Segmentation by retrieval with guided random walks: Application to left ventricle segmentation in MRI

Eslami, Abouzar; Karamalis, Athanasios; Katouzian, Amin; Navab, Nassir

doi:10.1016/j.media.2012.10.005

Cited by 70 publications

(46 citation statements)

References 34 publications

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“…To quantify the segmentation quality assessment for the proposed method we compute the following measures: overlap, sensitivity, specificity and similarity * . Moreover, the proposed method is compared with the latest techniques in random walks for prior incorporation such as guided random walks 17 and constrained random walks 18 as well as the non-rigid registration described in Section 2.2 as a baseline. All parameters are optimized manually for all approaches.…”

Section: Resultsmentioning

confidence: 99%

Cochlea segmentation using iterated random walks with shape prior

Pujadas

Kjer

Vera³

et al. 2016

SPIE Proceedings

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Cochlear implants can restore hearing to deaf or partially deaf patients. In order to plan the intervention, a model from high resolution µCT images is to be built from accurate cochlea segmentations and then, adapted to a patient-specific model. Thus, a precise segmentation is required to build such a model. We propose a new framework for segmentation of µCT cochlear images using random walks where a region term is combined with a distance shape prior weighted by a confidence map to adjust its influence according to the strength of the image contour. Then, the region term can take advantage of the high contrast between the background and foreground and the distance prior guides the segmentation to the exterior of the cochlea as well as to less contrasted regions inside the cochlea. Finally, a refinement is performed preserving the topology using a topological method and an error control map to prevent boundary leakage. We tested the proposed approach with 10 datasets and compared it with the latest techniques with random walks and priors. The experiments suggest that this method gives promising results for cochlea segmentation.

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Section: Resultsmentioning

confidence: 99%

Cochlea segmentation using iterated random walks with shape prior

Pujadas

Kjer

Vera³

et al. 2016

SPIE Proceedings

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“…Grab cut 21 extends graph cut by introducing an iterative segmentation scheme. There are also other graph-based image segmentation methods in the literature, such as random walk, 22 however, the selection of initial seeds on labels of interest is difficult in slow intensity varying medical images. Despite having a huge literature on graph-based algorithms, more attempts are still being made to develop a robust, suitable, and user friendly method.…”

Section: Graph-based Methodsmentioning

confidence: 99%

Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets

2015

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Abstract. Liver segmentation continues to remain a major challenge, largely due to its intense complexity with surrounding anatomical structures (stomach, kidney, and heart), high noise level and lack of contrast in pathological computed tomography (CT) data. We present an approach to reconstructing the liver surface in low contrast CT. The main contributions are: (1) a stochastic resonance-based methodology in discrete cosine transform domain is developed to enhance the contrast of pathological liver images, (2) a new formulation is proposed to prevent the object boundary, resulting from the cellular automata method, from leaking into the surrounding areas of similar intensity, and (3) a level-set method is suggested to generate intermediate segmentation contours from two segmented slices distantly located in a subject sequence. We have tested the algorithm on real datasets obtained from two sources, Hamad General Hospital and medical image computing and computerassisted interventions grand challenge workshop. Various parameters in the algorithm, such as w , Δt , z, α, μ, α 1 , and α 2 , play imperative roles, thus their values are precisely selected. Both qualitative and quantitative evaluation performed on liver data show promising segmentation accuracy when compared with ground truth data reflecting the potential of the proposed method.

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“…Lynch et al [3] used a levelset method and took temporal information into consideration. Eslami et al [24] proposed a guided random walk method. Instead of finding the LV chamber, they targeted for segmenting the ventricle wall.…”

Section: B Mri Image Segmentationmentioning

confidence: 99%

Estimation of the Volume of the Left Ventricle From MRI Images Using Deep Neural Networks

Liao

Chen

et al. 2019

IEEE Trans. Cybern.

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Abstract-Segmenting human left ventricle (LV) in magnetic resonance imaging (MRI) images and calculating its volume are important for diagnosing cardiac diseases. In 2016, Kaggle organized a competition to estimate the volume of LV from MRI images. The dataset consisted of a large number of cases, but only provided systole and diastole volumes as labels. We designed a system based on neural networks to solve this problem. It began with a detector combined with a neural network classifier for detecting regions of interest (ROIs) containing LV chambers. Then a deep neural network named hypercolumns fully convolutional network was used to segment LV in ROIs. The 2D segmentation results were integrated across different images to estimate the volume. With ground-truth volume labels, this model was trained end-to-end. To improve the result, an additional dataset with only segmentation label was used. The model was trained alternately on these two datasets with different types of teaching signals. We also proposed a variance estimation method for the final prediction. Our algorithm ranked the 4th on the test set in this competition.

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Segmentation by retrieval with guided random walks: Application to left ventricle segmentation in MRI

Cited by 70 publications

References 34 publications

Cochlea segmentation using iterated random walks with shape prior

Cochlea segmentation using iterated random walks with shape prior

Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets

Estimation of the Volume of the Left Ventricle From MRI Images Using Deep Neural Networks

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