Robust 3-D Airway Tree Segmentation for Image-Guided Peripheral Bronchoscopy

Graham, Michael W.; Gibbs, Jason D.; Cornish, Duane C.; Higgins, William E.

doi:10.1109/tmi.2009.2035813

Cited by 88 publications

(89 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite that, the results suggest that the algorithm is suitable for clinical setting, e.g. for bronchoscopic navigation [5,15], since most detected centerlines are within the airway boundaries. The tree extraction algorithm is efficient.…”

Section: Airway Trees In Rotational X-raymentioning

confidence: 86%

“…These algorithms model the tubular pattern directly and use discriminative classifiers on features describing low-level image characteristics [5,10,20]. The classifier responses are then used in a region growing algorithm to find the tree segmentations.…”

Section: Related Workmentioning

confidence: 99%

“…The detected centerlines have radius error within 1 mm of ground truth for 79.1% points. Please note, that this strict evaluation is performed at the centerline level rather than the branch level as in [5], for example. Despite that, the results suggest that the algorithm is suitable for clinical setting, e.g.…”

Section: Airway Trees In Rotational X-raymentioning

confidence: 99%

“…Accurate and efficient detection of tree-like structures such as airways or vessels in 3D medical images is important for accurate diagnosis, treatment monitoring, and surgical planning [1,5,8]. The segmentation of such structures is challenging due to noise, imaging artifacts, pathologies, and low-resolution of images (see Fig.…”

Section: Introductionmentioning

confidence: 99%

“…1 for examples). Despite these challenges, the segmented trees are required to be complete and have low number of false branches to make the results accurate for diagnostic and interventional procedures [5,9,15]. In this paper, we propose an efficient algorithm that addresses the challenges above and produces accurate tree detection.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Hierarchical Discriminative Framework for Detecting Tubular Structures in 3D Images

Breitenreicher

Sofka

Britzen

et al. 2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Detecting tubular structures such as airways or vessels in medical images is important for diagnosis and surgical planning. Many state-of-the-art approaches address this problem by starting from the root and progressing towards thinnest tubular structures usually guided by image filtering techniques. These approaches need to be tailored for each application and can fail in noisy or lowcontrast regions. In this work, we address these challenges by a two-layer model which consists of a low-level likelihood measure and a high-level measure verifying tubular branches. The algorithm starts by computing a robust measure of tubular presence using a discriminative classifier at multiple image scales. The measure is then used in an efficient multi-scale shortest path algorithm to generate candidate centerline branches and corresponding radii measurements. Finally, the branches are verified by a learning-based indicator function that discards false candidate branches. The experiments on detecting airways in rotational X-ray volumes show that the technique is robust to noise and correctly finds airways even in the presence of imaging artifacts.

show abstract

Section: Airway Trees In Rotational X-raymentioning

confidence: 86%

Section: Related Workmentioning

confidence: 99%

Section: Airway Trees In Rotational X-raymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Hierarchical Discriminative Framework for Detecting Tubular Structures in 3D Images

Breitenreicher

Sofka

Britzen

et al. 2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

Enhancement of Semantic Segmentation by Image‐Level Fine‐Tuning to Overcome Image Pattern Imbalance in HRCT of Diffuse Infiltrative Lung Diseases

Ham,

Park,

Yun

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Diagnosing diffuse infiltrative lung diseases (DILD) using high‐resolution computed tomography (HRCT) is challenging, even for expert radiologists, due to the complex and variable image patterns. Moreover, the imbalances among the six key DILD‐related patterns—normal, ground‐glass opacity, reticular opacity, honeycombing, emphysema, and consolidation—further complicate accurate segmentation and diagnosis. This study presents an enhanced U‐Net‐based segmentation technique aimed at addressing these challenges. The primary contribution of our work is the fine‐tuning of the U‐Net model using image‐level labels from 92 HRCT images that include various types of DILDs, such as cryptogenic organizing pneumonia, usual interstitial pneumonia, and nonspecific interstitial pneumonia. This approach helps to correct the imbalance among image patterns, improving the model's ability to accurately differentiate between them. By employing semantic lung segmentation and patch‐level machine learning, the fine‐tuned model demonstrated improved agreement with radiologists' evaluations compared to conventional methods. This suggests a significant enhancement in both segmentation accuracy and inter‐observer consistency. In conclusion, the fine‐tuned U‐Net model offers a more reliable tool for HRCT image segmentation, making it a valuable imaging biomarker for guiding treatment decisions in patients with DILD. By addressing the issue of pattern imbalances, our model significantly improves the accuracy of DILD diagnosis, which is crucial for effective patient care.

