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.
Z. Li and J. Yang—Equal contributions.
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Acknowledgement
This work was supported by the National Key Research and Development Program of China (2018AAA0100400), and in part by the National Natural Science Foundation of China (under Grants 62225113, 62222112 and 62176186).
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Li, Z., Yang, J., Xu, Y., Zhang, L., Dong, W., Du, B. (2023). Scale-Aware Test-Time Click Adaptation for Pulmonary Nodule and Mass Segmentation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14222. Springer, Cham. https://doi.org/10.1007/978-3-031-43898-1_65
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