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

翻译：肺结节和肿块是肺癌筛查中的关键影像学特征，在临床诊断中需谨慎处理。尽管基于深度学习的医学图像分割已取得显著成功，但针对结节和肿块等不同尺寸病变的鲁棒性能仍具挑战性。本文提出一种具备尺度感知测试时自适应能力的多尺度神经网络以应对该挑战。具体而言，我们引入了一种基于易获取的病变点击作为测试时线索的自适应尺度感知测试时点击自适应方法，从而提升分割性能，尤其对大型病变效果显著。该方法可无缝集成至现有网络中。在公开数据集和内部数据集上的大量实验一致表明，该方法优于部分基于CNN和Transformer的分割方法。我们的代码已开源至 https://github.com/SplinterLi/SaTTCA。