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、监测甚至治疗疾病的技术与方法。本文旨在探索深度学习技术在心脏短轴MRI（磁共振成像）图像语义分割中的应用，以提升心脏相关疾病的诊断、监测与治疗水平。研究重点聚焦于实现多种基于U-Net衍生架构的模型，以有效分离心脏特定区域，从而实现全面的解剖与功能分析。通过图像、图表及量化指标的整合，展示了模型的效能及其预测结果。此外，本文探讨了当前面临的挑战，并提出了未来优化的策略思路。本摘要为利用深度学习进行心脏图像分割的研究工作提供了简明概述，既总结了当前成果，也指出了需要进一步改进的方向。