Radiologists use the standardized Lung-RADS clinical scoring criteria to assess and report spiculations/lobulations and sharp/curved spikes on the surface of lung nodules, because they are good predictors of lung cancer. Manual spiculation/lobulation annotation and classification is a tedious task for radiologists due to the nodule’s 3D geometry and 2D slice-by-slice assessment. This work presents SCS-Voxel2Mesh, a multi-class end-to-end deep learning model that segments pulmonary nodules (while preserving spikes), classifies spikes (sharp/spiculation and curved/lobulation) and performs malignancy prediction. Comprehensive experiments were conducted to evaluate and compare the performance of SCS-Voxel2Mesh with other segmentation and 3D mesh generation methods using the CIR dataset. The results demonstrate competitive performance of SCS-Voxel2Mesh on both the nodule spike classification and malignancy prediction tasks.

Pulmonary nodule quantification is essential in forecasting and diagnosing potential malignant nodules, providing critical information for early intervention and treatment planning. However, most existing assistant diagnosis techniques primarily focus on the localization of lung nodules without comprehensive quantitative analysis, limiting their utility in clinical practice. To address this significant limitation, we present a novel and robust pulmonary nodule quantification framework named PN-Quant. It integrates a detection module, a segmentation module, and a quantification module to enable automated identification and precise measurement of lung nodules in full-size Computer Tomography (CT) scans, which facilitates the extraction of geometric characteristics, including volume, surface area, mass, sphericity, compactness, and elongation, offering valuable quantitative data for accurate nodule assessment. This study evaluates multiple PN-Quant pipelines with diverse configurations using datasets LIDC-IDRI, LNDb-19, and MSD-lung. Notably, the pipeline combining SANet and 3D UX-Net demonstrated superior performance, yielding low relative errors of 16.8%, 39.5%, and 24.4% on the respective datasets. These results underscore the effectiveness of the automated pipeline based on PN-Quant in efficiently and accurately quantifying pulmonary nodules across diverse datasets. The findings from this research highlight the potential of PN-Quant as a valuable tool for enhancing the precision and reliability of pulmonary nodule analysis in clinical settings, ultimately contributing to improved patient outcomes and clinical decision-making. Our source code is available at https://github.com/Xinkai-Tang/PN-Quant.

Lung nodule growth prediction is vital for improving lung cancer diagnosis. The irregular time intervals in clinical follow-up 3D CT scans and the complex nature of lung nodules present significant challenges for accurate modeling nodule growth. Leveraging these clinical follow-up data is crucial for enhancing prediction models. In this study, we propose Lung Nodule Growth Network (LNGNet), a two-stage novel model based on our self-constructed temporal CT dataset named LNt. LNGNet features an isotropic-MedNeXt backbone for superior feature extraction, the Adaptive Temporal Scaling (ATS) Module to handle irregular time intervals, and an autoencoder with attention feature maps to guide texture generation at future time points. LNGNet accurately predicts nodule mass and volume and generates high-quality shape and texture visualizations, with 5% improvement on volume prediction AUC, 7.2% improvement on mass prediction AUC and 2.89% improvement on dice score. These advancements demonstrate LNGNet’s potential for clinical application, enhancing early diagnosis and treatment strategies for lung cancer patients.

The development of CT technology has played a crucial role in assisting doctors in the diagnosis of lung nodules. However, due to their three-dimensional morphology and complex structure, two-dimensional medical images are insufficient for intuitive visualization and analysis of lung nodules, making three-dimensional reconstruction necessary to address this issue. In this study, we propose a novel three-dimensional reconstruction framework called CPAConv-POCO, which is based on point convolution. This framework employs continuous convolution instead of discrete convolution commonly used in traditional image processing tasks to handle unstructured data such as point cloud. Additionally, we introduce a new convolution kernel construction method called Position Adaptive Convolution (PAConv). PAConv dynamically assembles convolution kernels by combining basic weight matrices stored in Weight Bank. The coefficients of these weight matrices are adaptively learned from the positions of points using ScoreNet. This data-driven construction of kernels provides the flexibility of PAConv, allowing it to better handle irregular and unordered point cloud data. The convolution module obtained by combining these two components is named Continuous Position Adaptive Convolution (CPAConv). In our experiments, we extensively evaluated our method on the publicly available LUNA16 and LNDb datasets. In terms of reconstruction accuracy, the Intersection over Union(IoU) of CPAConv-POCO’s lung nodule reconstruction reaches 85.70%, which is 1.51% higher than POCO. On the LNDb dataset, the IoU of our method reaches 71.33%, which is 3.33% higher than POCO. We also evaluated the performance of our model on lung nodules of different diameters. On the LUNA16 dataset, the IoU of our method reaches 90.19% on lung nodules with a diameter smaller than 5mm, demonstrating superior reconstruction performance for small lung nodules.

