Electron tomography is useful for studying large macromolecular complex within their cellular context. The associate problems include crowding and complexity. Data exploration and 3D visualization of complexes require rendering of tomograms as well as extraction of all features of interest. We present algorithms for fully automatic boundary segmentation and skeletonization, and demonstrate their applications in feature extraction and visualization of cell and molecular tomographic imaging. We also introduce an interactive volumetric exploration and visualization tool (Volume Rover), which encapsulates implementations of the above volumetric image processing algorithms, and additionally uses efficient multi-resolution interactive geometry and volume rendering techniques for interactive visualization.
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http://dx.doi.org/10.1016/j.jsb.2003.09.037 | DOI Listing |
Parasit Vectors
January 2025
Faculty of Information Technology, Mutah University, Mutah, Jordan.
Background: Amebiasis represents a significant global health concern. This is especially evident in developing countries, where infections are more common. The primary diagnostic method in laboratories involves the microscopy of stool samples.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Statistics and Data Science, Jahangirnagar University, Dhaka, 1342, Bangladesh.
Background: Child mortality is a reliable and significant indicator of a nation's health. Although the child mortality rate in Bangladesh is declining over time, it still needs to drop even more in order to meet the Sustainable Development Goals (SDGs). Machine Learning models are one of the best tools for making more accurate and efficient forecasts and gaining in-depth knowledge.
View Article and Find Full Text PDFBMC Cancer
January 2025
Department of Urology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Background: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
Materials And Methods: The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas.
Mol Biol Rep
January 2025
Department of Clinical Pathology, Faculty of Medicine, Alexandria University, Alexandria, Egypt.
Background: The identification of circulating potential biomarkers may help earlier diagnosis of breast cancer, which is critical for effective treatment and better disease outcomes. We aimed to study the role of circ-FAF1 as a diagnostic biomarker in female breast cancer using peripheral blood samples of these patients, and to investigate the relation between circ-FAF1 and different clinicopathological features of the included patients.
Methods And Results: This case-control study enrolled 60 female breast cancer patients and 60 age-matched healthy control subjects.
Eur Radiol
January 2025
Department of Ultrasound, Chengdu Second People's Hospital, Chengdu, China.
Objectives: This study aimed to develop a multimodal radiopathomics model utilising preoperative ultrasound (US) and fine-needle aspiration cytology (FNAC) to predict large-number cervical lymph node metastasis (CLNM) in patients with clinically lymph node-negative (cN0) papillary thyroid carcinoma (PTC).
Materials And Methods: This multicentre retrospective study included patients with PTC between October 2017 and June 2024 across seven institutions. Patients were categorised based on the presence or absence of large-number CLNM in training, validation, and external testing cohorts.
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