AIMC Topic: Image Processing, Computer-Assisted

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A novel deep learning framework for retinal disease detection leveraging contextual and local features cues from retinal images.

Medical & biological engineering & computing
Retinal diseases are a serious global threat to human vision, and early identification is essential for effective prevention and treatment. However, current diagnostic methods rely on manual analysis of fundus images, which heavily depends on the exp...

Evaluation of deep learning-based scatter correction on a long-axial field-of-view PET scanner.

European journal of nuclear medicine and molecular imaging
OBJECTIVE: Long-axial field-of-view (LAFOV) positron emission tomography (PET) systems allow higher sensitivity, with an increased number of detected lines of response induced by a larger angle of acceptance. However this extended angle increases the...

Fine-scale striatal parcellation using diffusion MRI tractography and graph neural networks.

Medical image analysis
The striatum, a crucial part of the basal ganglia, plays a key role in various brain functions through its interactions with the cortex. The complex structural and functional diversity across subdivisions within the striatum highlights the necessity ...

Deep graph embedding based on Laplacian eigenmaps for MR fingerprinting reconstruction.

Medical image analysis
Magnetic resonance fingerprinting (MRF) is a promising technique for fast quantitative imaging of multiple tissue parameters. However, the highly undersampled schemes utilized in MRF typically lead to noticeable aliasing artifacts in reconstructed im...

A comprehensive analysis of deep learning and transfer learning techniques for skin cancer classification.

Scientific reports
Accurately and early diagnosis of melanoma is one of the challenging tasks due to its unique characteristics and different shapes of skin lesions. So, in order to solve this issue, the current study examines various deep learning-based approaches and...

Deep learning aided determination of the optimal number of detectors for photoacoustic tomography.

Biomedical physics & engineering express
Photoacoustic tomography (PAT) is a non-destructive, non-ionizing, and rapidly expanding hybrid biomedical imaging technique, yet it faces challenges in obtaining clear images due to limited data from detectors or angles. As a result, the methodology...

A dual-domain network with division residual connection and feature fusion for CBCT scatter correction.

Physics in medicine and biology
This study aims to propose a dual-domain network that not only reduces scatter artifacts but also retains structure details in cone-beam computed tomography (CBCT).The proposed network comprises a projection-domain sub-network and an image-domain sub...

Med-ImageTools: An open-source Python package for robust data processing pipelines and curating medical imaging data.

F1000Research
BACKGROUND: Machine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train computational models that can be implemented in clinical practice. However, processing large ...

Diagnosis of Sacroiliitis Through Semi-Supervised Segmentation and Radiomics Feature Analysis of MRI Images.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Sacroiliitis is a hallmark of ankylosing spondylitis (AS), and early detection plays an important role in managing the condition effectively. MRI is commonly used for diagnosing sacroiliitis, traditional methods often depend on subjective...

Optimizing MR-based attenuation correction in hybrid PET/MR using deep learning: validation with a flatbed insert and consistent patient positioning.

European journal of nuclear medicine and molecular imaging
PURPOSE: To address the challenges of verifying MR-based attenuation correction (MRAC) in PET/MR due to CT positional mismatches and alignment issues, this study utilized a flatbed insert and arms-down positioning during PET/CT scans to achieve preci...