Variational Shape Representation for Modeling, Elastic Registration and Segmentation

Farag, A.A.M.; Shalaby, Ahmed; Munim, Hossam Abd El; Farag, Aly A.

doi:10.1007/978-3-319-03813-1_3

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Segmentation of medical images often takes shape information into account to produce reliable results, since otherwise, significant noise information will be captured. Analytical methods based on shape features have been used in medical image segmentation [25–27]. For example, Alvén et al [28] proposed a method of shape perception label fusion, which is different from label fusion at the voxel level.…”

Section: Related Workmentioning

confidence: 99%

Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model

Zhang

Zhou

et al. 2019

Computational Intelligence and Neuroscience

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Zebrafish embryo fluorescent vessel analysis, which aims to automatically investigate the pathogenesis of diseases, has attracted much attention in medical imaging. Zebrafish vessel segmentation is a fairly challenging task, which requires distinguishing foreground and background vessels from the 3D projection images. Recently, there has been a trend to introduce domain knowledge to deep learning algorithms for handling complex environment segmentation problems with accurate achievements. In this paper, a novel dual deep learning framework called Dual ResUNet is developed to conduct zebrafish embryo fluorescent vessel segmentation. To avoid the loss of spatial and identity information, the U-Net model is extended to a dual model with a new residual unit. To achieve stable and robust segmentation performance, our proposed approach merges domain knowledge with a novel contour term and shape constraint. We compare our method qualitatively and quantitatively with several standard segmentation models. Our experimental results show that the proposed method achieves better results than the state-of-art segmentation methods. By investigating the quality of the vessel segmentation, we come to the conclusion that our Dual ResUNet model can learn the characteristic features in those cases where fluorescent protein is deficient or blood vessels are overlapped and achieves robust performance in complicated environments.

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Section: Related Workmentioning

confidence: 99%

Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model

Zhang

Zhou

et al. 2019

Computational Intelligence and Neuroscience

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“…Here, one can calculate regularising functionals that incorporate such deformations. Finally, [9] provides an overview of variational shape models as applied to the registration and segmentation problems. These could also be coupled with variational regularisation methods for image reconstruction.…”

Section: Survey Of Existing Workmentioning

confidence: 99%

Spatiotemporal PET Reconstruction Using ML-EM with Learned Diffeomorphic Deformation

Öktem

Pouchol

Verdier

2019

Machine Learning for Medical Image Reconstruction

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Patient movement in emission tomography deteriorates reconstruction quality because of motion blur. Gating the data improves the situation somewhat: each gate contains a movement phase which is approximately stationary. A standard method is to use only the data from a few gates, with little movement between them. However, the corresponding loss of data entails an increase of noise. Motion correction algorithms have been implemented to take into account all the gated data, but they do not scale well, especially not in 3D. We propose a novel motion correction algorithm which addresses the scalability issue. Our approach is to combine an enhanced ML-EM algorithm with deep learning based movement registration. The training is unsupervised, and with artificial data. We expect this approach to scale very well to higher resolutions and to 3D, as the overall cost of our algorithm is only marginally greater than that of a standard ML-EM algorithm. We show that we can significantly decrease the noise corresponding to a limited number of gates.

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