Automatic Approach for Lung Segmentation with Juxta-Pleural Nodules from Thoracic CT Based on Contour Tracing and Correction

Abstract

This paper presents a fully automatic framework for lung segmentation, in which juxta-pleural nodule problem is brought into strong focus. The proposed scheme consists of three phases: skin boundary detection, rough segmentation of lung contour, and pulmonary parenchyma refinement. Firstly, chest skin boundary is extracted through image aligning, morphology operation, and connective region analysis. Secondly, diagonal-based border tracing is implemented for lung contour segmentation, with maximum cost path algorithm used for separating the left and right lungs. Finally, by arc-based border smoothing and concave-based border correction, the refined pulmonary parenchyma is obtained. The proposed scheme is evaluated on 45 volumes of chest scans, with volume difference (VD) 11.15 ± 69.63 cm3, volume overlap error (VOE) 3.5057 ± 1.3719%, average surface distance (ASD) 0.7917 ± 0.2741 mm, root mean square distance (RMSD) 1.6957 ± 0.6568 mm, maximum symmetric absolute surface distance (MSD) 21.3430 ± 8.1743 mm, and average time-cost 2 seconds per image. The preliminary results on accuracy and complexity prove that our scheme is a promising tool for lung segmentation with juxta-pleural nodules.

Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer

“…In similar studies, it is shown that auto-contouring techniques can save time [16,17], while other studies show promising new techniques that could potentially save time if investigated [10,18]. Young et al showed a potential time save for contouring lymph node for pelvic cancer patients [19].…”

Dual-branch residual network for lung nodule segmentation

“…In order to alleviate this problem, In the energy optimization method, people usually convert the segmentation task into an energy minimization task to process. For example, in [22][23][24][25], the author uses a level set function to characterize the image, and when the segmented contour matches the nodule boundary, the energy function reaches a minimum. A similar approach is the lung nodules segmentation method based on shape prior hypotheses and level sets, proposed by Farag et al [7].…”

Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions

“…At present, there are various ranges of techniques implemented for segmentation. The work carried out by Wang and Guo [11] have presented a combined implementation of identifying skin boundary, segmenting contour regions, and refinement of lung region. Most recently, a unique segmentation process of segregating heart from the lung field was introduced by Dai et al [12].…”