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.

翻译：医学影像是指用于观察人体及其内部结构以诊断、监测乃至治疗疾病的技术与方法。本文旨在探讨深度学习技术在心脏短轴磁共振成像（MRI）语义分割中的应用，以提升心脏相关疾病的诊断、监测与治疗水平。研究重点在于实现多种基于U-Net衍生的架构，以有效分离心脏的特定区域，从而进行全面的解剖与功能分析。通过结合图像、图表及量化指标，展示了各模型及其预测结果的有效性。此外，本文还探讨了所面临的挑战，并概述了未来改进策略。本摘要简要概述了利用深度学习进行心脏图像分割的研究工作，既总结了现有成果，也指出了有待优化的方向。