3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilatation (PHVD) using multi-phase geodesic level-sets

Qiu, Wu; Yuan, Jing; Rajchl, Martin; Kishimoto, Jessica; Chen, Yi-Min; Ribaupierre, Sandrine de; Chiu, Bernard; Fenster, Aaron

doi:10.1016/j.neuroimage.2015.05.099

Cited by 20 publications

(24 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…37 Qui et al developed an algorithm for volumetric measurement of the ventricles with good results. 38 Segmentation of PTs, using high-quality MRI scans, may be a good estimate of the true PT volume as compared with traditional geometrical equations. However, the true PT volume can only be attained by anatomical dissection or by measurement of the tumor volume resected in the operating room.…”

Section: Discussionmentioning

confidence: 99%

Three-Dimensional Volumetric Segmentation of Pituitary Tumors: Assessment of Inter-rater Agreement and Comparison with Conventional Geometric Equations

et al. 2018

View full text Add to dashboard Cite

The assessment of pituitary tumor (PT) volume is important in the treatment and follow-up of patients with PT. Previously, PT volume estimation has been performed by conventional geometric equations (CGE) such as (simplified ellipsoid volume equation) and (sphere), both presuming a symmetric tumor shape, which occurs uncommonly in patients with PT. In contrast, three-dimensional (3D) voxel-based software segmentation takes the irregular and asymmetric shapes that PTs often possess into account and might be a more accurate method for PT volume segmentation. The purpose of this study is twofold. (1) To compare 3D segmentation with CGE for PT volume estimation. (2) To assess inter-rater reliability in 3D segmentation of PTs. Nineteen high-resolution (1mm slice thickness) T1-weighted MRI examinations of patients with PT were independently analyzed and manually segmented, using the software ITK-SNAP, by two certified neuroradiologists. Concurrently, the volumes of the PTs were estimated with and by a clinician, and the results were compared with the corresponding segmented volumes. There was a significant decrease in PT volume attained from the segmentations compared with the calculations made with ( < 0.001, mean volume 18% higher than segmentation) and ( < 0.001, mean volume 28% higher than segmentation). The intraclass correlation coefficient (ICC) for the two sets of segmented PTs was 0.99. CGE ( and ) significantly overestimates PT volume compared with 3D volumetric segmentation. The inter-rater agreement on manual 3D volumetric software segmentation is excellent.

show abstract

Section: Discussionmentioning

confidence: 99%

Three-Dimensional Volumetric Segmentation of Pituitary Tumors: Assessment of Inter-rater Agreement and Comparison with Conventional Geometric Equations

et al. 2018

View full text Add to dashboard Cite

show abstract

“…It indicates that depending on only this propagation framework is insufficient to handle this substructure. Perhaps incorporating the boundary-based segmentation technique [32] into this process will improve this limitation.…”

Section: Discussionmentioning

confidence: 99%

Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images

Yang

Xie

et al. 2017

BioMed Eng OnLine

View full text Add to dashboard Cite

BackgroundDual-source computed tomography (DSCT) is a very effective way for diagnosis and treatment of heart disease. The quantitative information of spatiotemporal DSCT images can be important for the evaluation of cardiac function. To avoid the shortcoming of manual delineation, it is imperative to develop an automatic segmentation technique for 4D cardiac images.MethodsIn this paper, we implement the heart segmentation-propagation framework based on nonrigid registration. The corresponding points of anatomical substructures are extracted by using the extension of n-dimensional scale invariant feature transform method. They are considered as a constraint term of nonrigid registration using the free-form deformation, in order to restrain the large variations and boundary ambiguity between subjects.ResultsWe validate our method on 15 patients at ten time phases. Atlases are constructed by the training dataset from ten patients. On the remaining data the median overlap is shown to improve significantly compared to original mutual information, in particular from 0.4703 to 0.5015 () for left ventricle myocardium and from 0.6307 to 0.6519 () for right atrium.ConclusionsThe proposed method outperforms standard mutual information of intensity only. The segmentation errors had been significantly reduced at the left ventricle myocardium and the right atrium. The mean surface distance of using our framework is around 1.73 mm for the whole heart.

show abstract

“…In future work, we mainly focus on three directions but not limited: Firstly, we will extend the current model using multi-phase segmentation method (Qiu, et al, 2015), (Kato, et al, 2015) or region-based convolutional networks (Girshick, et al, 2016). Secondly, we will further research the set of control parameters and improve the efficiency by multi-core CPU (Stegmaier, et al, 2016) or GPU acceleration technology (Zhou, He and Qiu, 2016).…”

Section: Discussionmentioning

confidence: 99%

An efficient similarity-based level set model for medical image segmentation

Pan

et al. 2016

JAMDSM

View full text Add to dashboard Cite

It is usually difficult to correctly segment medical images with intensity inhomogeneity, which is of great significance in understanding of medical images. The local image intensity features play a vital role in accurately segmenting medical images with intensity inhomogeneity. Therefore, it is crucial to acquire the local intensity features for a deeper understanding of medical images. The main idea of this paper is to construct an efficient similarity-based level set model, which synthesizes the similarity theory, curve evolution and level set. Firstly, a local statistical function is modeled as different scales of Gaussian distributions to estimate bias fields, in which a real image can be approximately obtained for a more accurate medical image segmentation. Secondly, a new potential function is constructed to maintain the stability of the curve evolution, especially the signed distance profile in the neighborhood of the zero level set, which plays an important role in the correct segmentation. Thirdly, an adaptive condition criterion has been proposed to accelerate the convergence in the curve processing. Finally, the experiments on artificial and medical images and comparisons with the current well-known region-based models are discussed in details. Our extensive experimental results demonstrate that the proposed method can correctly segment medical images with intensity inhomogeneity in a few iterations and also is less sensitive to the initial contour.

show abstract

3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilatation (PHVD) using multi-phase geodesic level-sets

Cited by 20 publications

References 76 publications

Three-Dimensional Volumetric Segmentation of Pituitary Tumors: Assessment of Inter-rater Agreement and Comparison with Conventional Geometric Equations

Three-Dimensional Volumetric Segmentation of Pituitary Tumors: Assessment of Inter-rater Agreement and Comparison with Conventional Geometric Equations

Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images

An efficient similarity-based level set model for medical image segmentation

Contact Info

Product

Resources

About