Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach
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 so...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/13/10/263 |
| _version_ | 1827704207482814464 |
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| author | Xin Chen Hong Zhao Ping Zhou |
| author_facet | Xin Chen Hong Zhao Ping Zhou |
| author_sort | Xin Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:35:29Z |
| format | Article |
| id | doaj.art-5bed3849c21549189dc356034ef77702 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T15:35:29Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-5bed3849c21549189dc356034ef777022023-11-20T17:17:04ZengMDPI AGAlgorithms1999-48932020-10-01131026310.3390/a13100263Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing ApproachXin Chen0Hong Zhao1Ping Zhou2College of Electrical and Information Engineering, Hunan University, Changsha 410082, ChinaCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410003, ChinaCollege of Biology, Hunan University, Changsha 410082, ChinaIn 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.https://www.mdpi.com/1999-4893/13/10/263lung lobe segmentationlung fissure classificationpoint cloudregion growingnormal vector and curvatureprincipal component analysis |
| spellingShingle | Xin Chen Hong Zhao Ping Zhou Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach Algorithms lung lobe segmentation lung fissure classification point cloud region growing normal vector and curvature principal component analysis |
| title | Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach |
| title_full | Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach |
| title_fullStr | Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach |
| title_full_unstemmed | Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach |
| title_short | Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach |
| title_sort | lung lobe segmentation based on lung fissure surface classification using a point cloud region growing approach |
| topic | lung lobe segmentation lung fissure classification point cloud region growing normal vector and curvature principal component analysis |
| url | https://www.mdpi.com/1999-4893/13/10/263 |
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