The Experts below are selected from a list of 15180 Experts worldwide ranked by ideXlab platform
Joseph M. Reinhardt - One of the best experts on this subject based on the ideXlab platform.
-
anatomy guided Lung Lobe segmentation in x ray ct images
IEEE Transactions on Medical Imaging, 2009Co-Authors: Soumik Ukil, Joseph M. ReinhardtAbstract:The human Lungs are divided into five distinct anatomic compartments called the Lobes, which are separated by the pulmonary fissures. The accurate identification of the fissures is of increasing importance in the early detection of pathologies, and in the regional functional analysis of the Lungs. We have developed an automatic method for the segmentation and analysis of the fissures, based on the information provided by the segmentation and analysis of the airway and vascular trees. This information is used to provide a close initial approximation to the fissures, using a watershed transform on a distance map of the vasculature. In a further refinement step, this estimate is used to construct a region of interest (ROI) encompassing the fissures. The ROI is enhanced using a ridgeness measure, which is followed by a 3-D graph search to find the optimal surface within the ROI. We have also developed an automatic method to detect incomplete fissures, using a fast-marching based segmentation of a projection of the optimal surface. The detected incomplete fissure is used to extrapolate and smoothly complete the fissure. We evaluate the method by testing on data sets from normal subjects and subjects with mild to moderate emphysema.
-
atlas driven Lung Lobe segmentation in volumetric x ray ct images
IEEE Transactions on Medical Imaging, 2006Co-Authors: Li Zhang, Eric A. Hoffman, Joseph M. ReinhardtAbstract:High-resolution X-ray computed tomography (CT) imaging is routinely used for clinical pulmonary applications. Since Lung function varies regionally and because pulmonary disease is usually not uniformly distributed in the Lungs, it is useful to study the Lungs on a Lobe-by-Lobe basis. Thus, it is important to segment not only the Lungs, but the lobar fissures as well. In this paper, we demonstrate the use of an anatomic pulmonary atlas, encoded with a priori information on the pulmonary anatomy, to automatically segment the oblique lobar fissures. Sixteen volumetric CT scans from 16 subjects are used to construct the pulmonary atlas. A ridgeness measure is applied to the original CT images to enhance the fissure contrast. Fissure detection is accomplished in two stages: an initial fissure search and a final fissure search. A fuzzy reasoning system is used in the fissure search to analyze information from three sources: the image intensity, an anatomic smoothness constraint, and the atlas-based search initialization. Our method has been tested on 22 volumetric thin-slice CT scans from 12 subjects, and the results are compared to manual tracings. Averaged across all 22 data sets, the RMS error between the automatically segmented and manually segmented fissures is 1.96/spl plusmn/0.71 mm and the mean of the similarity indices between the manually defined and computer-defined Lobe regions is 0.988. The results indicate a strong agreement between the automatic and manual Lobe segmentations.
-
atlas driven Lung Lobe segmentation in volumetric x ray ct images
Medical Imaging 2003: Physiology and Function: Methods Systems and Applications, 2003Co-Authors: Li Zhang, Eric A. Hoffman, Joseph M. ReinhardtAbstract:The positions of the lobar fissures are of growing interest as computer-based quantitative measures to detect early pathologies and to predict or measure outcomes emerge. While we have developed a semi-automatic fissure detection method in our previous work, in this paper we describe the use of an anatomic pulmonary atlas with a priori knowledge about lobar fissures to automatically segment the lobar fissures. 16 volumetric CT scans from 16 subjects are used to construct the pulmonary atlas. After deforming the fissures onto a template image, the average fissure and variability between different subjects can be obtained by local statistical measures. The probabilistic analysis for the atlas shows that the atlas can provide an initialization for the fissure detection in certain regions with a predictable variation, although the initialization may not be close and complete. A ridgeness measure is applied on original images to enhance the fissure contrast. The fissure detection is accomplished by the initial fissure search and the final fissure search. While only parts of the initial search results are correctly delineated, a regional statistic analysis of ridgeness selects the most "reliable" initial search results, which are then used to initialize the final search. Our method has been tested in 22 volumetric thin-slice CT images from 12 subjects, and the results are compared to manual tracings. The mean of the similarity indices between the manual and computer defined Lobes is 0.988. The results indicate a strong agreement between the automatic and manual Lobe segmentations.
