Image annotation is one of the most essential tasks for guaranteeing proper treatment for patients and tracking progress over the course of therapy in the field of medical imaging and disease diagnosis. However, manually annotating a lot of 2D and 3D imaging data can be extremely tedious. Deep Learning (DL) based segmentation algorithms have completely transformed this process and made it possible to automate image segmentation. By accurately segmenting medical images, these algorithms can greatly minimize the time and effort necessary for manual annotation. Additionally, by incorporating Active Learning (AL) methods, these segmentation algorithms can perform far more effectively with a smaller amount of ground truth data. We introduce MedDeepCyleAL, an end-to-end framework implementing the complete AL cycle. It provides researchers with the flexibility to choose the type of deep learning model they wish to employ and includes an annotation tool that supports the classification and segmentation of medical images. The user-friendly interface allows for easy alteration of the AL and DL model settings through a configuration file, requiring no prior programming experience. While MedDeepCyleAL can be applied to any kind of image data, we have specifically applied it to ophthalmology data in this project.

翻译：图像标注是确保患者获得恰当治疗并追踪治疗进程的关键任务之一，在医学影像与疾病诊断领域尤为重要。然而，手动标注大量二维和三维影像数据极为繁琐。基于深度学习的分割算法已彻底改变这一流程，使图像分割自动化成为可能。通过精准分割医学图像，这些算法能极大减少手动标注所需的时间与精力。此外，通过整合主动学习方法，这些分割算法可在更少标注数据条件下实现更高效性能。我们提出MedDeepCyleAL这一端到端框架，完整实现主动学习循环。该框架允许研究者灵活选择所需深度学习模型类型，并内置支持医学图像分类与分割的标注工具。用户可通过配置文件便捷调整主动学习与深度学习模型参数，无需编程经验。虽然MedDeepCyleAL可应用于任意图像数据，本项目特别将其应用于眼科医学数据。