2 research outputs found
Early detection of hip periprosthetic joint infections through CNN on Computed Tomography images
Early detection of an infection prior to prosthesis removal (e.g., hips,
knees or other areas) would provide significant benefits to patients.
Currently, the detection task is carried out only retrospectively with a
limited number of methods relying on biometric or other medical data. The
automatic detection of a periprosthetic joint infection from tomography imaging
is a task never addressed before. This study introduces a novel method for
early detection of the hip prosthesis infections analyzing Computed Tomography
images. The proposed solution is based on a novel ResNeSt Convolutional Neural
Network architecture trained on samples from more than 100 patients. The
solution showed exceptional performance in detecting infections with an
experimental high level of accuracy and F-score
Boosting multiple sclerosis lesion segmentation through attention mechanism
Magnetic resonance imaging is a fundamental tool to reach a diagnosis of
multiple sclerosis and monitoring its progression. Although several attempts
have been made to segment multiple sclerosis lesions using artificial
intelligence, fully automated analysis is not yet available. State-of-the-art
methods rely on slight variations in segmentation architectures (e.g. U-Net,
etc.). However, recent research has demonstrated how exploiting temporal-aware
features and attention mechanisms can provide a significant boost to
traditional architectures. This paper proposes a framework that exploits an
augmented U-Net architecture with a convolutional long short-term memory layer
and attention mechanism which is able to segment and quantify multiple
sclerosis lesions detected in magnetic resonance images. Quantitative and
qualitative evaluation on challenging examples demonstrated how the method
outperforms previous state-of-the-art approaches, reporting an overall Dice
score of 89% and also demonstrating robustness and generalization ability on
never seen new test samples of a new dedicated under construction dataset