show abstract

Integrating local and distant radiation‐induced lung injury: Development and validation of a predictive model for ventilation loss

Vicente,

Grande Gutierrez,

Oakes

et al. 2024

Medical Physics

View full text Add to dashboard Cite

BackgroundInvestigations on radiation‐induced lung injury (RILI) have predominantly focused on local effects, primarily those associated with radiation damage to lung parenchyma. However, recent studies from our group and others have revealed that radiation‐induced damage to branching serial structures such as airways and vessels may also have a substantial impact on post‐radiotherapy (RT) lung function. Furthermore, recent results from multiple functional lung avoidance RT trials, although promising, have demonstrated only modest toxicity reduction, likely because they were primarily focused on dose avoidance to lung parenchyma. These observations emphasize the critical need for predictive dose‐response models that effectively incorporate both local and distant RILI effects.PurposeWe develop and validate a predictive model for ventilation loss after lung RT. This model, referred to as P+A, integrates local (parenchyma [P]) and distant (central and peripheral airways [A]) radiation‐induced damage, modeling partial (narrowing) and complete (collapse) obstruction of airways.MethodsIn an IRB‐approved prospective study, pre‐RT breath‐hold CTs (BHCTs) and pre‐ and one‐year post‐RT 4DCTs were acquired from lung cancer patients treated with definitive RT. Up to 13 generations of airways were automatically segmented on the BHCTs using a research virtual bronchoscopy software. Ventilation maps derived from the 4DCT scans were utilized to quantify pre‐ and post‐RT ventilation, serving, respectively, as input data and reference standard (RS) in model validation. To predict ventilation loss solely due to parenchymal damage (referred to as P model), we used a normal tissue complication probability (NTCP) model. Our model used this NTCP‐based estimate and predicted additional loss due radiation‐induced partial or complete occlusion of individual airways, applying fluid dynamics principles and a refined version of our previously developed airway radiosensitivity model. Predictions of post‐RT ventilation were estimated in the sublobar volumes (SLVs) connected to the terminal airways. To validate the model, we conducted a k‐fold cross‐validation. Model parameters were optimized as the values that provided the lowest root mean square error (RMSE) between predicted post‐RT ventilation and the RS for all SLVs in the training data. The performance of the P+A and the P models was evaluated by comparing their respective post‐RT ventilation values with the RS predictions. Additional evaluation using various receiver operating characteristic (ROC) metrics was also performed.ResultsWe extracted a dataset of 560 SLVs from four enrolled patients. Our results demonstrated that the P+A model consistently outperformed the P model, exhibiting RMSEs that were nearly half as low across all patients (13 ± 3 percentile for the P+A model vs. 24 ± 3 percentile for the P model on average). Notably, the P+A model aligned closely with the RS in ventilation loss distributions per lobe, particularly in regions exposed to doses ≥13.5 Gy. The ROC analysis further supported the superior performance of the P+A model compared to the P model in sensitivity (0.98 vs. 0.07), accuracy (0.87 vs. 0.25), and balanced predictions.ConclusionsThese early findings indicate that airway damage is a crucial factor in RILI that should be included in dose‐response modeling to enhance predictions of post‐RT lung function.

show abstract

Robust 3-D Airway Tree Segmentation for Image-Guided Peripheral Bronchoscopy

Cited by 88 publications

References 38 publications

Hierarchical Discriminative Framework for Detecting Tubular Structures in 3D Images

Hierarchical Discriminative Framework for Detecting Tubular Structures in 3D Images

Enhancement of Semantic Segmentation by Image‐Level Fine‐Tuning to Overcome Image Pattern Imbalance in HRCT of Diffuse Infiltrative Lung Diseases

Integrating local and distant radiation‐induced lung injury: Development and validation of a predictive model for ventilation loss

Contact Info

Product

Resources

About