Interactive skeletonization of intensity volumes

Abeysinghe, Sasakthi S.; Ju, Tao

doi:10.1007/s00371-009-0325-5

Cited by 27 publications

(19 citation statements)

References 31 publications

(35 reference statements)

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“…17 In cases where manual operators reached a consensus on the bridging strand, the automated algorithm agreed 71.1 AE 1.7% of the time [ Fig. 4(a)].…”

Section: Discussionmentioning

confidence: 99%

“…Such errors are manifested as singly connected branches that are not consistent with the contiguous vascular graph. In prior work, such branches were resolved with the aid of manual input based on operator judgment in the form of interactive pointselection and curve-drawing, 17 or labeling of training data sets to aid supervised machine-learning methods. 18 In this work, errors in the vascular graph topology were resolved using an automated algorithm based on shortest-path computation.…”

Section: Methodsmentioning

confidence: 99%

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Imaging and graphing of cortical vasculature using dynamically focused optical coherence microscopy angiography

et al. 2016

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Abstract. Recently, optical coherence tomography (OCT) angiography has enabled label-free imaging of vasculature based on dynamic scattering in vessels. However, quantitative volumetric analysis of the vascular networks depicted in OCT angiography data has remained challenging. Multiple-scattering tails (artifacts specific to the imaging geometry) make automated assessment of vascular morphology problematic. We demonstrate that dynamically focused optical coherence microscopy (OCM) angiography with a high numerical aperture, chosen so the scattering length greatly exceeds the depth-of-field, significantly reduces the deleterious effect of multiplescattering tails in synthesized angiograms. Capitalizing on the improved vascular image quality, we devised and tailored a self-correcting automated graphing approach that achieves a reconstruction of cortical microvasculature from OCM angiography data sets with accuracy approaching that attained by trained operators. The automated techniques described here will facilitate more widespread study of vascular network topology in health and disease.

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“…17 In cases where manual operators reached a consensus on the bridging strand, the automated algorithm agreed 71.1 AE 1.7% of the time [ Fig. 4(a)].…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Imaging and graphing of cortical vasculature using dynamically focused optical coherence microscopy angiography

et al. 2016

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“…1C) is represented by its central axial line, and the backbone of the helix needs to be built. In addition to α-helices and β-sheets that can be detected from the density map, skeleton can also be derived from the density map [33][34][35][36]. Skeleton (red wire in Figure 1) represents possible connection patterns among helices and β-strands (yellow and green in Figure 1 C and D).…”

Section: Introductionmentioning

confidence: 98%

Deriving Protein Backbone Using Traces Extracted from Density Maps at Medium Resolutions

Nasr

2015

Bioinformatics Research and Applications

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Abstract. Electron cryomicroscopy is an experimental technique that is capable to produce three dimensional gray-scale images for protein molecules, called density maps. At medium resolution, the atomic details of the molecule cannot be visualized from density maps. However, some features of the molecule can be seen such as the locations of major secondary structures and the skeleton of the molecule. In addition, the order and direction of the detected secondary structure traces can be inferred. We introduce a method to construct the entire model of a protein directly for traces extracted from the density map. The initial results show that this method has good potential. A single model was built for each of the 12 proteins used in the test. The RMSD 100 of the models is slightly improved from our previous method. Keywords:Cryo-EM · Volume image · Skeletonization · Protein modeling · Loop modeling IntroductionElectron cryomicroscopy (cryo-EM) is an emerging technique that produces threedimensional (3D) electron density maps at a wide-range of resolutions [1][2][3][4]. When the resolution of density maps is higher than 4Å, the atomic structure can often be derived [5][6][7][8][9]. At the medium resolutions, such as 5-10Å, the backbone and the characteristic features of amino acids are not resolved. It is still challenging to derive the atomic structure from such a density map. When a component of the protein has atomic structure available, fitting can be performed to derive the atomic structure [10][11][12]. When a homologous model is available, rigid or flexible fitting can be used to derive the atomic structure [13][14][15][16][17][18]. However, it is still challenging to find a suitable template for many proteins. De novo modeling is an alternative method to derive atomic structures without relying on template structures [19][20][21][22][23][24]. It relies on the detection of secondary structure positions and the connection patterns encoded in the skeleton of the density map. A number of computational methods have been developed to detect α-helices from the density maps [25][26][27][28][29][30][31]. Most helices longer than two turns can be detected. Most of the major β-sheets can also be detected using various methods such as SheetTracer, SSEhunter,. By analyzing the twist of β-sheet

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“…Here, we briefly review geometric methods related to our technique. Medial axis [Blum 1967] is one of the best-known skeletal representations and is often employed to reconstruct curves [Abeysinghe and Ju 2009;Huang et al 2013] and surfaces [Amenta et al 1998]. To incorporate CT information, we adapt the idea of centeredness [Abeysinghe and Ju 2009] to our shaft and sheet primitive fitting.…”

Section: Introductionmentioning

confidence: 99%

Flower modeling via X-ray computed tomography

et al. 2014

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x a d c x b Figure 1: Eustoma model generated with our technique. From a CT volume (a) of a sample flower (b: photograph), we reconstructed a flower model (c). Pane (x) is a cross section of the volume (a). The reconstructed flower model was rendered with texture (d). AbstractThis paper presents a novel three dimensional (3D) flower modeling technique that utilizes an X-ray computed tomography (CT) system and real-world flowers. Although a CT system provides volume data that captures the internal structures of flowers, it is difficult to accurately segment them into regions of particular organs and model them as smooth surfaces because a flower consists of thin organs that contact one another. We thus introduce a semiautomatic modeling technique that is based on a new active contour model with energy functionals designed for flower CT. Our key idea is to approximate flower components by two important primitives, a shaft and a sheet. Based on our active contour model, we also provide novel user interfaces and a numerical scheme to fit these primitives so as to reconstruct realistic thin flower organs efficiently. To demonstrate the feasibility of our technique, we provide various flower models reconstructed from CT volumes.

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Interactive skeletonization of intensity volumes

Cited by 27 publications

References 31 publications

Imaging and graphing of cortical vasculature using dynamically focused optical coherence microscopy angiography

Imaging and graphing of cortical vasculature using dynamically focused optical coherence microscopy angiography

Deriving Protein Backbone Using Traces Extracted from Density Maps at Medium Resolutions

Flower modeling via X-ray computed tomography

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