Teeth segmentation is a crucial and fundamental player for doctors in diagnosis and treatment planning in dentistry. Due to the blurred interdental boundaries, variations in noise, and the complexities arising from the orientation and overlapping of dental structures within oral images, the segmentation process becomes extremely challenging and time-consuming. Nowadays, computational tools have been introduced as promising strategies for automating teeth segmentation. As one of them, this paper presents a novel architecture called LCAT-Net, designed to address these challenges and improve teeth segmentation in panoramic X-rays. The proposed architecture incorporates several components to address the above challenges. Firstly, it leverages the main components of the Half-UNet for lightweight feature extraction, starting from ghost modules, unified channel numbers, and full-scale feature fusion. Secondly, to give our model the ability to focus on critical regions for improved differentiation in complex dental structures, a convolutional block attention module (CBAM) is integrated into the network. Thirdly, the architecture incorporates a novel multi-scale context fusion (MCF) module, our proposed MCF module extracts multi-scale spatial information through a spatial context fusion (SCF) block, followed by a CBAM block that learns to balance channel-wise features. The network uses a Dense skip connection module (DSM) to reduce the semantic gap. Experiments on three dental panoramic X-ray image datasets of Children, Adults, and Combined (Children and Adults) consisting of 193, 1776, and 1969 X-rays show that our model outperformed the SOTA models in teeth segmentation, with a high mean Dice-scores of 0.9235, 0.9444, 0.9405, respectively. While requiring significantly fewer parameters and floating-point operations (FLOPs) than existing methods.

Accurate retinal vessel segmentation from eye fundus images is a major challenge in automated methods. U-Net has been widely used in medical image segmentation, and its effectiveness has led researchers to propose various U-Net variants. However, most of these variants are resource-intensive, suggesting that further development is needed. This study introduces a novel approach called SE-Half-UNet, which derives from the Half-UNet framework by incorporating the Squeeze-and-Excitation (SE) Module to bolster the network’s representative capacity by emphasizing channel relationships within feature maps. Our method achieves superior performance in retinal vessel segmentation, with a high F1-Score of 0.8303 and 0.8372 for DRIVE and CHASE_DB datasets, respectively. This new method facilitates the accurate analysis of eye images and encourages computer research advances.

In medical image analysis, retinal blood vessels segmentation is essential for the diagnosis and treatment of a variety of retinal diseases. This study introduces Atrous-Ghost Half-UNet (AGH-UNet), an advanced architecture of the Half-UNet architecture designed for enhanced retinal vessel segmentation. Our approach incorporates two primary innovations: Firstly, we integrate Atrous convolutions into the ghost module of the Half-UNet framework, replacing standard convolutions. This modification expands the receptive field without compromising spatial resolution, improving the capacity of the model in detecting large and small blood vessels with high accuracy. Secondly, we implement an Enhanced Mixing Loss (EML) function, adapted from the D-UNet model. The EML function addresses class imbalance issues by combining a weighted focal coefficient with a conventional loss term. By incorporating this loss function into our model, we aim to improve segmentation accuracy, particularly in scenarios involving imbalanced vessel structures or small vessels that are typically challenging to identify. Experimental results demonstrate that our proposed method achieves superior segmentation performance compared to the standard Half-UNet architecture. This research contributes to advancing retinal vessel segmentation techniques, potentially enhancing the efficacy of diagnostic and treatment processes for retinal pathologies.