Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation
This work addresses the problem of efficient and accurate annotation for medical imaging, specifically for COVID-19 lung lesions, with incremental improvements over existing interactive segmentation methods.
The paper tackles the challenge of interactive segmentation for ambiguous and noisy CT data of COVID-19 lung lesions by proposing an adaptive multi-scale online likelihood network (MONet), which achieved a 5.86% higher Dice score and 24.67% lower perceived workload compared to the state-of-the-art.
Existing interactive segmentation methods leverage automatic segmentation and user interactions for label refinement, significantly reducing the annotation workload compared to manual annotation. However, these methods lack quick adaptability to ambiguous and noisy data, which is a challenge in CT volumes containing lung lesions from COVID-19 patients. In this work, we propose an adaptive multi-scale online likelihood network (MONet) that adaptively learns in a data-efficient online setting from both an initial automatic segmentation and user interactions providing corrections. We achieve adaptive learning by proposing an adaptive loss that extends the influence of user-provided interaction to neighboring regions with similar features. In addition, we propose a data-efficient probability-guided pruning method that discards uncertain and redundant labels in the initial segmentation to enable efficient online training and inference. Our proposed method was evaluated by an expert in a blinded comparative study on COVID-19 lung lesion annotation task in CT. Our approach achieved 5.86% higher Dice score with 24.67% less perceived NASA-TLX workload score than the state-of-the-art. Source code is available at: https://github.com/masadcv/MONet-MONAILabel