10 research outputs found
A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-rays
Accurate teeth segmentation and orientation are fundamental in modern oral
healthcare, enabling precise diagnosis, treatment planning, and dental implant
design. In this study, we present a comprehensive approach to teeth
segmentation and orientation from panoramic X-ray images, leveraging deep
learning techniques. We build our model based on FUSegNet, a popular model
originally developed for wound segmentation, and introduce modifications by
incorporating grid-based attention gates into the skip connections. We
introduce oriented bounding box (OBB) generation through principal component
analysis (PCA) for precise tooth orientation estimation. Evaluating our
approach on the publicly available DNS dataset, comprising 543 panoramic X-ray
images, we achieve the highest Intersection-over-Union (IoU) score of 82.43%
and Dice Similarity Coefficient (DSC) score of 90.37% among compared models in
teeth instance segmentation. In OBB analysis, we obtain the Rotated IoU (RIoU)
score of 82.82%. We also conduct detailed analyses of individual tooth labels
and categorical performance, shedding light on strengths and weaknesses. The
proposed model's accuracy and versatility offer promising prospects for
improving dental diagnoses, treatment planning, and personalized healthcare in
the oral domain. Our generated OBB coordinates and codes are available at
https://github.com/mrinal054/Instance_teeth_segmentation
Integrated Image and Location Analysis for Wound Classification: A Deep Learning Approach
The global burden of acute and chronic wounds presents a compelling case for
enhancing wound classification methods, a vital step in diagnosing and
determining optimal treatments. Recognizing this need, we introduce an
innovative multi-modal network based on a deep convolutional neural network for
categorizing wounds into four categories: diabetic, pressure, surgical, and
venous ulcers. Our multi-modal network uses wound images and their
corresponding body locations for more precise classification. A unique aspect
of our methodology is incorporating a body map system that facilitates accurate
wound location tagging, improving upon traditional wound image classification
techniques. A distinctive feature of our approach is the integration of models
such as VGG16, ResNet152, and EfficientNet within a novel architecture. This
architecture includes elements like spatial and channel-wise
Squeeze-and-Excitation modules, Axial Attention, and an Adaptive Gated
Multi-Layer Perceptron, providing a robust foundation for classification. Our
multi-modal network was trained and evaluated on two distinct datasets
comprising relevant images and corresponding location information. Notably, our
proposed network outperformed traditional methods, reaching an accuracy range
of 74.79% to 100% for Region of Interest (ROI) without location
classifications, 73.98% to 100% for ROI with location classifications, and
78.10% to 100% for whole image classifications. This marks a significant
enhancement over previously reported performance metrics in the literature. Our
results indicate the potential of our multi-modal network as an effective
decision-support tool for wound image classification, paving the way for its
application in various clinical contexts
Event shapes in e+e- annihilation and deep inelastic scattering
This article reviews the status of event-shape studies in e+e- annihilation
and DIS. It includes discussions of perturbative calculations, of various
approaches to modelling hadronisation and of comparisons to data.Comment: Invited topical review for J.Phys.G; 40 pages; revised version
corrects some nomenclatur