Overcoming the Challenge of Accurate Segmentation of Lung Nodules: A Multi-crop CNN Approach

Lung nodules are generated based on the growth of small and round- or oval-shaped cells in the lung, which are either cancerous or non-cancerous. Accurate segmentation of these nodules is crucial for early detection and diagnosis of lung cancer. However, lung nodules can have various shapes, sizes, and densities, making their accurate segmentation a difficult task. Moreover, they can be easily confused with other structures in the lung, including blood vessels and airways, further complicating the segmentation process. To address this challenge, this paper proposes a novel multi-crop convolutional neural network (multi-crop CNN) model that utilizes different sized cropped regions of CT scan images for accurate segmentation of lung nodules. The model consists of three modules, namely the feature representation module, boundary refinement module, and segmentation module. The feature representation module captures features from the lung CT scan image using cropped regions of different sizes, while the boundary refinement module combines the boundary maps and feature maps to generate a final feature map for the segmentation process. The segmentation module produces a high-resolution segmentation map that shows improved accuracy in segmenting cancerous lung nodules. The proposed multi-crop CNN model is evaluated on two segmentation datasets namely LUNA 16 and LIDC-IDRI with an accuracy of 98.3% and 98.5%, respectively. The performances are measured in terms of accuracy, recall, precision, dice coefficient, specificity, AUC/ROC, Hausdorff distance, Jaccard index, and average Hausdorff. Overall, the proposed multi-crop CNN model demonstrates the potential to enhance the lung nodule segmentation accuracy, which could lead to earlier detection and diagnosis of lung cancer and ultimately reduce mortality rates associated with the disease.