AN iterative model-constrained graph-cut algorithm for Abdominal Aortic Aneurysm thrombus segmentation

Freiman, Moti; Esses, Steven J.; Joskowicz, Leo

doi:10.1109/isbi.2010.5490085

Cited by 29 publications

(27 citation statements)

References 14 publications

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“…3 shows a representative example. These results are comparable to previously reported methods [11,12,13,14] without their underlying assumptions and with a significant improvement in the running time.…”

Section: Resultssupporting

confidence: 89%

Anatomical Structures Segmentation by Spherical 3D Ray Casting and Gradient Domain Editing

Kronman

Joskowicz

2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. Fuzzy boundaries of anatomical structures in medical images make segmentation a challenging task. We present a new segmentation method that addresses the fuzzy boundaries problem. Our method maps the lengths of 3D rays cast from a seed point to the unit sphere, estimates the fuzzy boundaries location by thresholding the gradient magnitude of the rays lengths, and derives the true boundaries by Laplacian interpolation on the sphere. Its advantages are that it does not require a global shape prior or curvature based constraints, that it has an automatic stopping criteria, and that it is robust to anatomical variability, noise, and parameters values settings. Our experimental evaluation on 23 segmentations of kidneys and on 16 segmentations of abdominal aortic aneurysms (AAA) from CT scans yielded an average volume overlap error of 12.6% with respect to the ground-truth. These results are comparable to those of other segmentation methods without their underlying assumptions.

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Section: Resultssupporting

confidence: 89%

Anatomical Structures Segmentation by Spherical 3D Ray Casting and Gradient Domain Editing

Kronman

Joskowicz

2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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“…Several methods for abdominal aortic segmentation using graph cuts have been proposed (Duquette et al, 2012;Freiman et al, 2010). The method proposed in (Duquette et al, 2012) is able to perform segmentation in MR images in addition to CT ones.…”

Section: In Order To Analyze This Kind Of Information (2d 3d Ct or Mmentioning

confidence: 99%

Automatic Abdominal Aortic Aneurysm segmentation in MR images

Martinez-Muñoz

Ruiz-Fernández

Galiana-Merino

2016

Expert Systems with Applications

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1 Highlights  We use a spatial fuzzy C-means algorithm to detect and segment the lumen  We use a graph cut algorithm to segment the aortic wall  The detection and segmentation process is fully automatic  We get a 79% overlapping between our segmentation and the one from the specialist ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T Abdominal Aortic Aneurism is a disease related to a weakening in the aortic wall that can cause a break in the aorta and the death. The detection of an unusual dilatation of a section of the aorta is an indicative of this disease. However, it is difficult to diagnose because it is necessary image diagnosis using computed tomography or magneticresonance. An automatic diagnosis system would allow to analyze abdominal magnetic resonance images and to warn doctors if any anomaly is detected. We focus our research in magnetic resonance images because of the absence of ionizing radiation.Although there are proposals to identify this disease in magnetic resonance images, they need an intervention from clinicians to be precise and some of them are computationally hard. In this paper we develop a novel approach to analyze magnetic resonance abdominal images and detect the lumen and the aortic wall. The method combines different algorithms in two stages to improve the detection and the segmentation so it can be applied to similar problems with other type of images or structures. In a first stage, we use a spatial fuzzy C-means algorithm with morphological image analysis to detect and segment the lumen; and subsequently, in a second stage, we apply a graph cut algorithm to segment the aortic wall. The obtained results in the analyzed images are pretty successful obtaining an average of 79% of overlapping between the automatic segmentation provided by our method and the aortic wall identified by a medical specialist. The main impact of the proposed method is that it works in a completely automatic way with a low computational cost, which is of great significance for any expert and intelligent system.

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“…Several AAA thrombus segmentation methods have been recently developed. The method by De Bruijne et al [21] is an interactive contour tracking method for axial slices; Olabarriaga et al [22] employ a deformable model approach based on a nonparametric statistical greylevel appearance model to determine the deformable model adaptation direction starting from a lumen contour shape interactive segmentation; Zhuge et al [23] present a level-set segmentation based on a parametric statistical model; Demirci et al [24] propose a deformable B-spline parametric model based on a nonparametric intensity distribution model and; Freiman et al [25] apply a an iterative model-constrained graphcut algorithm. All these methods involve at least as much user interaction as the AL approach, either for initialization or during the algorithm evolution.…”

Section: The Medical Image Problemmentioning

confidence: 99%

“…The CTA images used in the computational experiments were provided by the group of Leo Joskowicz [25] of the Mount Sinai School of Medicine, New York, NY. Borja Ayerdi has a predoctoral grant from the Basque Government.…”

Section: Acknowledgementsmentioning

confidence: 99%

Applications of Hybrid Extreme Rotation Forests for image segmentation

Ayerdi

Maiora

D’Anjou

2013

HIS

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This paper introduces the Hybrid Extreme Rotation Forest (HERF) classifier describing two succesful applications in the image segmentation domain. The HERF is an ensemble of classifiers composed of Extreme Learning Machines (ELM) and Decision Trees. Training of the HERF includes optimal rotation of random partitions of the feature set aimed to increase diversity. The first application is the segmentation of 3D Computed Tomography Angiography (CTA) following an Active Learning (AL) strategy for the optimal sample selection to minimize the number of data samples needed to obtain a required accuracy degree. AL is pertinent for interactive learning processes where a human operator is required to select training samples to enhance the classifier in an iterative process, therefore labeling samples for training may be a time consuming and expensive process. CTA image segmentation is one of such processes, due to the variability in CTA images which hinders the generalization of classifiers trained on one dataset to new datasets. Following an AL strategy, the human operator is presented with a visual selection of pixels whose labeling would be most informative for the classifier. After adding those labeled pixels to the training data, the classifier is retrained. This iteration is repeated until image segmentation quality meets the required level. The approach is applied to the segmentation of the thrombus in CTA imaging of Abdominal Aortic Aneurysm (AAA) patients, showing that the structures of interest can be accurately segmented after a few iterations using a small data sample. The second application is a new semisupervised segmentation algorithm for hyperspectral images. The algorithm steps are: 1) supervised training an initial classifier from a small balanced training set, 2) clustering of the image pixels, by a k-means algorithm 3) adding unlabeled pixels to the original trainning data set according to the spatial neighborhood and the cluster membership, 4) supervised training of the classifier with the enriched training data set, 6) classification of the hyperspectral image 4) spatial regularization of classification results consisting in selecting the most frequent class in each pixel neighborhood. Results on two well known benchmarking hyperspectral images improve over state of the art algorithms.

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AN iterative model-constrained graph-cut algorithm for Abdominal Aortic Aneurysm thrombus segmentation

Cited by 29 publications

References 14 publications

Anatomical Structures Segmentation by Spherical 3D Ray Casting and Gradient Domain Editing

Anatomical Structures Segmentation by Spherical 3D Ray Casting and Gradient Domain Editing

Automatic Abdominal Aortic Aneurysm segmentation in MR images

Applications of Hybrid Extreme Rotation Forests for image segmentation

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