P2ED: A four-quadrant framework for progressive prompt enhancement in 3D interactive medical imaging segmentation

• Published in: Neural networks 2025-03, Vol.183, Article 106973
• Main Authors: Chang, Ao, Tao, Xing, Huang, Yuhao, Yang, Xin, Zeng, Jiajun, Zhou, Xinrui, Huang, Ruobing, Ni, Dong
• Format: Article
• Language: English
• Subjects: 3D interactive segmentation; Medical image segmentation; Prompt learning
• ISSN: 0893-6080
• DOI: 10.1016/j.neunet.2024.106973

Interactive segmentation allows active user participation to enhance output quality and resolve ambiguities. This may be especially indispensable to medical image segmentation to address complex anatomy and customization to varying user requirements. Existing approaches often encounter issues such as information dilution, limited adaptability to diverse user interactions, and insufficient response. To address these challenges, we present a novel 3D interactive framework P2ED that divides the task into four quadrants. It is equipped with a multi-granular prompt encrypted to extract prompt features from various hierarchical levels, along with a progressive hierarchical prompt decrypter to adaptively heighten the attention to the scarce prompt features along three spatial axes. Finally, it is appended by a calibration module to further align the prediction with user intentions. Extensive experiments demonstrate that the proposed P2ED achieves accurate results with fewer user interactions compared to state-of-the-art methods and is effective in promoting the upper limit of segmentation performance. The code will be released in https://github.com/chuyhu/P2ED.