The state-of-the-art interactive image segmentation algorithms are sensitive to the user inputs and often unable to produce an accurate boundary with a small amount of user interaction. They frequently rely on laborious user editing to refine the segmentation boundary. In this paper, we propose a robust and accurate interactive method based on the recently developed continuous-domain convex active contour model. The proposed method exhibits many desirable properties of an effective interactive image segmentation algorithm, including robustness to user inputs and different initializations, the ability to produce a smooth and accurate boundary contour, and the ability to handle topology changes. Experimental results on a benchmark data set show that the proposed tool is highly effective and outperforms the state-of-the-art interactive image segmentation algorithms.
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http://dx.doi.org/10.1109/TIP.2012.2191566 | DOI Listing |
Eur J Nucl Med Mol Imaging
January 2025
Institute of Radiation Medicine, Fudan University, Xietu Road 2094, Shanghai, 200032, China.
Objectives: Mesothelin (MSLN) is an antigen that is overexpressed in various cancers, and its interaction with tumor-associated cancer antigen 125 plays a multifaceted role in tumor metastasis. The serum MSLN expression level can be detected using enzyme-linked immunosorbent assay; however, non-invasive visualization of its expression at the tumor site is currently lacking. Therefore, the aim of this study was to develop a molecular probe for imaging MSLN expression through positron emission tomography (PET).
View Article and Find Full Text PDFElectromagn Biol Med
January 2025
Department of Mathematics, University of Gour Banga, Malda, India.
In cardiovascular research, electromagnetic fields generated by Riga plates are utilized to study or manipulate blood flow dynamics, which is particularly crucial in developing treatments for conditions such as arterial plaque deposition and understanding blood behavior under varied flow conditions. This research predicts the flow patterns of blood enhanced with gold and maghemite nanoparticles (gold-maghemite/blood) in an electromagnetic microchannel influenced by Riga plates with a temperature gradient that decays exponentially, under sudden changes in pressure gradient. The flow modeling includes key physical influences like radiation heat emission and Darcy drag forces in porous media, with the flow mathematically represented through unsteady partial differential equations solved using the Laplace transform (LT) method.
View Article and Find Full Text PDFBioconjug Chem
January 2025
Department of Physics, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India.
Silica nano/microparticles have generated significant interest for the past decades, emerging as a versatile material with a wide range of applications in photonic crystals, bioimaging, chemical sensors, and catalysis. This study focused on synthesizing silica nano/microparticles ranging from 20 nm to 1.2 μm using the Stöber and modified Stöber methods.
View Article and Find Full Text PDFPhysiol Rep
February 2025
Department of Biomedical Engineering, Toyo University, Saitama, Japan.
The present study aims to examine the effect of 4 h of continuous sitting on cerebral endothelial function, which is a crucial component of cerebral blood flow regulation. We hypothesized that 4 h of sitting may impair cerebral endothelial function similarly to how it affects lower limb vasculature. Thirteen young, healthy participants were instructed to remain seated for 4 h without moving their lower limbs.
View Article and Find Full Text PDFPhysiol Rep
February 2025
Motion and Exercise Science, University of Stuttgart, Stuttgart, Germany.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training.
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