  Medical imaging refers to the technologies and methods utilized to view the
human body and its inside, in order to diagnose, monitor, or even treat medical
disorders. This paper aims to explore the application of deep learning
techniques in the semantic segmentation of Cardiac short-axis MRI (Magnetic
Resonance Imaging) images, aiming to enhance the diagnosis, monitoring, and
treatment of medical disorders related to the heart. The focus centers on
implementing various architectures that are derivatives of U-Net, to
effectively isolate specific parts of the heart for comprehensive anatomical
and functional analysis. Through a combination of images, graphs, and
quantitative metrics, the efficacy of the models and their predictions are
showcased. Additionally, this paper addresses encountered challenges and
outline strategies for future improvements. This abstract provides a concise
overview of the efforts in utilizing deep learning for cardiac image
segmentation, emphasizing both the accomplishments and areas for further
refinement.
