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.

图像标注是医学成像和疾病诊断领域中确保患者适当治疗和追踪疗程进展的最重要任务之一。通过使用深度学习的分割算法和主动学习方法，MedDeepCyleAL 提供了一个完整的端到端框架，可自动化图像标注并在眼科数据方面表现出优越性能。