AIMC Topic: Image Processing, Computer-Assisted

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A dataset of microscopic spirometra mansoni for medical image segmentation.

Computers in biology and medicine
Accurate diagnosis of adult Spirometra mansoni infections remains challenging, due to the limited sensitivity and high cost associated with immunodiagnostic methods. Advances in computer vision suggest deep learning-based etiological image analysis c...

Enhancing cancer diagnostics through a novel deep learning-based semantic segmentation algorithm: A low-cost, high-speed, and accurate approach.

Computers in biology and medicine
Deep learning-based semantic segmentation approaches provide an efficient and automated means for cancer diagnosis and monitoring, which is important in clinical applications. However, implementing these approaches outside the experimental environmen...

Streamlining tuberculosis detection with foundation model-based weakly supervised transformer.

Computers in biology and medicine
Tuberculosis (TB) remains a major global health challenge, particularly in low- and middle-income countries. Traditional microscopy-based diagnostics are labor-intensive and error-prone, while automated deep learning models often require detailed exp...

Integrating multi-source data for skin burn classification using deep learning.

Computers in biology and medicine
BACKGROUND: Skin burns result from thermal or chemical damage to the skin, requiring timely and accurate assessment for effective treatment. Determining the degree of burns is crucial for appropriate clinical decisions, especially for interventions l...

Development of an anomaly detection system for Gibbs artifact identification in amyloid PET imaging.

Radiological physics and technology
The PET Imaging Site Qualification Program for amyloid positron emission tomography (PET) in Japan includes visual evaluation of the cylinder phantom. This visual evaluation requires observation of the entire image of the phantom and confirmation of ...

Towards a comprehensive characterization of arteries and veins in retinal imaging.

Computers in biology and medicine
Retinal fundus imaging is crucial for diagnosing and monitoring eye diseases, which are often linked to systemic health conditions such as diabetes and hypertension. Current deep learning techniques often narrowly focus on segmenting retinal blood ve...

Refining cardiac segmentation from MRI volumes with CT labels for fine anatomy of the ascending aorta.

Radiological physics and technology
Magnetic resonance imaging (MRI) is time-consuming, posing challenges in capturing clear images of moving organs, such as cardiac structures, including complex structures such as the Valsalva sinus. This study evaluates a computed tomography (CT)-gui...

Automated quantitative analysis of peri-articular bone microarchitecture in HR-pQCT knee images.

Computer methods and programs in biomedicine
UNLABELLED: Applying HR-pQCT to image the knee necessitates the development and validation of novel image analysis workflows. Here, we present and validate the first automated workflow for in vivo quantitative assessment of peri-articular bone densit...

Kernelized weighted local information based picture fuzzy clustering with multivariate coefficient of variation and modified total Bregman divergence measure for brain MRI image segmentation.

Computers in biology and medicine
This paper proposes a novel clustering method for noisy image segmentation using a kernelized weighted local information approach under the Picture Fuzzy Set (PFS) framework. Existing kernel-based fuzzy clustering methods struggle with noisy environm...

Artery fragment guided approach for enhancing cerebral aneurysm detection in TOF-MRA imaging.

Computer methods and programs in biomedicine
BACKGROUND: Cerebral aneurysms are a type of cerebrovascular disease that poses a severe threat to life and health. Early screening using Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) can effectively reduce the risk of rupture. Despite the ...