Structure matching driven by joint-saliency-structure adaptive kernel regression

Qin, Binjie; Shen, Zhuangming; Zhou, Zien; Zhou, Jing; Lv, Yisong

doi:10.1016/j.asoc.2015.10.035

Cited by 10 publications

(2 citation statements)

References 77 publications

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“…There is still room in the future work to improve the accuracy of segmenting small branches of thin vessels and enhance temporal-spatial consistency of vessel tree mask. To achieve a reliable segmentation of all small and thin vessels in the low-contrast and noisy XCA sequence, the channel-wise attention scheme can be further integrated into pixe-wise (or superpixel wise) saliency-aware image matching [79,80] and segmentation [81] methods to automatically choose the key frame that contains the most salient small and thin vessels from the XCA sequence so that this frame's feature representation can be taken as priors for pixel-wise labeling in sequential vessel segmentation. Deep feature matching [82] and deep temporal-spatial correlation [83] in the image sequence can also be utilized to transfer the learning priors from key frame to its neighbouring frames containing unsharp small vessels.…”

Section: Discussionmentioning

confidence: 99%

Sequential vessel segmentation via deep channel attention network

et al. 2020

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Accurately segmenting contrast-filled vessels from X-ray coronary angiography (XCA) image sequence is an essential step for the diagnosis and therapy of coronary artery disease. However, developing automatic vessel segmentation is particularly challenging due to the overlapping structures, low contrast and the presence of complex and dynamic background artifacts in XCA images. This paper develops a novel encoder-decoder deep network architecture which exploits the several contextual frames of 2D+t sequential images in a sliding window centered at current frame to segment 2D vessel masks from the current frame. The architecture is equipped with temporal-spatial feature extraction in encoder stage, feature fusion in skip connection layers and channel attention mechanism in decoder stage. In the encoder stage, a series of 3D convolutional layers are employed to hierarchically extract temporal-spatial features. Skip connection layers subsequently fuse the temporal-spatial feature maps and deliver them to the corresponding decoder stages. To efficiently discriminate

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Section: Discussionmentioning

confidence: 99%

Sequential vessel segmentation via deep channel attention network

et al. 2020

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“…Therefore, the registration of the mask and contrast images is required to reduce the motion artifact before subtraction. However, due to the complex and dynamic backgrounds producing outliers [32,33] in the two images, the efficiency of this registration-based vessel extraction method is largely limited by the structure matching accuracy in the challenging image registration with outliers [34–36]. Recently, various low-dimensional representation learning techniques have been developed for the feature analysis of video data [37,38].…”

Section: Introductionmentioning

confidence: 99%

Accurate vessel extraction via tensor completion of background layer in X-ray coronary angiograms

Qin

Jin

Hao

et al. 2019

Pattern Recognition

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This paper proposes an effective method for accurately recovering vessel structures and intensity information from the X-ray coronary angiography (XCA) images of moving organs or tissues. Specifically, a global logarithm transformation of XCA images is implemented to fit the X-ray attenuation sum model of vessel/background layers into a low-rank, sparse decomposition model for vessel/background separation. The contrast-filled vessel structures are extracted by distinguishing the vessels from the low-rank backgrounds by using a robust principal component analysis and by constructing a vessel mask via Radon-like feature filtering plus spatially adaptive thresholding. Subsequently, the low-rankness and inter-frame spatio-temporal connectivity in the complex and noisy backgrounds are used to recover the vessel-masked background regions using tensor completion of all other background regions, while the twist tensor nuclear norm is minimized to complete the background layers. Finally, the method is able to accurately extract vessels’ intensities from the noisy XCA data by subtracting the completed background layers from the overall XCA images. We evaluated the vessel visibility of resulting images on real X-ray angiography data and evaluated the accuracy of vessel intensity recovery on synthetic data. Experiment results show the superiority of the proposed method over the state-of-the-art methods.

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Automatic deformable PET/MRI registration for preclinical studies based on B-splines and non-linear intensity transformation

Bricq

Kidane

Zavala-Bojorquez

et al. 2018

Med Biol Eng Comput

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PET images deliver functional data, whereas MRI images provide anatomical information. Merging the complementary information from these two modalities is helpful in oncology. Alignment of PET/MRI images requires the use of multi-modal registration methods. Most of existing PET/MRI registration methods have been developed for humans and few works have been performed for small animal images. We proposed an automatic tool allowing PET/MRI registration for pre-clinical study based on a two-level hierarchical approach. First, we applied a non-linear intensity transformation to the PET volume to enhance. The global deformation is modeled by an affine transformation initialized by a principal component analysis. A free-form deformation based on B-splines is then used to describe local deformations. Normalized mutual information is used as voxel-based similarity measure. To validate our method, CT images acquired simultaneously with the PET on tumor-bearing mice were used. Results showed that the proposed algorithm outperformed affine and deformable registration techniques without PET intensity transformation with an average error of 0.72 ± 0.44 mm. The optimization time was reduced by 23% due to the introduction of robust initialization. In this paper, an automatic deformable PET-MRI registration algorithm for small animals is detailed and validated. Graphical abstract ᅟ.

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Structure matching driven by joint-saliency-structure adaptive kernel regression

Cited by 10 publications

References 77 publications

Sequential vessel segmentation via deep channel attention network

Sequential vessel segmentation via deep channel attention network

Accurate vessel extraction via tensor completion of background layer in X-ray coronary angiograms

Automatic deformable PET/MRI registration for preclinical studies based on B-splines and non-linear intensity transformation

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