Automatic Heart Isolation for CT Coronary Visualization Using Graph-Cuts

Funka-Lea, Gareth; Boykov, Yuri; Florin, Charles; Jolly, Marie-Pierre; Moreau-Gobard, Romain; Ramaraj, R.; Rinck, Daniel

doi:10.1109/isbi.2006.1624991

Cited by 59 publications

(53 citation statements)

References 13 publications

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“…Therefore, mean distances from 3.9 to 9.3 mm are perfectly acceptable. These results are also satisfactory if they are compared with the 5.5 mm of average error obtained by Funka-Lea et al in [13] on contrast CT exams with sub-millimeter resolutions.…”

Section: ∩A| |A|supporting

confidence: 74%

“…In particular, the new combined devices PET/CT and SPECT/CT, widely used in cardiology and oncology, often furnish non-contrast and low-resolution CT images. Our approach, as opposed to the one by Funka-Lea et al [13] for example, is adapted to this type of data.…”

Section: Resultsmentioning

confidence: 99%

“…However, there is also a need to segment the heart as a whole in order to distinguish its limits and the separations with surrounding structures such as the liver or the aorta. The existing methods for segmenting the heart have been focused on MRI modality [5,[7][8][9][10][11] or ultrasound [6,12] but rarely on CT [13][14][15][16]. However, for the aforementioned applications, CT is one of the most common anatomical imaging modalities.…”

Section: Related Workmentioning

confidence: 99%

“…The work by Funka-Lea et al [13] deals with the segmentation of the heart as a whole in CT. Their goal is to isolate the outer surface of the entire heart in order to easily visualize the coronary vessels. They make use of graph-cuts for the segmentation [17,18].…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Moreno

Takemura

Colliot

et al. 2008

Pattern Recognition

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International audienceSegmenting the heart in medical images is a challenging and important task for many applications. In particular, segmenting the heart in CT images is very useful for cardiology and oncological applications such as radiotherapy. Although the majority of methods in the literature are designed for ventricle segmentation, there is a real interest in segmenting the heart as a whole in this modality. In this paper, we address this problem and propose an automatic and robust method, based on anatomical knowledge about the heart, in particular its position with respect to the lungs. This knowledge is represented in a fuzzy formalism and it is used both to define a region of interest and to drive the evolution of a deformable model in order to segment the heart inside this region. The proposed method has been applied on non-contrast CT images and the obtained results have been compared to manual segmentations of the heart, showing the good accuracy and high robustness of our approach

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Section: ∩A| |A|supporting

confidence: 74%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Moreno

Takemura

Colliot

et al. 2008

Pattern Recognition

View full text Add to dashboard Cite

show abstract

“…However, the segmentation must be solved iteratively. In [7], a method that favors cuts that are orthogonal to the line from the current point to the center was proposed. This is effectively a prior for blob-like structures but does not solve the small cut problem.…”

Section: Joint Detection and Segmentationmentioning

confidence: 99%

Segmentation Based Features for Lymph Node Detection from 3-D Chest CT

Feulner

Zhou

Hammon

et al. 2011

Machine Learning in Medical Imaging

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Abstract. Lymph nodes routinely need to be considered in clinical practice in all kinds of oncological examinations. Automatic detection of lymph nodes from chest CT data is however challenging because of low contrast and clutter. Sliding window detectors using traditional features easily get confused by similar structures like muscles and vessels. It recently has been proposed to combine segmentation and detection to improve the detection performance. Features extracted from a segmentation that is initialized with a detection candidate can be used to train a classifier that decides whether the detection is a true or false positive. In this paper, the graph cuts method is adapted to the problem of lymph nodes segmentation. We propose a setting that requires only a single positive seed and at the same time solves the small cut problem of graph cuts. Furthermore, we propose a feature set that is extracted from the candidate segmentation. A classifier is trained on this feature set and used to reject false alarms. Cross validation on 54 CT datasets showed that the proposed system reaches a detection rate of 60.9% with only 6.1 false alarms per volume image, which is better than the current state of the art of mediastinal lymph node detection.

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Segmentation and tracking of lung nodules via graph‐cuts incorporating shape prior and motion from 4D CT

2017

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Purpose: We have developed a robust tool for performing volumetric and temporal analysis of nodules from respiratory gated four-dimensional (4D) CT. The method could prove useful in IMRT of lung cancer. Methods: We modified the conventional graph-cuts method by adding an adaptive shape prior as well as motion information within a signed distance function representation to permit more accurate and automated segmentation and tracking of lung nodules in 4D CT data. Active shape models (ASM) with signed distance function were used to capture the shape prior information, preventing unwanted surrounding tissues from becoming part of the segmented object. The optical flow method was used to estimate the local motion and to extend three-dimensional (3D) segmentation to 4D by warping a prior shape model through time. The algorithm has been applied to segmentation of wellcircumscribed, vascularized, and juxtapleural lung nodules from respiratory gated CT data. Results: In all cases, 4D segmentation and tracking for five phases of high-resolution CT data took approximately 10 min on a PC workstation with AMD Phenom II and 32 GB of memory. The method was trained based on 500 breath-held 3D CT data from the LIDC data base 1 and was tested on 17 4D lung nodule CT datasets consisting of 85 volumetric frames. The validation tests resulted in an average Dice Similarity Coefficient (DSC) = 0.68 for all test data. An important by-product of the method is quantitative volume measurement from 4D CT from end-inspiration to end-expiration which will also have important diagnostic value. Conclusion: The algorithm performs robust segmentation of lung nodules from 4D CT data. Signed distance ASM provides the shape prior information which based on the iterative graph-cuts framework is adaptively refined to best fit the input data, preventing unwanted surrounding tissue from merging with the segmented object.

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Automatic Heart Isolation for CT Coronary Visualization Using Graph-Cuts

Cited by 59 publications

References 13 publications

Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Segmentation Based Features for Lymph Node Detection from 3-D Chest CT

Segmentation and tracking of lung nodules via graph‐cuts incorporating shape prior and motion from 4D CT

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