3D-SIFT-Flow for atlas-based CT liver image segmentation

Xu, Yan; Xu, Chenchao; Xiao, Kun; Wang, Hongkai; Chang, Eric; Huang, Weimin; Fan, Yubo

doi:10.1118/1.4945021

Cited by 21 publications

(18 citation statements)

References 49 publications

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“…In these approaches, image segmentations are divided into a number of functional modules, and numerous hand-crafted signal processing algorithms and image features are examined according to human experience and knowledge. Although some mathematical models [4][5][6][7][8][9][10][11] have recently been introduced, conventional CT image segmentation methods still attempt to emulate limited human-specified rules or operations in segmenting CT images directly. These methods can achieve reasonable segmentation results on CT images for a special organ type within a known narrow scan range.…”

Section: Introductionmentioning

confidence: 99%

Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

et al. 2017

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Purpose: We propose a single network trained by pixel-to-label deep learning to address the general issue of automatic multiple organ segmentation in three-dimensional (3D) computed tomography (CT) images. Our method can be described as a voxel-wise multiple-class classification scheme for automatically assigning labels to each pixel/voxel in a 2D/3D CT image. Methods: We simplify the segmentation algorithms of anatomical structures (including multiple organs) in a CT image (generally in 3D) to a majority voting scheme over the semantic segmentation of multiple 2D slices drawn from different viewpoints with redundancy. The proposed method inherits the spirit of fully convolutional networks (FCNs) that consist of "convolution" and "deconvolution" layers for 2D semantic image segmentation, and expands the core structure with 3D-2D-3D transformations to adapt to 3D CT image segmentation. All parameters in the proposed network are trained pixel-to-label from a small number of CT cases with human annotations as the ground truth. The proposed network naturally fulfills the requirements of multiple organ segmentations in CT cases of different sizes that cover arbitrary scan regions without any adjustment. Results: The proposed network was trained and validated using the simultaneous segmentation of 19 anatomical structures in the human torso, including 17 major organs and two special regions (lumen and content inside of stomach). Some of these structures have never been reported in previous research on CT segmentation. A database consisting of 240 (95% for training and 5% for testing) 3D CT scans, together with their manually annotated ground-truth segmentations, was used in our experiments. The results show that the 19 structures of interest were segmented with acceptable accuracy (88.1% and 87.9% voxels in the training and testing datasets, respectively, were labeled correctly) against the ground truth. Conclusions: We propose a single network based on pixel-to-label deep learning to address the challenging issue of anatomical structure segmentation in 3D CT cases. The novelty of this work is the policy of deep learning of the different 2D sectional appearances of 3D anatomical structures for CT cases and the majority voting of the 3D segmentation results from multiple crossed 2D sections to achieve availability and reliability with better efficiency, generality, and flexibility than conventional segmentation methods, which must be guided by human expertise.

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Section: Introductionmentioning

confidence: 99%

Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

et al. 2017

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“…Memory and runtime requirement of SIFT- and SURF-based approaches also may limit their broad application. For example, 3D-SIFT-Flow estimating optical flows between 512×512×256 CT images of the liver required 24 GB memory to store SIFT features of the two CT images (Xu et al 2016). In addition, since both descriptors are constructed based on directions of image gradients, they are not directly applicable to multimodality image registration (i.e., gradient directions of distinct modality images at corresponding points can be either same or opposite).…”

Section: Discussionmentioning

confidence: 99%

Performance evaluation of MIND demons deformable registration of MR and CT images in spinal interventions

et al. 2016

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Accurate intraoperative localization of target anatomy and adjacent nervous and vascular tissue is essential to safe, effective surgery, and multimodality deformable registration can be used to identify such anatomy by fusing preoperative CT or MR images with intraoperative images. A deformable image registration method has been developed to estimate viscoelastic diffeomorphisms between preoperative MR and intraoperative CT using modality-independent neighborhood descriptors (MIND) and a Huber metric for robust registration. The method, called MIND Demons, optimizes a constrained symmetric energy functional incorporating priors on smoothness, geodesics, and invertibility by alternating between Gauss-Newton optimization and Tikhonov regularization in a multiresolution scheme. Registration performance was evaluated for the MIND Demons method with a symmetric energy formulation in comparison to an asymmetric form, and sensitivity to anisotropic MR voxel-size was analyzed in phantom experiments emulating image-guided spine-surgery in comparison to a free-form deformation (FFD) method using local mutual information (LMI). Performance was validated in a clinical study involving 15 patients undergoing intervention of the cervical, thoracic, and lumbar spine. The target registration error (TRE) for the symmetric MIND Demons formulation [1.3 ± 0.8 mm (median ± interquartile)] outperformed the asymmetric form [3.6 ± 4.4 mm]. The method demonstrated fairly minor sensitivity to anisotropic MR voxel size, with median TRE ranging 1.3 – 2.9 mm for MR slice thickness ranging 0.9 – 9.9 mm, compared to TRE = 3.2 – 4.1 mm for LMI FFD over the same range. Evaluation in clinical data demonstrated sub-voxel TRE (< 2 mm) in all fifteen cases with realistic deformations that preserved topology with sub-voxel invertibility (0.001 mm) and positive-determinant spatial Jacobians. The approach therefore appears robust against realistic anisotropic resolution characteristics in MR and yields registration accuracy suitable to application in image-guided spine-surgery.

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“…Xu et al [10] developed a 3D-scale invariant feature transform-based registration and designed an objective function to label the target image for liver segmentation. Salman et al [11] discovered a feature-constrained Mahalanobis distance cost function to determine the active shape model, and liver segmentation is further achieved through a 3D graph cut.…”

Section: Introductionmentioning

confidence: 99%

Automatic liver segmentation based on appearance and context information

Zheng

et al. 2017

BioMed Eng OnLine

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BackgroundAutomated image segmentation has benefits for reducing clinicians’ workload, quicker diagnosis, and a standardization of the diagnosis.MethodsThis study proposes an automatic liver segmentation approach based on appearance and context information. The relationship between neighboring pixels in blocks is utilized to estimate appearance information, which is used for training the first classifier and obtaining the probability distribution map. The map is used for extracting context information, along with appearance features, to train the next classifier. The prior probability distribution map is achieved after iterations and refined through an improved random walk for liver segmentation without user interaction.ResultsThe proposed approach is evaluated using CT images with eight contemporary approaches, and it achieves the highest VOE, RVD, ASD, RMSD and MSD. It also achieves a high average score of 76 using the MICCAI-2007 Grand Challenge scoring system.ConclusionsExperimental results show that the proposed method is superior to eight other state of the art methods.

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3D-SIFT-Flow for atlas-based CT liver image segmentation

Abstract: Experimental results show that 3D-SIFT-Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy.

Cited by 21 publications

References 49 publications

Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

Performance evaluation of MIND demons deformable registration of MR and CT images in spinal interventions

Automatic liver segmentation based on appearance and context information

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