-
Lung Lobe segmentation by graph search with 3D shape constraints
Medical Imaging 2001: Physiology and Function from Multidimensional Images, 2001Co-Authors: Li Zhang, Eric A. Hoffman, Joseph M. ReinhardtAbstract:The Lung Lobes are natural units for reporting image-based measurements of the respiratory system. Lobar segmentation can also be used in pulmonary image processing to guide registration and drive additional segmentation. We have developed a 3D shape-constrained lobar segmentation technique for volumetric pulmonary CT images. The method consists of a search engine and shape constraints that work together to detect lobar fissures using gray level information and anatomic shape characteristics in two steps: (1) a coarse localization step, (2) a fine tuning step. An error detecting mechanism using shape constraints is used in our method to correct erroneous search results. Our method has been tested in four subjects, and the results are compared to manually traced results. The average RMS difference between the manual results and shape-constrained segmentation results is 2.23 mm. We further validated our method by evaluating the repeatability of lobar volumes measured from repeat scans of the same subject. We compared lobar air and tissue volume variations to show that most of the lobar volume variations are due to difference in air volume scan to scan.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Hiroyasu Yokomise - One of the best experts on this subject based on the ideXlab platform.
-
Spontaneous torsion of the right upper Lung Lobe: a case report.
Surgical Case Reports, 2017Co-Authors: Yusuke Kita, Kazuhito Nii, Natsumi Matsuura, Hiroyasu YokomiseAbstract:Pulmonary torsion is usually caused by thoracic surgery or trauma. Spontaneous pulmonary torsion caused by tumor and pleural effusion is very rare. A 76-year-old Asian male with a chronic cough and suspected Lung or pleural tumor presented with sudden dyspnea. Computed tomography showed that the right upper Lung Lobe contained a large tumor in the region of S1-3; the tumor had shifted to the posterior thoracic space and rotated 90° counterclockwise, potentially impeding blood flow. The patient underwent emergency right upper Lobectomy for torsion of the right upper Lung Lobe. He recovered uneventfully and was discharged without complications. We experienced a rare case of spontaneous torsion of the right upper Lung Lobe caused by a large tumor and massive pleural effusion.
-
Spontaneous torsion of the right upper Lung Lobe: a case report
Surgical Case Reports, 2017Co-Authors: Yusuke Kita, Natsumi Matsuura, Tetsuhiko Go, Hiroyasu YokomiseAbstract:Background Pulmonary torsion is usually caused by thoracic surgery or trauma. Spontaneous pulmonary torsion caused by tumor and pleural effusion is very rare. Case presentation A 76-year-old Asian male with a chronic cough and suspected Lung or pleural tumor presented with sudden dyspnea. Computed tomography showed that the right upper Lung Lobe contained a large tumor in the region of S1-3; the tumor had shifted to the posterior thoracic space and rotated 90° counterclockwise, potentially impeding blood flow. The patient underwent emergency right upper Lobectomy for torsion of the right upper Lung Lobe. He recovered uneventfully and was discharged without complications. Conclusions We experienced a rare case of spontaneous torsion of the right upper Lung Lobe caused by a large tumor and massive pleural effusion.
-
successful treatment for Lung cancer associated with pulmonary sequestration
The Annals of Thoracic Surgery, 2005Co-Authors: Taku Okamoto, Daiki Masuya, Takashi Nakashima, Shinya Ishikawa, Yasumichi Yamamoto, Chenglong Huang, Hiroyasu YokomiseAbstract:We encountered a 69-year-old man with Lung adenocarcinoma and pulmonary sequestration. The cancer lesion was located in the left upper Lobe, with sequestration of the left lower Lobe. Left upper Lobectomy was performed after induction chemoradiotherapy, but the sequestered Lung Lobe was preserved because the preoperative respiratory function was poor. Preservation of the sequestered Lung during surgery for Lung cancer should be considered in patients who have poor respiratory function and no signs of respiratory infection.
George R Washko - One of the best experts on this subject based on the ideXlab platform.
