Objective: The aim of the study was to evaluate the value of the single photon emission computed tomography/computed tomography (SPECT/CT) fusion imaging in the diagnosis of benign and malignant lesions in bones.
Methods: One hundred and forty one bone lesions of 125 cancer patients, for whom the natures of the lesions were not able to be determined by the 99Tc(m)-MDP whole-body bone scan, were examined by the SPECT, CT and SPECT/CT fusion imaging simultaneously. All of the images were blindly interpreted independently by two experienced nuclear medicine physicians. The natures of the lesions were eventually confirmed by MRI, pathology or follow-up diagnosis six months later.
Results: The diagnostic sensitivity of SPECT, SPECT+CT and SPECT/CT for the 141 bone lesions was 82.5%, 93.7%, and 98.4% respectively. The specificity was 66.7%, 80.8%, and 93.6% respectively. The accuracy was 73.8%, 86.5%, and 95.7% respectively. The specificity and accuracy of SPECT/CT for diagnosing bone lesions were significantly higher than those of SPECT and SPECT+CT (P<0.05).
Conclusion: SPECT/CT can effectively differentiate benign and malignant bone lesions.
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Digit Biomark
December 2024
Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI, USA.
Introduction: This research is focused on early detection of Alzheimer's disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images.
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Front Plant Sci
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College of Big Data, Yunnan Agricultural University, Kunming, China.
Introduction: Weeds are a major factor affecting crop yield and quality. Accurate identification and localization of crops and weeds are essential for achieving automated weed management in precision agriculture, especially given the challenges in recognition accuracy and real-time processing in complex field environments. To address this issue, this paper proposes an efficient crop-weed segmentation model based on an improved UNet architecture and attention mechanisms to enhance both recognition accuracy and processing speed.
View Article and Find Full Text PDFJ Orthop Surg Res
January 2025
Department of Spine Surgery, Binzhou Medical University Hospital, No. 661, Huanghe Er Road, Binzhou, 256603, Shandong, China.
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View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia.
Background: Due to the complexity and cost of preparing histopathological slides, deep learning-based methods have been developed to generate high-quality histological images. However, existing approaches primarily focus on spatial domain information, neglecting the periodic information in the frequency domain and the complementary relationship between the two domains. In this paper, we proposed a generative adversarial network that employs a cross-attention mechanism to extract and fuse features across spatial and frequency domains.
View Article and Find Full Text PDFMicrosc Res Tech
January 2025
Department of Physics, National Institute of Technology Silchar, Silchar, Assam, India.
Red blood cells (RBCs) or Erythrocytes are essential components of the human body and they transport oxygen from the lungs to the body's tissues, regulate balance, and support the immune system. Abnormalities in RBC shapes (Poikilocytosis) and sizes (Anisocytosis) can impede oxygen-carrying capacity, leading to conditions such as anemia, thalassemia, McLeod Syndrome, liver disease, and so on. Hematologists typically spend considerable time manually examining RBC's shapes and sizes using a microscope and it is time-consuming.
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