AIMC Topic: Image Processing, Computer-Assisted

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D2C-Morph: Brain regional segmentation based on unsupervised registration network with similarity analysis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jun 30, 2025
Brain regional segmentation is an image-processing approach widely used in brain image analyses. Deep learning models that perform segmentation alone play an important role in medical fields such as automatic diagnosis and prognosis prediction. This ...
Image Processing, Computer-Assisted Brain Unsupervised Machine Learning Deep Learning Humans Magnetic Resonance Imaging Algorithms
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Evaluation of meibomian gland dysfunction with deep learning model considering different datasets and gland morphology.

Computers in biology and medicine Jun 28, 2025
Meibomian gland dysfunction (MGD) is recognized as the primary cause of evaporative-type dry eye disease (DED). Diagnosis typically involves assessing meibomian gland (MG) morphology alongside symptom evaluation. Traditionally, experts manually grade...
Meibomian Glands Meibomian Gland Dysfunction Middle Aged Deep Learning Image Processing, Computer-Assisted Dry Eye Syndromes Adult Male Humans Female Databases, Factual Aged
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Radio DINO: A foundation model for advanced radiomics and AI-driven medical imaging analysis.

Computers in biology and medicine Jun 28, 2025
Radiomics is transforming medical imaging by extracting complex features that enhance disease diagnosis, prognosis, and treatment evaluation. However, traditional approaches face significant challenges, such as the need for manual feature engineering...
Humans Databases, Factual Image Processing, Computer-Assisted Diagnostic Imaging Radiomics Deep Learning
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DeepEM Playground: Bringing deep learning to electron microscopy labs.

Journal of microscopy Jun 28, 2025
Deep learning (DL) has transformed image analysis, enabling breakthroughs in segmentation, object detection, and classification. However, a gap persists between cutting-edge DL research and its practical adoption in electron microscopy (EM) labs. Thi...
Deep Learning Humans Image Processing, Computer-Assisted Microscopy, Electron
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FiBar: A tool for analyzing fiber diameters in complex drug delivery systems from scanning electron microscopy images.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences Jun 27, 2025
Electrospinning increases opportunities to facilitate the production of drug delivery systems (DDSs), such as complex biomaterials. However, the manual measurement of fiber diameters remains a critical bottleneck, hindering efficiency and scalability...
Software Microscopy, Electron, Scanning Nanofibers Drug Delivery Systems Image Processing, Computer-Assisted
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D-RD-UNet: A dual-stage dual-class framework with connectivity correction for hepatic vessels segmentation.

Computers in biology and medicine Jun 27, 2025
Accurate segmentation of hepatic and portal veins is critical for preoperative planning in liver surgery, especially for resection and transplantation procedures. Extensive anatomical variability, pathological alterations, and inherent class imbalanc...
Liver Portal Vein Imaging, Three-Dimensional Image Processing, Computer-Assisted Humans Algorithms Tomography, X-Ray Computed Hepatic Veins Deep Learning
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Self-supervised suppression of MRI cardiac device artifacts based on multi-instance contrastive learning and anisotropic spatiotemporal transformer.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jun 25, 2025
Cardiovascular implantable electronic devices (CIEDs) induce severe off-resonance artifacts in balanced steady-state free precession (bSSFP) cine MRI, limiting diagnostic utility for a growing patient population. While supervised and unpaired learnin...
Supervised Machine Learning Artifacts Anisotropy Magnetic Resonance Imaging, Cine Algorithms Image Processing, Computer-Assisted Humans
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A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images.

Biomedical engineering online Jun 25, 2025
OBJECTIVE: To investigate the potential of a hybrid multi-instance learning model (TGMIL) combining Transformer and graph attention networks for classifying gastric adenocarcinoma differentiation on whole-slide images (WSIs) without manual annotation...
Adenocarcinoma Stomach Neoplasms Humans Image Processing, Computer-Assisted Machine Learning
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CATransU-Net: Cross-attention TransU-Net for field rice pest detection.

PloS one Jun 25, 2025
Accurate detection of rice pests in field is a key problem in field pest control. U-Net can effectively extract local image features, and Transformer is good at dealing with long-distance dependencies. A Cross-Attention TransU-Net (CATransU-Net) mode...
Insecta Neural Networks, Computer Pest Control Algorithms Image Processing, Computer-Assisted Animals Oryza
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PoseNet++: A multi-scale and optimized feature extraction network for high-precision human pose estimation.

PloS one Jun 25, 2025
Human pose estimation (HPE) has made significant progress with deep learning; however, it still faces challenges in handling occlusions, complex poses, and complex multi-person scenarios. To address these issues, we propose PoseNet++, a novel approac...
Posture Algorithms Deep Learning Image Processing, Computer-Assisted Humans Neural Networks, Computer
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