GI tract unraveling with curved cross sections

Wang, Ge; McFarland, Gordon B.; Brown, Bruce P.; Vannier, Michael W.

doi:10.1109/42.700745

Cited by 84 publications

(50 citation statements)

References 12 publications

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“…11(a), since TC lines are observed as sequences of relatively flat and narrow ridge-breaks traversing haustral folds, the experimentally obtained TCs can be measured by analyzing overlapping area ratio (OAR) between the manually drawn TCs and the TCs drawn by our FSGP method. Mathematically, the OAR is calculated by: (9) where v i stands for the set of vertex extracted by FSGP (see red lines in Fig. 11(b)), u j stands for the set of vertex chosen manually by experts (see green/black area in Fig.…”

Section: Discussionmentioning

confidence: 99%

A novel colon wall flattening model for computed tomographic colonography: method and validation

Wang

Chen

et al. 2014

Computer Methods in Biomechanics and Biomedical Engineering: Im

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Computed tomographic colonography (CTC) has been developed for screening of colon cancer. Flattening the three-dimensional (3D) colon wall into two-dimensional (2D) image is believed to (1) provide supplementary information to the endoscopic views and further (2) facilitate colon registration, taniae coli (TC) detection, and haustral fold segmentation. Though the previouslyused conformal mapping-based flattening methods can preserve the angular geometry, they have the limitations in providing accurate information of the 3D inner colon wall due to the lack of undulating topography. In this paper, we present a novel colon-wall flattening method using a strategy of 2.5D approach. Coupling with the conformal flattening model, the presented new approach builds an elevation distance map to depict the neighborhood characteristics of the inner colon wall. We validated the new method via two CTC applications: TC detection and haustral fold segmentation. Experimental results demonstrated the effectiveness of our strategy for CTC studies.

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Section: Discussionmentioning

confidence: 99%

A novel colon wall flattening model for computed tomographic colonography: method and validation

Wang

Chen

et al. 2014

Computer Methods in Biomechanics and Biomedical Engineering: Im

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“…Our approach to flattening such a surface is based on a certain mathematical technique from Riemann surface theory, which allows us to map any highly undulating tubular surface without handles or self-intersections onto a planar rectangle in a conformal manner. There is some related work in the interesting paper [15] on the topological flattening of a tube onto the plane and its application to virtual colonoscopy. In [15], an electro-magnetic field is simulated by placing charges along a fly-through path.…”

Section: Introductionmentioning

confidence: 99%

“…There is some related work in the interesting paper [15] on the topological flattening of a tube onto the plane and its application to virtual colonoscopy. In [15], an electro-magnetic field is simulated by placing charges along a fly-through path. The resulting field lines which emanate from a point on the path define a surface whose intersection with the colon surface forms a loop which is flattened into the plane.…”

Section: Introductionmentioning

confidence: 99%

Non-distorting Flattening for Virtual Colonoscopy

Haker

Angenent

Tannenbaum

et al. 2000

Lecture Notes in Computer Science

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Abstract. In this paper, we consider a novel 3D visualization technique based on conformal surface flattening for virtual colonoscopy. Such visualization methods could be important in virtual colonoscopy since they have the potential for non-invasively determining the presence of polyps and other pathologies. Further, we demonstrate a method which presents a surface scan of the entire colon as a cine, and affords a viewer the opportunity to examine each point on the surface without distortion. From a triangulated surface representation of the colon, we indicate how the flattening procedure may be implemented using a finite element technique. We give a simple example of how the flattening map can be composed with other maps to enhance certain mapping properties. Finally, we show how the use of curvature based colorization and shading maps can be used to aid in the inspection process.

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“…al. 18 propose a solution that models gastrointestinal tract cross sections as electrical force lines. This approach is computationally complex, to the extent where the authors suggest means of approximating their full algorithm, at the expense of a guarantee that the cross sections do not intersect.…”

Section: Introductionmentioning

confidence: 99%

3D surface parameterization using manifold learning for medial shape representation

Ward

Hamarneh

2007

SPIE Proceedings

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The choice of 3D shape representation for anatomical structures determines the effectiveness with which segmentation, visualization, deformation, and shape statistics are performed. Medial axis-based shape representations have attracted considerable attention due to their inherent ability to encode information about the natural geometry of parts of the anatomy. In this paper, we propose a novel approach, based on nonlinear manifold learning, to the parameterization of medial sheets and object surfaces based on the results of skeletonization. For each single-sheet figure in an anatomical structure, we skeletonize the figure, and classify its surface points according to whether they lie on the upper or lower surface, based on their relationship to the skeleton points. We then perform nonlinear dimensionality reduction on the skeleton, upper, and lower surface points, to find the intrinsic 2D coordinate system of each. We then center a planar mesh over each of the low-dimensional representations of the points, and map the meshes back to 3D using the mappings obtained by manifold learning. Correspondence between mesh vertices, established in their intrinsic 2D coordinate spaces, is used in order to compute the thickness vectors emanating from the medial sheet. We show results of our algorithm on real brain and musculoskeletal structures extracted from MRI, as well as an artificial multi-sheet example. The main advantages to this method are its relative simplicity and noniterative nature, and its ability to correctly compute nonintersecting thickness vectors for a medial sheet regardless of both the amount of coincident bending and thickness in the object, and of the incidence of local concavities and convexities in the object's surface. INTRODUCTIONAs radiologists continue the transition to examining medical images and anatomical structures in 3D, the choice of 3D shape representation for anatomical shapes is important. A good choice of representation enables effective segmentation, visualization, deformation, and shape statistics in research studies. Medial axis-based representations, inspired by Blum's development of the medial axis transform (MAT), 1 have attracted considerable attention due to their inherent ability to capture object bending, elongation, and thickness in a coordinate system local to the object.2, 3 Furthermore, medial axis-based representations inherently decompose shapes into the intuitive notions, so that each characteristic can be studied separately, either qualitatively or quantitatively. 4Medial axis-based representations such as medial patches 5 and m-reps 3 typically decompose an anatomical structure into one or more single-figural objects. Each single figural object is represented using a connected set of loci (medial nodes in medial patches nomenclature; atoms in m-reps) which lie within the object on or near its medial surface forming a medial sheet, and a set of thickness vectors (medial patches nomenclature; spokes in m-reps) which emanate from the medial nodes and terminate ...

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GI tract unraveling with curved cross sections

Cited by 84 publications

References 12 publications

A novel colon wall flattening model for computed tomographic colonography: method and validation

A novel colon wall flattening model for computed tomographic colonography: method and validation

Non-distorting Flattening for Virtual Colonoscopy

3D surface parameterization using manifold learning for medial shape representation

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