Medical image segmentation involves identifying and delineating regions of interest within medical images. Given an input medical image, the task is to output a corresponding mask highlighting specific anatomical structures or abnormalities. Participants are provided with a labeled dataset of CT images and their associated masks and are challenged to develop models capable of accurate segmentation. Techniques such as convolutional neural networks (CNNs), U-Net architectures, and data augmentation are encouraged to improve performance. The evaluation focuses on the model's ability to generalize and accurately segment unseen images.