Abstract
The segmentation of malignant nodules is crucial to pre-operative planning, while it adhere to lung tissue extremely, which leads to false positive too high. Considering inaccurate segmentation of adhesive pulmonary nodules, Dual prior-guided Astrous Convolutional Network (DPACN) is proposed to achieve coarse-to-fine nodules segmentation. In view of spatial continuity and visual similarity of pulmonary nodules in CT sequences, visual prior module is proposed to focus on the visual feature and location prior module is proposed to focus on the spatial feature. The result of dual prior concatenated into Astrous Convolutional Network to fine-tune previous result and obtain the more accurate nodules segmentation result of other slices. In order to verify the validity of our method, we conduct experiment on 1,200 adhesive pulmonary nodules. Our method yielded Dice coefficient of 87.57%, Volumetric Overlap Error of 4.86% and demonstrated that proposed method can distinguish pulmonary nodules and lung other tissue and segment adhesive nodules effectively.
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Xiao, N., Luo, S., Qiang, Y., Zhao, J., Lian, J. (2021). DPACN: Dual Prior-Guided Astrous Convolutional Network for Adhesive Pulmonary Nodules Segmentation on CT Sequence. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13021. Springer, Cham. https://doi.org/10.1007/978-3-030-88010-1_47
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