Increasing Accuracy of Optimal Surfaces Using Min-Marginal Energies

Petersen, Jens; Arias-Lorza, Andres M.; Selvan, Raghavendra; Bos, Daniel; Lugt, Aad van der; Pedersen, Jesper Holst; Nielsen, Mads; Bruijne, Marleen de

doi:10.1109/tmi.2018.2890386

Cited by 3 publications

(3 citation statements)

References 41 publications

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“…We also compared the performance with a state-of-the-art non-CNN vessel wall segmentation method, Optimal front segmentation (Opfront) [14], which is based on the graph cut algorithm.…”

Section: Ablation Study and Comparison Methodsmentioning

confidence: 99%

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Automated Artery Localization and Vessel Wall Segmentation Using Tracklet Refinement and Polar Conversion

et al. 2020

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Quantitative analysis of blood vessel wall structures is important to study atherosclerotic diseases and assess cardiovascular event risks. To achieve this, accurate identification of vessel luminal and outer wall contours is needed. Computer-assisted tools exist, but manual preprocessing steps, such as region of interest identification and/or boundary initialization, are still needed. In addition, prior knowledge of the ring shape of vessel walls has not been fully explored in designing segmentation methods. In this work, a fully automated artery localization and vessel wall segmentation system is proposed. A tracklet refinement algorithm was adapted to robustly identify the artery of interest from a neural network-based artery centerline identification architecture. Image patches were extracted from the centerlines and converted in a polar coordinate system for vessel wall segmentation. The segmentation method used 3D polar information and overcame problems such as contour discontinuity, complex vessel geometry, and interference from neighboring vessels. Verified by a large (>32000 images) carotid artery dataset collected from multiple sites, the proposed system was shown to better automatically segment the vessel wall than traditional vessel wall segmentation methods or standard convolutional neural network approaches. In addition, a segmentation uncertainty score was estimated to effectively identify slices likely to have errors and prompt manual confirmation of the segmentation. This robust vessel wall segmentation system has applications in different vascular beds and will facilitate vessel wall feature extraction and cardiovascular risk assessment.

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“…We also compared the performance with a state-of-the-art non-CNN vessel wall segmentation method, Optimal front segmentation (Opfront) [14], which is based on the graph cut algorithm.…”

Section: Ablation Study and Comparison Methodsmentioning

confidence: 99%

“…al. [12]- [14]. Another category of methods segment the vessel wall area by classifying pixels into vessel wall regions and non-vessel wall regions using machine learning models [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Automated Artery Localization and Vessel Wall Segmentation Using Tracklet Refinement and Polar Conversion

et al. 2020

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“…An optimal-surface graph-cut method (Opfront) [ 11 , 14 ] was used to refine the Bronchinet airway lumen segmentations and obtain the wall segmentation. Opfront performance was tuned on several parameters, which depend on the scan resolution and protocol.…”

Section: Methodsmentioning

confidence: 99%

Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction

et al. 2023

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Objectives Computed tomography (CT)–based bronchial parameters correlate with disease status. Segmentation and measurement of the bronchial lumen and walls usually require significant manpower. We evaluate the reproducibility of a deep learning and optimal-surface graph-cut method to automatically segment the airway lumen and wall, and calculate bronchial parameters. Methods A deep-learning airway segmentation model was newly trained on 24 Imaging in Lifelines (ImaLife) low-dose chest CT scans. This model was combined with an optimal-surface graph-cut for airway wall segmentation. These tools were used to calculate bronchial parameters in CT scans of 188 ImaLife participants with two scans an average of 3 months apart. Bronchial parameters were compared for reproducibility assessment, assuming no change between scans. Results Of 376 CT scans, 374 (99%) were successfully measured. Segmented airway trees contained a mean of 10 generations and 250 branches. The coefficient of determination (R2) for the luminal area (LA) ranged from 0.93 at the trachea to 0.68 at the 6th generation, decreasing to 0.51 at the 8th generation. Corresponding values for Wall Area Percentage (WAP) were 0.86, 0.67, and 0.42, respectively. Bland–Altman analysis of LA and WAP per generation demonstrated mean differences close to 0; limits of agreement (LoA) were narrow for WAP and Pi10 (± 3.7% of mean) and wider for LA (± 16.4–22.8% for 2–6th generations). From the 7th generation onwards, there was a sharp decrease in reproducibility and a widening LoA. Conclusion The outlined approach for automatic bronchial parameter measurement on low-dose chest CT scans is a reliable way to assess the airway tree down to the 6th generation. Statement on clinical relevance This reliable and fully automatic pipeline for bronchial parameter measurement on low-dose CT scans has potential applications in screening for early disease and clinical tasks such as virtual bronchoscopy or surgical planning, while also enabling the exploration of bronchial parameters in large datasets. Key Points • Deep learning combined with optimal-surface graph-cut provides accurate airway lumen and wall segmentations on low-dose CT scans. • Analysis of repeat scans showed that the automated tools had moderate-to-good reproducibility of bronchial measurements down to the 6thgeneration airway. • Automated measurement of bronchial parameters enables the assessment of large datasets with less man-hours.

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