A Modified Bi-Directional Convolutional U-Net (BCDU-Net) Neural Network Approach for Lung CT Image Segmentation

Abstract

<jats:p>In this paper, a modified Bi-directional Convolutional Long Short-Term Memory U-Net (BCDU-Net) neural network is presented, which aims at enhancing medical image segmentation for lung cancer diagnosis. By integrating BConvLSTM in the decoding path and densely connected convolutional layers in the encoding path, the proposed model achieves greater stability and precision in segmenting lung CT images. The addition of Batch Normalization (BN) after the up-convolutional layers accelerates convergence speed by six times. A notable feature of BCDU-Net is its adaptability to different imaging modalities, enabling it to generalize across diverse data sources by reducing overfitting, a limitation seen in many existing models. This adaptability allows the model’s ability to be integrated into various clinical environments, ensuring consistent and reliable results across different equipment. Another key contribution is the enhanced interpretability of the model, a critical improvement when compared with traditional U-Net models. BCDU-Net accurately segments complex anatomical structures, such as the left and right lungs, and precisely identifies tumors near central bronchi or trachea airways, which is crucial for lung cancer treatment planning. The proposed model was tested on the Cancer Imaging Archive (TCIA) dataset from the 2017 Lung CT Segmentation Challenge, achieving Dice Similarity Coefficients (DSC) of 97.97 for the right lung and 97.73 for the left lung. Overall, the BCDU-Net model demonstrates superior accuracy and interpretability in medical image segmentation and holds promise for broader applications in medical imaging beyond lung cancer diagnosis.</jats:p>