Vessel-guided airway tree segmentation: A voxel classification approach

Lo, Pechin; Sporring, Jon; Ashraf, Haseem; Pedersen, Jesper Johannes Holst; Bruijne, Marleen de

doi:10.1016/j.media.2010.03.004

Cited by 132 publications

(156 citation statements)

References 45 publications

Supporting

Mentioning

154

Contrasting

Unclassified

Order By: Relevance

“…Lung fields, main bronchi and vessel tree were segmented as described in [11]. First, we applied the current-based registration [5] to register vessel trees and lung surfaces and computed the final deformation field for the whole image region.…”

Section: Methodsmentioning

confidence: 99%

Lung CT registration combining intensity, curves and surfaces

Gorbunova

Durrleman

et al. 2010

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Self Cite

View full text Add to dashboard Cite

In this paper we propose a registration method that combines intensity information with geometrical information in the form of curves and surfaces derived from lung CT images. Vessel tree centerlines and lung surfaces were extracted from segmented structures. First, a current-based registration was applied to align the pulmonary vessel tree and the lung surfaces. Subsequently, the resulting deformation field was used to constrain an intensity-based registration method. We applied the combined registration on a set of image pairs, extracted at the end exhale and the end inhale phases of 4D-CT scans. The proposed combined registration was compared to intensity-based registration, using a set of manually selected landmarks. The proposed registration decreases the mean and the standard deviation of the target registration errors for all 5 cases to on average 1.47±1.05 mm, compared to the intensity-based registration without constraint 1.74 ± 1.31 mm.Index Terms-Image registration, BSplines, currents, lung CT. INTRODUCTIONThe ultimate goal of any registration algorithm is to establish dense point-to-point correspondence between two images. Generally, registration of lung CT images is a difficult problem due to the possible large variation between the scans. Scans of the same patient taken at maximum inspiration, can have more than 0.5 liter difference in lung volume. The registration of end exhale and end inhale phases of 4D-CT lung images is an even more difficult problem due to the large and non-uniform deformations during the breathing cycle [1].Image registration methods can be divided into two groups of methods: intensity-based and feature-based. A feature-based method establishes deformations based on lowdimensional features, derived from the original images, while * This work is financially supported by the Danish Council for Strategic Research under the Programme Commission for Nanoscience and Technology, Biotechnology and IT (NABIIT), the Netherlands Organization for Scientific Research (NWO), and AstraZeneca, Lund, Sweden. Authors would like to thank Jon Sporring, Copenhagen University, Department of Computer Science, eScience Center, for fruitful discussions.intensity-based method considers intensity information over complete image. The state-of-the art registration methods for lung CT images are mainly intensity-based approaches [2] because the feature-based methods generally produce less accurate results [3].Recently, Li et al. [4] developed an image registration algorithm where the intensity-based registration was improved with the subsequent bio-mechanical simulation of lung inflation. Results showed an improvement in both accuracy of registration and physical plausibility of the deformation field for the combined approach. We previously developed a feature-based algorithm for registering lung CT images and compared it to intensity-based registration [5]. The overall accuracy of the feature-based algorithm was slightly worse than that of the intensity-based algorithm, but in 35 % of landmarks t...

show abstract

Section: Methodsmentioning

confidence: 99%

Lung CT registration combining intensity, curves and surfaces

Gorbunova

Durrleman

et al. 2010

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this paper, we follow voxel classification framework [7,8], but our voxel-level labeling is data-driven and statistically learned from the annotated lesion image masks [6,9] on a number of CT scans. Only voxel probability assignment and thresholding (e.g., suppressing background clutter) are employed to obtain a lesion class-probability response map.…”

Section: Introductionmentioning

confidence: 99%

Computer Aided Diagnosis Using Multilevel Image Features on Large-Scale Evaluation

Lü

Devarakota²,

Vikal³

et al. 2014

Medical Computer Vision. Large Data in Medical Imaging

View full text Add to dashboard Cite

Abstract. Computer aided diagnosis (CAD) of cancerous anatomical structures via 3D medical images has emerged as an intensively studied research area. In this paper, we present a principled three-tiered image feature learning approach to capture task specific and data-driven class discriminative statistics from an annotated image database. It integrates voxel-, instance-, and database-level feature learning, aggregation and parsing. The initial segmentation is proceeded as robust voxel labeling and thresholding. After instance-level spatial aggregation, extracted features can also be flexibly tuned for classifying lesions, or discriminating different subcategories of lesions. We demonstrate the effectiveness in the lung nodule detection task which handles all types of solid, partial-solid, and ground-glass nodules using the same set of learned features. Our hierarchical feature learning framework, which was extensively trained and validated on large-scale multiple site datasets of 879 CT volumes (510 training and 369 validation), achieves superior performance than other state-of-the-art CAD systems. The proposed method is also shown to be applicable for colonic polyp detection, including all polyp morphological subcategories, via 770 tagged-prep CT scans from multiple medical sites (358 training and 412 validation).

show abstract

“…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%

“…Top-down approaches start at a root point and propagate the tree segmentation into distant branches, for example by region growing [2,9]. These algorithms obtain the segmentation by energy-based image filtering techniques [3,7,13] which evaluate manually tuned cost functions at certain image locations.…”

Section: Introductionmentioning

confidence: 99%

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

Vessel-guided airway tree segmentation: A voxel classification approach

Cited by 132 publications

References 45 publications

Lung CT registration combining intensity, curves and surfaces

Lung CT registration combining intensity, curves and surfaces

Computer Aided Diagnosis Using Multilevel Image Features on Large-Scale Evaluation

Hierarchical Discriminative Framework for Detecting Tubular Structures in 3D Images

Contact Info

Product

Resources

About