Image-based evaluation of lymph nodes is an essential step in cancer diagnosis, treatment and monitoring. Current clinical practice mostly uses qualitative or semi-quantitative measures in evaluation and thus suffers from inaccuracy due to intra- and inter-observer variability and increased human efforts. This becomes a more serious issue in head and neck cancers due to the large number of clinically relevant lymph nodes. In this project an AI-based automatic segmentation software will be developed for quantitative cervical lymph node evaluation to increase the accuracy and reduce the cost. However, there are a few challenges in developing and deploying such a software due to different clinical practices such as usage of different modalities (MRI and/or CT) and complex clinical workflow. To address these challenges, a novel AI algorithm that can handle the variability in imaging modalities and support incremental learning using site-specific data to enhance its robustness will be developed; a private-cloud-based software framework with high usability will then be developed to incorporate this algorithm and provide advanced visualization and reporting for clinical usage. This software will have high impact on all stages of patient care for head and neck cancers and can be further extended to other cancers.