Automatic lung lobe segmentation in x-ray CT images by 3D watershed transform using anatomic information from the segmented airway tree

2005 ◽  
Author(s):  
Soumik Ukil ◽  
Eric A. Hoffman ◽  
Joseph M. Reinhardt
Keyword(s):  
Ct Images ◽  
Watershed Transform ◽  
X Ray ◽  
Lung Lobe ◽  
Airway Tree ◽  
Lobe Segmentation ◽  
Anatomic Information

Atlas-driven lung lobe segmentation in volumetric x-ray CT images

2003 ◽  
Author(s):  
Li Zhang ◽  
Eric A. Hoffman ◽  
Joseph M. Reinhardt
Keyword(s):  
Ct Images ◽  
X Ray ◽  
Lung Lobe ◽  
Lobe Segmentation

scholarly journals Anatomy-Guided Lung Lobe Segmentation in X-Ray CT Images

2009 ◽  
Vol 28 (2) ◽  
pp. 202-214 ◽  
Author(s):  
S. Ukil ◽  
J.M. Reinhardt
Keyword(s):  
Ct Images ◽  
X Ray ◽  
Lung Lobe ◽  
Lobe Segmentation

Atlas-driven lung lobe segmentation in volumetric X-ray CT images

2006 ◽  
Vol 25 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Li Zhang ◽  
E.A. Hoffman ◽  
J.M. Reinhardt
Keyword(s):  
Ct Images ◽  
X Ray ◽  
Lung Lobe ◽  
Lobe Segmentation

Fully automatic lung lobe segmentation using V/Q SPECT/CT images

2020 ◽  
Author(s):  
A Király ◽  
S Urbán ◽  
Z Besenyi ◽  
L Pávics ◽  
N Zsótér ◽  
...  
Keyword(s):  
Ct Images ◽  
Lung Lobe ◽  
Fully Automatic ◽  
Lobe Segmentation

scholarly journals Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 263
Author(s):  
Xin Chen ◽  
Hong Zhao ◽  
Ping Zhou
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Region Growing ◽  
Disease Diagnosis ◽  
Ct Images ◽  
Normal Vector ◽  
Lung Lobe ◽  
Surface Classification ◽  
Lobe Segmentation ◽  
Lung Lobes

In anatomy, the lung can be divided by lung fissures into several pulmonary lobe units with specific functions. Identifying the lung lobes and the distribution of various diseases among different lung lobes from CT images is important for disease diagnosis and tracking after recovery. In order to solve the problems of low tubular structure segmentation accuracy and long algorithm time in segmenting lung lobes based on lung anatomical structure information, we propose a segmentation algorithm based on lung fissure surface classification using a point cloud region growing approach. We cluster the pulmonary fissures, transformed into point cloud data, according to the differences in the pulmonary fissure surface normal vector and curvature estimated by principal component analysis. Then, a multistage spline surface fitting method is used to fill and expand the lung fissure surface to realize the lung lobe segmentation. The proposed approach was qualitatively and quantitatively evaluated on a public dataset from Lobe and Lung Analysis 2011 (LOLA11), and obtained an overall score of 0.84. Although our approach achieved a slightly lower overall score compared to the deep learning based methods (LobeNet_V2 and V-net), the inter-lobe boundaries from our approach were more accurate for the CT images with visible lung fissures.

Lung lobe segmentation by anatomy-guided 3D watershed transform

2003 ◽  
Author(s):  
Jan-Martin Kuhnigk ◽  
Horst Hahn ◽  
Milo Hindennach ◽  
Volker Dicken ◽  
Stefan Krass ◽  
...  
Keyword(s):  
Watershed Transform ◽  
Lung Lobe ◽  
Lobe Segmentation

Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images

2003 ◽  
Vol 22 (8) ◽  
pp. 940-950 ◽  
Author(s):  
D. Aykac ◽  
E.A. Hoffman ◽  
G. McLennan ◽  
J.M. Reinhardt
Keyword(s):  
Three Dimensional ◽  
Ct Images ◽  
X Ray ◽  
Human Airway ◽  
Airway Tree

scholarly journals Pulmonary Lobe Segmentation in CT Images Based on Lung Anatomy Knowledge

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yuanyuan Peng ◽  
Hualan Zhong ◽  
Zheng Xu ◽  
Hongbin Tu ◽  
Xiong Li ◽  
...  
Keyword(s):  
Left Lung ◽  
Region Of Interest ◽  
Ground Truth ◽  
Ct Images ◽  
Lung Lobe ◽  
Lung Anatomy ◽  
Lobe Segmentation ◽  
The Right ◽  
Anatomy Knowledge ◽  
High Median

In computed tomography (CT) images, pulmonary lobe segmentation is an arduous task due to its complex structures. To remedy the problem, we introduce a new framework based on lung anatomy knowledge for lung lobe segmentation. Firstly, the priori knowledge of lung anatomy is used to identify the fissure region of interest. Then, an oriented derivative of stick filter is applied to isolate plate-like structures from clutters for lobar fissure verification. Finally, a surface fitting model is employed to complete the incomplete fissure surface for lung lobe segmentation. Compared with manually segmented fissure references, the designed approach obtained a high median F1-score of 0.8865 in the left lung and obtained a high median F1-score of 0.9200 in the right lung. The average percentages of the segmented lung lobes in the lung lobe ground truth are 0.960, 0.989, 0.973, 0.920, and 0.985 for the left upper, left lower, right upper, right middle, and right lower lobes, respectively. The perfect performance of the proposed scheme is tested by visual inspection and quantitative evaluation.

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