means of polygons to differentiate between touching and overlapping nuclei and have accordingly achieved promising performance. Each polygon is represented by a set of centroid-to-boundary distances, which are in turn predicted by features of the centroid pixel for a single nucleus. However, using the centroid pixel alone does not provide sufficient contextual information for robust prediction. To handle this problem, we propose a Context-aware Polygon Proposal Network (CPP-Net) for nucleus segmentation. First, we sample a point set rather than one single pixel within each cell for distance prediction. This strategy substantially enhances contextual information and thereby improves the robustness of the prediction. Second, we propose a Confidence-based Weighting Module, which adaptively fuses the predictions from the sampled point set. Third, we introduce a novel Shape-Aware Perceptual (SAP) loss that constrains the shape of the predicted polygons. Here, the SAP loss is based on an additional network that is pre-trained by means of mapping the centroid probability map and the pixel-to-boundary distance maps to a different nucleus representation. Extensive experiments justify the effectiveness of each component in the proposed CPP-Net. Finally, CPP-Net is found to achieve state-of-the-art performance on three publicly available databases, namely DSB2018, BBBC06, and PanNuke. Code of this paper will be released

Self-Templated Synthesis of Triphenylene-Based Uniform Hollow Spherical Two-Dimensional Covalent Organic Frameworks for Drug Delivery

Constructing two-dimensional covalent organic frameworks (2DCOFs) with a desirable crystalline structure and morphology is promising but remains a significant challenge. Herein, we report self-templated synthesis of uniform hollow spherical 2DCOFs based on 2,3,6,7,10,11-hexakis(4-aminophenyl) triphenylene. A detailed time-dependent study of hollow sphere formation reveals an intriguing transformation from initial homogeneous solid spheres into uniform hollow spheres with the Ostwald ripening mechanism. Impressively, the resultant spherical 2DCOFs are composed of high crystallinity nanosheets and even hexagonal single crystals, as demonstrated by transmission electron microscopy. Thanks to its uniform morphology and high crystallinity, the pore volume of the obtained 2DCOFs is up to 1.947 cm3 g–1, which makes it function as superior nanocarriers for efficient controlled drug delivery. This result provides an avenue for improving COFs’ performance by regulating their morphology