Spleen Segmentation

Spleen segmentation, the automated identification and delineation of the spleen in medical images (primarily CT and MRI scans), aims to improve diagnostic accuracy and streamline clinical workflows. Current research heavily utilizes deep learning, employing architectures like encoder-decoder networks, variational autoencoders, and vision transformers, often incorporating techniques like multi-scale feature extraction and attention mechanisms to handle the spleen's variable shape and size. This work is driven by the need for efficient and accurate spleen volumetry, crucial for assessing splenomegaly and related conditions, and is facilitated by the development of publicly available datasets with diverse image characteristics. Improved segmentation methods promise faster diagnosis, better treatment planning, and more reliable assessment of disease severity.