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.

本文旨在探讨深度学习技术在心脏短轴 MRI 影像的语义分割中的应用，旨在增强与心脏相关的医疗疾病的诊断、监测和治疗，重点是实施各种派生于 U-Net 的体系结构，以有效地分离心脏的具体部分进行综合解剖和功能分析，并通过图像、图表和定量指标的结合来展示模型及其预测的效果，并讨论了遇到的挑战和未来改进的策略。这篇摘要简要概述了利用深度学习进行心脏图像分割的努力，强调了成就和进一步改进的领域。