R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation

• URL: http://arxiv.org/abs/2105.02290v1
• Date: Wed, 5 May 2021 19:17:14 GMT
• Title: R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation
• Authors: Dhaval D. Kadia, Md Zahangir Alom, Ranga Burada, Tam V. Nguyen, Vijayan K. Asari
• Abstract summary: We propose a novel model, namely, Recurrent Residual 3D U-Net (R2U3D), for the 3D lung segmentation task. In particular, the proposed model integrates 3D convolution into the Recurrent Residual Neural Network based on U-Net. The proposed R2U3D network is trained on the publicly available dataset LUNA16 and it achieves state-of-the-art performance.
• Score: 17.343802171952195
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 3D lung segmentation is essential since it processes the volumetric information of the lungs, removes the unnecessary areas of the scan, and segments the actual area of the lungs in a 3D volume. Recently, the deep learning model, such as U-Net outperforms other network architectures for biomedical image segmentation. In this paper, we propose a novel model, namely, Recurrent Residual 3D U-Net (R2U3D), for the 3D lung segmentation task. In particular, the proposed model integrates 3D convolution into the Recurrent Residual Neural Network based on U-Net. It helps learn spatial dependencies in 3D and increases the propagation of 3D volumetric information. The proposed R2U3D network is trained on the publicly available dataset LUNA16 and it achieves state-of-the-art performance on both LUNA16 (testing set) and VESSEL12 dataset. In addition, we show that training the R2U3D model with a smaller number of CT scans, i.e., 100 scans, without applying data augmentation achieves an outstanding result in terms of Soft Dice Similarity Coefficient (Soft-DSC) of 0.9920.

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