TL;DR
F focal-modulation-driven global context with deformable-convolution-based local adaptation produces boundary-refined pancreatic tumor segmentations on CT, which improves boundary fidelity and provides a transparent layer of review that may support treatment planning and longitudinal monitoring.
Accurate kidney and renal-tumor segmentation is challenging because lesion size, location, morphology, and boundary contrast vary substantially across abdominal CT scans. Most existing methods rely on a single form of local evidence and struggle to recover the boundaries of small lesions while maintaining global anat...
DE-GAN is proposed, a contrast-enhancing conditional GAN that combines input-adaptive dynamic convolutions, style-aware feature mixing, and coordinate encoding to synthesize slice-adaptive FLAIR images to synthesize slice-adaptive FLAIR images.
MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.
VGG16-MCA UNet is presented, a hybrid architecture pairing an ImageNet-pretrained VGG16 encoder with a decoder in which a Multi-Channel Attention (MCA) module recalibrates features after each skip-connection fusion, trained with the Focal Tversky loss to counter severe foreground-background imbalance.
SA-Net, a dual-branch CNN–GNN Network that integrates synergistic attention and frequency-domain modulation, provides accurate and robust segmentation performance under challenging ultrasound imaging conditions.
Abdominal medical image segmentation is essential for computer-aided diagnosis, lesion localization, quantitative organ analysis, and treatment planning. However, abdominal computed tomography (CT) images often contain large anatomical variations, complex tissue structures, low contrast, blurred boundaries, and small t...
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