Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass
  Segmentation
Zhihao Li Jiancheng Yang Yongchao Xu Li Zhang Wenhui Dong Bo Du
Pulmonary nodules and masses are crucial imaging features in lung cancer
screening that require careful management in clinical diagnosis. Despite the
success of deep learning-based medical image segmentation, the robust
performance on various sizes of lesions of nodule and mass is still
challenging. In this paper, we propose a multi-scale neural network with
scale-aware test-time adaptation to address this challenge. Specifically, we
introduce an adaptive Scale-aware Test-time Click Adaptation method based on
effortlessly obtainable lesion clicks as test-time cues to enhance segmentation
performance, particularly for large lesions. The proposed method can be
seamlessly integrated into existing networks. Extensive experiments on both
open-source and in-house datasets consistently demonstrate the effectiveness of
the proposed method over some CNN and Transformer-based segmentation methods.
Our code is available at https://github.com/SplinterLi/SaTTCA