13 research outputs found
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 HE
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