ConvUNET: a Novel Depthwise Separable ConvNet for Lung Nodule Segmentation

Lung nodule segmentation is usually considered a 3D semantic segmentation task. Due to the small size, diverse morphology, and low recognition of lung nodules, it is hard to segment any nodule precisely. To solve this problem, we propose a lightweight depthwise separable convolutional network named ConvUNET, which consists of a hierarchical encoder and a U-shaped decoder. Compared with some Transformer-based models (e.g., SwinUNETR) and ConvNeXt-based models (e.g., 3D UX-Net), our model has the advantages of fewer parameters, faster inference speed, and higher accuracy. We test the segmentation performance on the LUNA-16 and LNDb-19 datasets using standard 5-fold cross-validations, and the proposed method achieves competitive dice scores of 88.90% and 84.16%, respectively. Besides, it also shows considerable precision in segmenting lung nodules with diverse characteristics. Our source code is available at https://github.com/Xinkai-Tang/ConvUNET.