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Aim: The main objective of this work is to propose an efficient segmentation model for accurate and robust lung segmentation from computed tomography (CT) images, even when the lung contains abnormalities such as juxtapleural nodules, cavities, and consolidation. Methodology: A novel deep learning‑based segmentation model, pyramid‑dilated dense U‑Net (PDD‑U‑Net), is proposed to directly segment lung regions from the whole CT image. The model is integrated with pyramid‑dilated convolution blocks to capture and preserve multi‑resolution spatial features effectively. In addition, shallow and deeper stream features are embedded in the nested U‑Net structure at the decoder side to enhance the segmented output. The effect of three loss functions is investigated in this paper, as the medical image analysis method requires precise boundaries. The proposed PDD‑U‑Net model with shape‑aware loss function is tested on the lung CT segmentation challenge (LCTSC) dataset with standard lung CT images and the lung image database consortium‑image database resource initiative (LIDC‑IDRI) dataset containing both typical and pathological lung CT images. Results: The performance of the proposed method is evaluated using Intersection over Union, dice coefficient, precision, recall, and average Hausdorff distance metrics. Segmentation results showed that the proposed PDD‑U‑Net model outperformed other segmentation methods and achieved a 0.983 dice coefficient for the LIDC‑IDRI dataset and a 0.994 dice coefficient for the LCTSC dataset. Conclusions: The proposed PDD‑U‑Net model with shape‑aware loss function is an effective and accurate method for lung segmentation from CT images, even in the presence of abnormalities such as cavities, consolidation, and nodules. The model’s integration of pyramid‑dilated convolution blocks and nested U‑Net structure at the decoder side, along with shape‑aware loss function, contributed to its high segmentation accuracy. This method could have significant implications for the computer‑aided diagnosis system, allowing for quick and accurate analysis of lung regions. [ABSTRACT FROM AUTHOR]

Aims: Deep-learning methods are becoming versatile in the field of medical image analysis. The hand-operated examination of smaller nodules from computed tomography scans becomes a challenging and time-consuming task due to the limitation of human vision. A standardized computer-aided diagnosis (CAD) framework is required for rapid and accurate lung cancer diagnosis. The National Lung Screening Trial recommends routine screening with low-dose computed tomography among high-risk patients to reduce the risk of dying from lung cancer by early cancer detection. The evolvement of clinically acceptable CAD system for lung cancer diagnosis demands perfect prototypes for segmenting lung region, followed by identifying nodules with reduced false positives. Recently, deep-learning methods are increasingly adopted in medical image diagnosis applications. Subjects and Methods: In this study, a deep-learning-based CAD framework for lung cancer diagnosis with chest computed tomography (CT) images is built using dilated SegNet and convolutional neural networks (CNNs). A dilated SegNet model is employed to segment lung from chest CT images, and a CNN model with batch normalization is developed to identify the true nodules from all possible nodules. The dilated SegNet and CNN models have been trained on the sample cases taken from the LUNA16 dataset. The performance of the segmentation model is measured in terms of Dice coefficient, and the nodule classifier is evaluated with sensitivity. The discriminant ability of the features learned by a CNN classifier is further confirmed with principal component analysis. Results: Experimental results confirm that the dilated SegNet model segments the lung with an average Dice coefficient of 0.89 ± 0.23 and the customized CNN model yields a sensitivity of 94.8 on categorizing cancerous and noncancerous nodules. Conclusions: Thus, the proposed CNN models achieve efficient lung segmentation and two-dimensional nodule patch classification in CAD system for lung cancer diagnosis with CT screening. [ABSTRACT FROM AUTHOR]