Implicit Shape Modeling for Anatomical Structure Refinement of Volumetric Medical Images

Shape modeling of volumetric data is essential for medical image analysis and computer-aided intervention. In practice, automated shape reconstruction cannot always achieve satisfactory results due to limited image resolution and a lack of sufficiently detailed shape priors used as constraints. In this paper, a unified framework is proposed for 3D shape modelling and segmentation refinement based on implicit neural networks. To learn a sharable shape prior from different instances within the same category during training, physical details of volumetric data are firstly used to construct Physical-Informed Continuous Coordinate Transform (PICCT) for implicit shape modeling. For improved shape representation, implicit shape constraints based on Signed Distance Function (SDF) are used for both instances and latent templates. For inference, a Template Interaction Module (TIM) is proposed to refine 3D shapes produced by Convolutional Neural Networks (CNNs) via deforming deep implicit templates with latent codes. Experimental results on validation datasets involving liver, pancreas and lung segmentation demonstrate the superiority of our approach in shape refinement and reconstruction. The Chamfer Distance/Earth Mover's Distance achieved by the proposed method are 0.232/0.087 for the Liver dataset, 0.128/0.069 for the Pancreas dataset, and 0.417/0.100 for the Lung Lobe dataset, respectively

A Historical Sedimentary Record of Mercury in a Shallow Eutrophic Lake: Impacts of Human Activities and Climate Change

Mercury and its derivatives are hazardous environmental pollutants and could affect the aquatic ecosystems and human health by biomagnification. Lake sediments can provide important historical information regarding changes in pollution levels and thus trace anthropogenic or natural influences. This research investigates the 100-year history of mercury (Hg) deposition in sediments from Chao Lake, a shallow eutrophic lake in China. The results indicate that the Hg deposition history can be separated into three stages (pre-1960s, 1960s–1980s, and post-1980s) over the last 100 years. Before the 1960s, Hg concentrations in the sediment cores varied little and had no spatial difference. Since the 1960s, the concentration of Hg began to increase gradually, and showed a higher concentration of contamination in the western half of the lake region than in the eastern half of the lake region due to all kinds of centralized human-input sources. The influences of anthropogenic factors and hydrological change are revealed by analyzing correlations between Hg and heavy metals (Fe, Co, Cr, Cu, Mn, Pb, and Zn), stable carbon and nitrogen isotopes (δ13C and δ15N), nutrients, particle sizes, and meteorological factors. The results show that Hg pollution intensified after the 1960s, mainly due to hydrological change, rapid regional development and urbanization, and the proliferation of anthropogenic Hg sources. Furthermore, the temperature, wind speed, and evaporation are found to interactively influence the environmental behaviors and environmental fate of Hg