Abstract
As 3D navigated bronchoscopy is increasingly used for the biopsy and treatment of peripherally located lung cancer lesions, accurate segmentation of distal small airways plays an important role in both pre- and intra-operative navigation. When adopting CNN-based methods in this task, the gradients to these peripheral branches may disappear before arriving at the bottom layers. Firstly, this is closely related to the ratio of the foreground gradient to the background gradient. Generally, small ratios can lead to the erosion of the surface while the consequence is more serious for the distal small airways. To accurately segment the branches of different sizes, we propose a local-imbalance-based weight that adjusts the gradient ratios according to the quantification of local class imbalance. In addition, if the features of some under-represented areas are not learned in the first few epochs, the gradients to these regions may be filtered out by the last activation layer in the following training. To resolve this problem, we propose in this paper a BP-based weight enhancement strategy that restarts the training with refined weight maps. The largest connected domain in our results achieves a tree length detected rate of \(95\%\) with a precision of \(92\%\) in the Binary Airway Segmentation Dataset. The code is publicly available at https://github.com/haozheng-sjtu/Local-imbalance-based-Weight.
This research was partly supported by National Key R&D Program of China (No. 2019YFB1311503), Committee of Science and Technology, Shanghai, China (No. 19510711200), Shanghai Sailing Program (No. 20YF1420800), National Nature Science Foundation of China (No. 62003208), Shanghai Municipal of Science and Technology Project (No. 20JC1419500), and Science and Technology Commission of Shanghai Municipality (No. 20DZ2220400).
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Zheng, H. et al. (2021). Refined Local-imbalance-based Weight for Airway Segmentation in CT. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12901. Springer, Cham. https://doi.org/10.1007/978-3-030-87193-2_39
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