Fluid Segmentation

Fluid segmentation in medical imaging, particularly optical coherence tomography (OCT) scans of the retina and volumetric CT scans of the lungs, aims to automatically identify and delineate fluid regions within images to aid diagnosis and treatment planning. Current research emphasizes deep learning approaches, employing convolutional neural networks (CNNs) with attention mechanisms and transformer architectures like Swin Transformers to improve segmentation accuracy and robustness across different imaging modalities and devices. These advancements are crucial for improving the efficiency and accuracy of medical image analysis, enabling faster and more reliable diagnoses of conditions like macular edema and facilitating precise image-guided interventions.