Abstract

Medical image segmentation is pivotal in modern healthcare for precise delineation of anatomical structures such as the gall bladder, kidney, liver, and spleen, facilitating accurate diagnostics and treatment planning. Deep learning, particularly convolutional neural networks (CNNs), has revolutionized this field by automating segmentation tasks with high accuracy and efficiency. This paper explores the application of deep learning techniques specific to these organs, addressing challenges like anatomical variability and image complexity. Advanced methodologies including attention mechanisms and contextual aggregation are integrated to enhance segmentation performance. The study leverages large annotated datasets to train and evaluate state-of-the-art segmentation models, aiming to improve clinical outcomes through reliable organ segmentation.