Supervised Feature Learning for Curvilinear Structure Segmentation

Becker, Carlos; Rigamonti, Roberto; Lepetit, Vincent; Fua, Pascal

doi:10.1007/978-3-642-40811-3_66

Cited by 128 publications

(138 citation statements)

References 18 publications

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“…. Precision-and-recall curves for pixelwise segmentation of curvilinear structures using path opening operator [27] with different setups of length, supervised feature learning [3], baseline MPP, and the proposed method.…”

Section: Methodsmentioning

confidence: 99%

“…Mathematical morphology operator, path opening [27], is applied on such gradient magnitude image (c). Linearity score of each pixel is drawn by the supervised feature learning algorithm [3] (d). We provide line hypotheses (e)-(g) associated with different hyperparameter vectors.…”

Section: Monte Carlo Sampler With Delayed Rejectionmentioning

confidence: 99%

“…Then, we integrate K probability maps into a mixture density PŜ : Fig. 3 compares image gradient magnitude, morphological filtering [27], supervised feature learning [3], line hypotheses, and the mixture density. Since the input image contains many high frequency components, its gradient also highlights non-linear structures in the background.…”

Section: Combination Of Line Hypotheses Into a Probability Mapmentioning

confidence: 99%

“…We integrate line hypotheses of the proposed MPP model into a mixture density PŜ . The mixture density shows the consensus between line hypotheses in the sense that the pixels corresponding to line structures are more highlighted when compared to [3,27].…”

Section: Combination Of Line Hypotheses Into a Probability Mapmentioning

confidence: 99%

“…Pixelwise segmentation for linear structure extraction measures the linearity for each pixel, and then sets up a threshold to remove out redundant outcomes [8,5,25,30]. Supervised learning algorithm [3] has been proposed to find optimal convolution kernels for extracting linear features. Mathematical morphology operator [27] can enhance thin line structures based on shape information.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Marked Point Process Model for Curvilinear Structures Extraction

Jeong¹,

Tarabalka²,

Zerubia³

2015

Lecture Notes in Computer Science

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Abstract. In this paper, we propose a new marked point process (MPP) model and the associated optimization technique to extract curvilinear structures. Given an image, we compute the intensity variance and rotated gradient magnitude along the line segment. We constrain high level shape priors of the line segments to obtain smoothly connected line configuration. The optimization technique consists of two steps to reduce the significance of the parameter selection in our MPP model. We employ Monte Carlo sampler with delayed rejection to collect line hypotheses over different parameter spaces. Then, we maximize the consensus among line detection results to reconstruct the most plausible curvilinear structures without parameter estimation process. Experimental results show that the algorithm effectively localizes curvilinear structures on a wide range of datasets.

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Section: Methodsmentioning

confidence: 99%

Section: Monte Carlo Sampler With Delayed Rejectionmentioning

confidence: 99%

Section: Combination Of Line Hypotheses Into a Probability Mapmentioning

confidence: 99%

Section: Combination Of Line Hypotheses Into a Probability Mapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Marked Point Process Model for Curvilinear Structures Extraction

Jeong¹,

Tarabalka²,

Zerubia³

2015

Lecture Notes in Computer Science

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Vessel segmentation from abdominal magnetic resonance images: adaptive and reconstructive approach

Göçeri

Shah

Gürcan

2016

Numer Methods Biomed Eng

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The liver vessels, which have low signal and run next to brighter bile ducts, are difficult to segment from MR images. This study presents a fully automated and adaptive method to segment portal and hepatic veins on magnetic resonance images. In the proposed approach, segmentation of these vessels is achieved in four stages: (i) initial segmentation, (ii) refinement, (iii) reconstruction, and (iv) post-processing. In the initial segmentation stage, k-means clustering is used, the results of which are refined iteratively with linear contrast stretching algorithm in the next stage, generating a mask image. In the reconstruction stage, vessel regions are reconstructed with the marker image from the first stage and the mask image from the second stage. Experimental data sets include slices that show fat tissues, which have the same gray level values with vessels, outside the margin of the liver. These structures are removed in the last stage. Results show that the proposed approach is more efficient than other thresholding-based methods. Copyright © 2016 John Wiley & Sons, Ltd.

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Supervised machine learning for coronary artery lumen segmentation in intravascular ultrasound images

Cui

Xia

Zhang

2020

Numer Methods Biomed Eng

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Intravascular ultrasound (IVUS) has been widely used to capture cross sectional lumen frames of inner wall of coronary arteries. This kind of medical imaging modalities is capable of providing detailed and significant information of lumen contour shape, which is very important for clinical diagnosis and analysis of cardiovascular diseases. Numerous learning based techniques have recently become very popular for coronary artery segmentation due to their impressive results. In this work, a supervised machine learning method for coronary artery lumen segmentation with high accuracy and minimal user interaction is designed. The fully discriminative lumen segmentation method jointly learning a classifier the weak learners rely on and the features of the classifier is developed. Additionally, the theoretical supports of the Gradient Boosting framework used in this work and its quadratic approximation are presented. The proposed algorithm is tested on the public datasets of boundary detection of lumen in IVUS challenge held in MICCAI 2011 and achieves a higher average Jaccard similarity of 96.8% and a lower mean error distance of 0.55 (in Cartesian coordinates), which shows higher accuracy compared to the existing learning based methods. Moreover, three real patient IVUS datasets are used to evaluate the performance of the proposed coronary artery lumen segmentation algorithm, which is shown to achieve lower percent error of lumen area of 1.861% ± 0.965%, 1.968% ± 0.864%, and 1.671% ± 0.584%, respectively, compared to the manually measured lumen area (ground truth). The proposed lumen segmentation method is found to be superior to the latest learning based segmentation techniques. Given the efficiency and robustness, our method has great potential in IVUS images processing and coronary artery segmentation and quantification.Novelty StatementThe main contributions are summarized in the following aspects: A detailed review of related work about learning based coronary artery lumen segmentation in intravascular ultrasound images is presented. A fully discriminative lumen segmentation method jointly learning a classifier our weak learners rely on and the features of the classifier is developed. The theoretical supports of the Gradient Boosting framework and its quadratic approximation used in this work are presented.

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Supervised Feature Learning for Curvilinear Structure Segmentation

Cited by 128 publications

References 18 publications

Marked Point Process Model for Curvilinear Structures Extraction

Marked Point Process Model for Curvilinear Structures Extraction

Vessel segmentation from abdominal magnetic resonance images: adaptive and reconstructive approach

Supervised machine learning for coronary artery lumen segmentation in intravascular ultrasound images

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