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Water-fat separation is a non-linear non-convex parameter estimation problem in magnetic resonance imaging typically solved using spatial constraints. However, there is still limited knowledge on how to separate in vivo three chemical species in the presence of magnetic field inhomogeneities. The proposed method uses multiple graph-cuts in a hierarchical multi-resolution framework to perform robust chemical species separation in the breast for subjects with and without silicone implants. Experimental results show that the proposed method can decrease the computational time for water-fat separation and perform accurate water-fat-silicone separation with only a limited number of acquired echo images at 3 T. The silicone-separated images have an improved spatial resolution and image contrast compared to conventional scans used for regular monitoring of the silicone implant's integrity.
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http://dx.doi.org/10.1109/TMI.2022.3180302 | DOI Listing |
PLoS One
August 2025
Information Department, Jilin Qianwei Hospital, Changchun, China.
In medical imaging diagnosis, accurate segmentation of the knee joint can help doctors better observe and diagnose lesions, thereby improving diagnostic accuracy and treatment effectiveness. Vision Mamba mainly relies on the State Space Model (SSM) for feature modeling, which excels at capturing global contextual information but cannot capture local texture features. Moreover, features of different scales are not effectively integrated, resulting in the model's weak segmentation ability on small-scale tissues (such as cartilage areas).
View Article and Find Full Text PDFMed Image Anal
October 2025
Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Key Laboratory of Flexible Medical Robotics, Tongren Hospital, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address:
Robot-assisted microsurgery is a promising technique for a number of clinical specialties including neurosurgery. One of the prerequisites of such procedures is accurate vision guidance, delineating not only the exposed surface details but also embedded microvasculature. Conventional microscopic cameras used for vascular imaging are susceptible to specular reflections and changes in ambient light with low tissue resolution and contrast.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
June 2025
Medical image segmentation remains a challenging task due to the intricate nature of anatomical structures and the wide range of target sizes. In this paper, we propose a novel U -shaped segmentation network that integrates CNN and Transformer architectures to address these challenges. Specifically, our network architecture consists of three main components.
View Article and Find Full Text PDFbioRxiv
May 2025
Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Integrating spatially-resolved transcriptomics (SRT) across biological samples is essential for understanding dynamic changes in tissue architecture and cell-cell interactions . While tools exist for multisample single-cell RNA-seq, methods tailored to multisample SRT remain limited. Here, we introduce Popari, a probabilistic graphical model for factor-based decomposition of multisample SRT that captures condition-specific changes in spatial organization.
View Article and Find Full Text PDFMed Image Anal
July 2025
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China. Electronic address:
Accurate and efficient group-wise registration for medical images is fundamentally important to construct a common template image for population-level analysis. However, current group-wise registration faces the challenges posed by the algorithm's efficiency and capacity, and adaptability to large variations in the subject populations. This paper addresses these challenges with a novel Nested Hierarchical Group-wise Registration (NHGR) framework.
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