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Fully Automated Pulmonary Lobar Segmentation: Influence of Different Prototype Software Programs onto Quantitative Evaluation of Chronic Obstructive Lung Disease.

Lim HJ, Weinheimer O, Wielpütz MO, Dinkel J, Hielscher T, Gompelmann D, Kauczor HU, Heussel CP - PLoS ONE (2016)

Bottom Line: Segmentation using programs 1, 3 and 4 was unsuccessful in 1 (1%), 7 (10%) and 5 (7%) patients, respectively.The 53 patients with successful segmentation by all 4 programs were included for further analysis.Only a single software program was able to successfully analyze all scheduled data-sets.

View Article: PubMed Central - PubMed

Affiliation: Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.

ABSTRACT

Objectives: Surgical or bronchoscopic lung volume reduction (BLVR) techniques can be beneficial for heterogeneous emphysema. Post-processing software tools for lobar emphysema quantification are useful for patient and target lobe selection, treatment planning and post-interventional follow-up. We aimed to evaluate the inter-software variability of emphysema quantification using fully automated lobar segmentation prototypes.

Material and methods: 66 patients with moderate to severe COPD who underwent CT for planning of BLVR were included. Emphysema quantification was performed using 2 modified versions of in-house software (without and with prototype advanced lung vessel segmentation; programs 1 [YACTA v.2.3.0.2] and 2 [YACTA v.2.4.3.1]), as well as 1 commercial program 3 [Pulmo3D VA30A_HF2] and 1 pre-commercial prototype 4 [CT COPD ISP ver7.0]). The following parameters were computed for each segmented anatomical lung lobe and the whole lung: lobar volume (LV), mean lobar density (MLD), 15th percentile of lobar density (15th), emphysema volume (EV) and emphysema index (EI). Bland-Altman analysis (limits of agreement, LoA) and linear random effects models were used for comparison between the software.

Results: Segmentation using programs 1, 3 and 4 was unsuccessful in 1 (1%), 7 (10%) and 5 (7%) patients, respectively. Program 2 could analyze all datasets. The 53 patients with successful segmentation by all 4 programs were included for further analysis. For LV, program 1 and 4 showed the largest mean difference of 72 ml and the widest LoA of [-356, 499 ml] (p<0.05). Program 3 and 4 showed the largest mean difference of 4% and the widest LoA of [-7, 14%] for EI (p<0.001).

Conclusions: Only a single software program was able to successfully analyze all scheduled data-sets. Although mean bias of LV and EV were relatively low in lobar quantification, ranges of disagreement were substantial in both of them. For longitudinal emphysema monitoring, not only scanning protocol but also quantification software needs to be kept constant.

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Related in: MedlinePlus

Examples of patients who were excluded.Patients for whom at least one of the programs could not generate results at all were excluded from the analysis. (A) Program 3 could not recognize right middle lobe (arrow) in this 57 year-old patient with FEV1 = 24% due to unsuccessful lobar segmentation. (B) A 50 year-old patient with FEV1 = 20% had incorrect outline of the lung and could not be processed normally in program 3. (C) Program 4 included part of the central airway to right upper lobe (arrow) in this 75 year-old patient with FEV1 = 44%. Program 3 also failed in lobar segmentation for the same patient. FEV1 = forced expiratory volume in 1s.
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pone.0151498.g001: Examples of patients who were excluded.Patients for whom at least one of the programs could not generate results at all were excluded from the analysis. (A) Program 3 could not recognize right middle lobe (arrow) in this 57 year-old patient with FEV1 = 24% due to unsuccessful lobar segmentation. (B) A 50 year-old patient with FEV1 = 20% had incorrect outline of the lung and could not be processed normally in program 3. (C) Program 4 included part of the central airway to right upper lobe (arrow) in this 75 year-old patient with FEV1 = 44%. Program 3 also failed in lobar segmentation for the same patient. FEV1 = forced expiratory volume in 1s.

Mentions: Program 1 failed to segment the right upper lobe for one patient in both of the sessions. The segmentation failed because the right upper lobe bronchi were not segmented by the bronchial tree segmentation algorithm causing the following lobe segmentation algorithm to fail also. There was an unexpected halt with program 3 during the lobar segmentation process of 6 patients (Fig 1A), and erroneous outline of the lung was produced in another patient (Fig 1B). Program 4 also failed to generate results during the segmentation process of 5 patients. In one patient in which segmentation could not be achieved with program 3, program 4 also delivered erroneous results due to segmentation of part of the central airway as right upper lobe (Fig 1C). Program 2 was able to analyze all datasets. We observed that right upper lobe and right middle lobe were major source of relative variability of lobar segmentation resulting in difference of quantification results considering the course of the minor fissure. Fig 2 shows the example of the patients who had substantially different values by programs.


