A novel 3D lightweight model for COVID-19 lung CT Lesion Segmentation

Abstract

3D-based medical image segmentation, offering enhanced spatial information compared to 2D slice-based methods, encounters challenges arising from factors such as a restricted clinical sample size, imbalanced foreground-background pixel distribution, and suboptimal generalization performance. To address these challenges, we propose a lightweight segmentation model tailored to 3D medical images. Employing the K-means algorithm, our approach efficiently extracts the Region of Interest (ROI) from medical images, facilitating lung area segmentation while minimizing interference from background pixels. We address the risk of model overfitting by adopting the Focal loss in conjunction with the Dice coefficient as our loss function. Feature extraction capabilities are bolstered through the incorporation of a parallel attention mechanism at skip connections, aiming to enhance the representation of both shallow and deep layers. Moreover, we optimize computational efficiency and memory utilization by substituting 3 × 3 convolutions with depth-wise separable convolutions and integrating residual connections for improved gradient propagation. The introduction of Ghost-inspired 1 × 1 convolution ensures consistent feature dimensions before and after residual connections. Experimental evaluation, conducted on a dataset comprising 199 COVID-19-Seg cases through 5-fold cross-validation, underscores the superior performance of our proposed model. Evaluation metrics, including Average Surface Distance (ASD), accuracy, sensitivity, Dice coefficient, and Intersection over Union (IOU) accuracy, yield values of 19.880, 99.90 %, 58.90 %, 56.10 %, and 41.00 %, respectively. In comparison to the other state-of-the-art segmentation models, our approach achieves heightened segmentation accuracy and generalization performance while incurring only a marginal increase in parameters and computational complexity.