A Novel Colon Wall Flattening Model for Computed Tomographic Colonography: Method and Validation

Abstract

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 previously-used 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.

Men and women have similarly shaped carpometacarpal joint bones

“…The technique implemented to generate the statistical shape model for this paper was based on the work by Zhang et al (2014). In summary, a custom template cubic-Lagrange piece-wise parametric mesh (Nielsen, 1987) (Figure 1 C) was created and fitted to all data clouds in the training set via an iterative fitting process (Figure 1 D).…”

“…Statistical shape modeling is necessary to quantify the variation in shape across a population (Chan et al, 2013; Fitzpatrick et al, 2011; Van Haver et al, 2013; Zhang et al, 2014) and classify morphological variation by decomposing shapes into a set of statistically significant modes (commonly principal components) (Cootes et al, 1992; Dryden and Mardia, 1998). This is effective for understanding morphology (Bischoff et al, 2013; Vos et al, 2004) and for determining relationships between morphology and parameters of interest, such as sex and age (Anderson et al, 2010; Bischoff et al, 2013; Fitzpatrick et al, 2011).…”

Relating speech production to tongue muscle compressions using tagged and high-resolution magnetic resonance imaging

“…In the future, manual generation of fiber directions should be replaced by the result from tongue DTI techniques. Manual segmentation of the two GG and T muscles should be replaces by a complete set of internal tongue muscles obtained from registration of a tongue atlas [13] . Moreover, we are looking to study the behavior of more muscles in more subjects during various speech patterns.…”

Construction of An Unbiased Spatio-Temporal Atlas of the Tongue During Speech

“…Recent advances in tongue imaging methods such as magnetic resonance imaging (MRI) have accelerated new advances in image and motion analysis, including segmentation [1, 2], motion tracking [3], motion clustering [4], and registration [5, 6]. However, despite the popularity of an atlas in other organs (e.g., the brain [7,8] or the heart [9]), research on the tongue or vocal tract atlas is still in its infancy; recently the first vocal tract atlas and statistical model have been published in [10], where structural MRI from normal subjects were used to build the atlas.…”

“…However, despite the popularity of an atlas in other organs (e.g., the brain [7,8] or the heart [9]), research on the tongue or vocal tract atlas is still in its infancy; recently the first vocal tract atlas and statistical model have been published in [10], where structural MRI from normal subjects were used to build the atlas. However, to the best of our knowledge, there has been no spatio-temporal atlas of the tongue during speech or swallowing to date.…”