Fully Automated Pulmonary Lobar Segmentation: Influence of Different Prototype Software Programs onto Quantitative Evaluation of Chronic Obstructive Lung Disease.

Lim HJ, Weinheimer O, Wielpütz MO, Dinkel J, Hielscher T, Gompelmann D, Kauczor HU, Heussel CP - PLoS ONE (2016)

Examples of patients who were excluded.Patients for whom at least one of the programs could not generate results at all were excluded from the analysis. (A) Program 3 could not recognize right middle lobe (arrow) in this 57 year-old patient with FEV1 = 24% due to unsuccessful lobar segmentation. (B) A 50 year-old patient with FEV1 = 20% had incorrect outline of the lung and could not be processed normally in program 3. (C) Program 4 included part of the central airway to right upper lobe (arrow) in this 75 year-old patient with FEV1 = 44%. Program 3 also failed in lobar segmentation for the same patient. FEV1 = forced expiratory volume in 1s.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4814108&req=5

pone.0151498.g001: Examples of patients who were excluded.Patients for whom at least one of the programs could not generate results at all were excluded from the analysis. (A) Program 3 could not recognize right middle lobe (arrow) in this 57 year-old patient with FEV1 = 24% due to unsuccessful lobar segmentation. (B) A 50 year-old patient with FEV1 = 20% had incorrect outline of the lung and could not be processed normally in program 3. (C) Program 4 included part of the central airway to right upper lobe (arrow) in this 75 year-old patient with FEV1 = 44%. Program 3 also failed in lobar segmentation for the same patient. FEV1 = forced expiratory volume in 1s.
Mentions: Program 1 failed to segment the right upper lobe for one patient in both of the sessions. The segmentation failed because the right upper lobe bronchi were not segmented by the bronchial tree segmentation algorithm causing the following lobe segmentation algorithm to fail also. There was an unexpected halt with program 3 during the lobar segmentation process of 6 patients (Fig 1A), and erroneous outline of the lung was produced in another patient (Fig 1B). Program 4 also failed to generate results during the segmentation process of 5 patients. In one patient in which segmentation could not be achieved with program 3, program 4 also delivered erroneous results due to segmentation of part of the central airway as right upper lobe (Fig 1C). Program 2 was able to analyze all datasets. We observed that right upper lobe and right middle lobe were major source of relative variability of lobar segmentation resulting in difference of quantification results considering the course of the minor fissure. Fig 2 shows the example of the patients who had substantially different values by programs.

Bottom Line: Segmentation using programs 1, 3 and 4 was unsuccessful in 1 (1%), 7 (10%) and 5 (7%) patients, respectively.The 53 patients with successful segmentation by all 4 programs were included for further analysis.Only a single software program was able to successfully analyze all scheduled data-sets.

View Article: PubMed Central - PubMed

Affiliation: Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.

ABSTRACT

Objectives: Surgical or bronchoscopic lung volume reduction (BLVR) techniques can be beneficial for heterogeneous emphysema. Post-processing software tools for lobar emphysema quantification are useful for patient and target lobe selection, treatment planning and post-interventional follow-up. We aimed to evaluate the inter-software variability of emphysema quantification using fully automated lobar segmentation prototypes.

Material and methods: 66 patients with moderate to severe COPD who underwent CT for planning of BLVR were included. Emphysema quantification was performed using 2 modified versions of in-house software (without and with prototype advanced lung vessel segmentation; programs 1 [YACTA v.2.3.0.2] and 2 [YACTA v.2.4.3.1]), as well as 1 commercial program 3 [Pulmo3D VA30A_HF2] and 1 pre-commercial prototype 4 [CT COPD ISP ver7.0]). The following parameters were computed for each segmented anatomical lung lobe and the whole lung: lobar volume (LV), mean lobar density (MLD), 15th percentile of lobar density (15th), emphysema volume (EV) and emphysema index (EI). Bland-Altman analysis (limits of agreement, LoA) and linear random effects models were used for comparison between the software.

Results: Segmentation using programs 1, 3 and 4 was unsuccessful in 1 (1%), 7 (10%) and 5 (7%) patients, respectively. Program 2 could analyze all datasets. The 53 patients with successful segmentation by all 4 programs were included for further analysis. For LV, program 1 and 4 showed the largest mean difference of 72 ml and the widest LoA of [-356, 499 ml] (p<0.05). Program 3 and 4 showed the largest mean difference of 4% and the widest LoA of [-7, 14%] for EI (p<0.001).

Conclusions: Only a single software program was able to successfully analyze all scheduled data-sets. Although mean bias of LV and EV were relatively low in lobar quantification, ranges of disagreement were substantial in both of them. For longitudinal emphysema monitoring, not only scanning protocol but also quantification software needs to be kept constant.

Show MeSH
Related in: MedlinePlus