The increasing application of Low Dose Spiral CT (LDCT) has enhanced lung nodule detection, promoting early diagnosis and better cure outcomes. Addressing the challenges of false positives and the radiologists’ growing workload, we refined the ConvNeXt model by integrating dilated residual modules for precise lung nodule detection. This led to a 9.48% improvement in 2D accuracy on the Luna16 dataset, attaining 87.24%. The 3D feature accuracy reached 81.78%, a leap of 21.6%, in 10-fold cross-validation. For the LNDB dataset, 2D classification surged to 95.74% (a 1.78% boost), and 3D prediction precision escalated from 72.02% to 80.73%.

Lung nodule segmentation is usually considered a 3D semantic segmentation task. Due to the small size, diverse morphology, and low recognition of lung nodules, it is hard to segment any nodule precisely. To solve this problem, we propose a lightweight depthwise separable convolutional network named ConvUNET, which consists of a hierarchical encoder and a U-shaped decoder. Compared with some Transformer-based models (e.g., SwinUNETR) and ConvNeXt-based models (e.g., 3D UX-Net), our model has the advantages of fewer parameters, faster inference speed, and higher accuracy. We test the segmentation performance on the LUNA-16 and LNDb-19 datasets using standard 5-fold cross-validations, and the proposed method achieves competitive dice scores of 88.90% and 84.16%, respectively. Besides, it also shows considerable precision in segmenting lung nodules with diverse characteristics. Our source code is available at https://github.com/Xinkai-Tang/ConvUNET.

In computer-aided diagnosis systems, lung nodule detection plays a crucial role in the overall framework. In this work, we propose a new three-dimensional deformable convolutional neural network (dcnn) method for lung nodule detection based on the Faster R-CNN framework. We incorporate deformable convolutions to design a hybrid convolutional module, which enhances feature extraction in the lung nodule detection model. By leveraging the deformable convolutions’ characteristics, the network is capable of capturing the diverse morphological variations of lung nodules, addressing challenges such as large morphological variations and the inability to capture unified image features. This improves the accuracy of the lung nodule detection algorithm. Additionally, we employ a second-stage network to further discriminate suspected nodules, which enhances the recognition of non-nodule tissues and reduces false positive nodules. To comprehensively evaluate the performance of various lung nodule detection models, we conducted experiments using the publicly available LUNA16 dataset. Our method surpasses other detection algorithms in terms of CPM, achieving a 1.5% improvement. Particularly, the nodule recognition rate is significantly improved at lower false positive rates. In addition, in other metrics such as F1-score, AP, we also achieved 0.6%, 2% improvement.

UNet and its extended versions are the most used networks in the lung nodule segmentation from CT images. However, current UNet-like methods still suffer from some problems: 1) The heterogeneity of lung nodules affect the segmentation performance; 2) the mixture of lung nodules and their surrounding tissues in the CT image increases the segmentation difficulty. To address these issues, we propose a novel hierarchical attention 3D UNet named HAUNet-3D. It introduces the attention mechanism at multiple scales and organizes them in a bottom-up hierarchical connection way. Such a proposition could better capture features with various sizes and guide the fusion of features from adjacent attention outputs without losing the advantages of 3D UNet. In experiment, our method has been extensively evaluated on the public LUNA16 dataset. It achieves competitive segmentation performance on dice similarity coefficient of 83.34% and average surface distance of 0.28 mm. More importantly, our method is proven to be more robust to the heterogeneous types of lung nodules and shows better segmentation performance on small lung nodules.