-
automatic Lung Lobe segmentation using particles thin plate splines and maximum a posteriori estimation
Medical Image Computing and Computer-Assisted Intervention, 2010Co-Authors: James C Ross, Raul San Jose Estepar, Gordon Kindlmann, Alejandro A Diaz, Carlfredrik Westin, Edwin K Silverman, George R WashkoAbstract:We present a fully automatic Lung Lobe segmentation algorithm that is effective in high resolution computed tomography (CT) datasets in the presence of confounding factors such as incomplete fissures (anatomical structures indicating Lobe boundaries), advanced disease states, high body mass index (BMI), and low-dose scanning protocols. In contrast to other algorithms that leverage segmentations of auxiliary structures (esp. vessels and airways), we rely only upon image features indicating fissure locations. We employ a particle system that samples the image domain and provides a set of candidate fissure locations. We follow this stage with maximum a posteriori (MAP) estimation to eliminate poor candidates and then perform a post-processing operation to remove remaining noise particles. We then fit a thin plate spline (TPS) interpolating surface to the fissure particles to form the final Lung Lobe segmentation. Results indicate that our algorithm performs comparably to pulmonologist-generated Lung Lobe segmentations on a set of challenging cases.
-
MICCAI (3) - Automatic Lung Lobe segmentation using particles, thin plate splines, and maximum a posteriori estimation
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, 2010Co-Authors: James C Ross, Gordon Kindlmann, Alejandro A Diaz, Carlfredrik Westin, Edwin K Silverman, Raúl San José Estépar, George R WashkoAbstract:We present a fully automatic Lung Lobe segmentation algorithm that is effective in high resolution computed tomography (CT) datasets in the presence of confounding factors such as incomplete fissures (anatomical structures indicating Lobe boundaries), advanced disease states, high body mass index (BMI), and low-dose scanning protocols. In contrast to other algorithms that leverage segmentations of auxiliary structures (esp. vessels and airways), we rely only upon image features indicating fissure locations. We employ a particle system that samples the image domain and provides a set of candidate fissure locations. We follow this stage with maximum a posteriori (MAP) estimation to eliminate poor candidates and then perform a post-processing operation to remove remaining noise particles. We then fit a thin plate spline (TPS) interpolating surface to the fissure particles to form the final Lung Lobe segmentation. Results indicate that our algorithm performs comparably to pulmonologist-generated Lung Lobe segmentations on a set of challenging cases.
Li Zhang - One of the best experts on this subject based on the ideXlab platform.
-
atlas driven Lung Lobe segmentation in volumetric x ray ct images
IEEE Transactions on Medical Imaging, 2006Co-Authors: Li Zhang, Eric A. Hoffman, Joseph M. ReinhardtAbstract:High-resolution X-ray computed tomography (CT) imaging is routinely used for clinical pulmonary applications. Since Lung function varies regionally and because pulmonary disease is usually not uniformly distributed in the Lungs, it is useful to study the Lungs on a Lobe-by-Lobe basis. Thus, it is important to segment not only the Lungs, but the lobar fissures as well. In this paper, we demonstrate the use of an anatomic pulmonary atlas, encoded with a priori information on the pulmonary anatomy, to automatically segment the oblique lobar fissures. Sixteen volumetric CT scans from 16 subjects are used to construct the pulmonary atlas. A ridgeness measure is applied to the original CT images to enhance the fissure contrast. Fissure detection is accomplished in two stages: an initial fissure search and a final fissure search. A fuzzy reasoning system is used in the fissure search to analyze information from three sources: the image intensity, an anatomic smoothness constraint, and the atlas-based search initialization. Our method has been tested on 22 volumetric thin-slice CT scans from 12 subjects, and the results are compared to manual tracings. Averaged across all 22 data sets, the RMS error between the automatically segmented and manually segmented fissures is 1.96/spl plusmn/0.71 mm and the mean of the similarity indices between the manually defined and computer-defined Lobe regions is 0.988. The results indicate a strong agreement between the automatic and manual Lobe segmentations.
