Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, India.
Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, India.
J Imaging Inform Med. 2024 Jun;37(3):988-1007. doi: 10.1007/s10278-024-01004-1. Epub 2024 Feb 12.
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.
肺结节是基于肺部圆形或椭圆形细胞的生长而产生的，这些细胞可能是癌性的，也可能是非癌性的。准确地对这些结节进行分割对于早期发现和诊断肺癌至关重要。然而，肺结节的形状、大小和密度各不相同，因此准确地对其进行分割是一项艰巨的任务。此外，它们很容易与肺部的其他结构混淆，包括血管和气道，这进一步增加了分割过程的复杂性。为了解决这个挑战，本文提出了一种新的多裁剪卷积神经网络（multi-crop CNN）模型，该模型利用 CT 扫描图像的不同大小裁剪区域来准确地分割肺结节。该模型由三个模块组成，分别是特征表示模块、边界细化模块和分割模块。特征表示模块使用不同大小的裁剪区域从肺部 CT 扫描图像中提取特征，而边界细化模块则将边界图和特征图结合起来，为分割过程生成最终的特征图。分割模块生成高分辨率的分割图，提高了对癌性肺结节的分割准确性。该多裁剪 CNN 模型在两个分割数据集 LUNA16 和 LIDC-IDRI 上进行了评估，准确率分别为 98.3%和 98.5%。性能的衡量标准包括准确率、召回率、精度、Dice 系数、特异性、AUC/ROC、Hausdorff 距离、Jaccard 指数和平均 Hausdorff 距离。总体而言，该多裁剪 CNN 模型具有提高肺结节分割准确性的潜力，这可能有助于更早地发现和诊断肺癌，并最终降低与该疾病相关的死亡率。
