Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation

• URL: http://arxiv.org/abs/2307.15645v1
• Date: Fri, 28 Jul 2023 16:04:34 GMT
• Title: Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation
• Authors: Zhihao Li, Jiancheng Yang, Yongchao Xu, Li Zhang, Wenhui Dong, and Bo Du
• Abstract summary: Pulmonary nodules and masses are crucial imaging features in lung cancer screening. Despite the success of deep learning-based medical image segmentation, the robust performance on various sizes of lesions is still challenging. We propose a multi-scale neural network with scale-aware test-time adaptation to address this challenge.
• Score: 35.381677272157866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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