-
atlas driven Lung Lobe segmentation in volumetric x ray ct images
Medical Imaging 2003: Physiology and Function: Methods Systems and Applications, 2003Co-Authors: Li Zhang, Eric A. Hoffman, Joseph M. ReinhardtAbstract:The positions of the lobar fissures are of growing interest as computer-based quantitative measures to detect early pathologies and to predict or measure outcomes emerge. While we have developed a semi-automatic fissure detection method in our previous work, in this paper we describe the use of an anatomic pulmonary atlas with a priori knowledge about lobar fissures to automatically segment the lobar fissures. 16 volumetric CT scans from 16 subjects are used to construct the pulmonary atlas. After deforming the fissures onto a template image, the average fissure and variability between different subjects can be obtained by local statistical measures. The probabilistic analysis for the atlas shows that the atlas can provide an initialization for the fissure detection in certain regions with a predictable variation, although the initialization may not be close and complete. A ridgeness measure is applied on original images to enhance the fissure contrast. The fissure detection is accomplished by the initial fissure search and the final fissure search. While only parts of the initial search results are correctly delineated, a regional statistic analysis of ridgeness selects the most "reliable" initial search results, which are then used to initialize the final search. Our method has been tested in 22 volumetric thin-slice CT images from 12 subjects, and the results are compared to manual tracings. The mean of the similarity indices between the manual and computer defined Lobes is 0.988. The results indicate a strong agreement between the automatic and manual Lobe segmentations.
-
Lung Lobe segmentation by graph search with 3D shape constraints
Medical Imaging 2001: Physiology and Function from Multidimensional Images, 2001Co-Authors: Li Zhang, Eric A. Hoffman, Joseph M. ReinhardtAbstract:The Lung Lobes are natural units for reporting image-based measurements of the respiratory system. Lobar segmentation can also be used in pulmonary image processing to guide registration and drive additional segmentation. We have developed a 3D shape-constrained lobar segmentation technique for volumetric pulmonary CT images. The method consists of a search engine and shape constraints that work together to detect lobar fissures using gray level information and anatomic shape characteristics in two steps: (1) a coarse localization step, (2) a fine tuning step. An error detecting mechanism using shape constraints is used in our method to correct erroneous search results. Our method has been tested in four subjects, and the results are compared to manually traced results. The average RMS difference between the manual results and shape-constrained segmentation results is 2.23 mm. We further validated our method by evaluating the repeatability of lobar volumes measured from repeat scans of the same subject. We compared lobar air and tissue volume variations to show that most of the lobar volume variations are due to difference in air volume scan to scan.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
James C Ross - One of the best experts on this subject based on the ideXlab platform.
-
automatic Lung Lobe segmentation using particles thin plate splines and maximum a posteriori estimation
Medical Image Computing and Computer-Assisted Intervention, 2010Co-Authors: James C Ross, Raul San Jose Estepar, Gordon Kindlmann, Alejandro A Diaz, Carlfredrik Westin, Edwin K Silverman, George R WashkoAbstract:We present a fully automatic Lung Lobe segmentation algorithm that is effective in high resolution computed tomography (CT) datasets in the presence of confounding factors such as incomplete fissures (anatomical structures indicating Lobe boundaries), advanced disease states, high body mass index (BMI), and low-dose scanning protocols. In contrast to other algorithms that leverage segmentations of auxiliary structures (esp. vessels and airways), we rely only upon image features indicating fissure locations. We employ a particle system that samples the image domain and provides a set of candidate fissure locations. We follow this stage with maximum a posteriori (MAP) estimation to eliminate poor candidates and then perform a post-processing operation to remove remaining noise particles. We then fit a thin plate spline (TPS) interpolating surface to the fissure particles to form the final Lung Lobe segmentation. Results indicate that our algorithm performs comparably to pulmonologist-generated Lung Lobe segmentations on a set of challenging cases.
-
MICCAI (3) - Automatic Lung Lobe segmentation using particles, thin plate splines, and maximum a posteriori estimation
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, 2010Co-Authors: James C Ross, Gordon Kindlmann, Alejandro A Diaz, Carlfredrik Westin, Edwin K Silverman, Raúl San José Estépar, George R WashkoAbstract:We present a fully automatic Lung Lobe segmentation algorithm that is effective in high resolution computed tomography (CT) datasets in the presence of confounding factors such as incomplete fissures (anatomical structures indicating Lobe boundaries), advanced disease states, high body mass index (BMI), and low-dose scanning protocols. In contrast to other algorithms that leverage segmentations of auxiliary structures (esp. vessels and airways), we rely only upon image features indicating fissure locations. We employ a particle system that samples the image domain and provides a set of candidate fissure locations. We follow this stage with maximum a posteriori (MAP) estimation to eliminate poor candidates and then perform a post-processing operation to remove remaining noise particles. We then fit a thin plate spline (TPS) interpolating surface to the fissure particles to form the final Lung Lobe segmentation. Results indicate that our algorithm performs comparably to pulmonologist-generated Lung Lobe segmentations on a set of challenging cases.
