3D Reconstruction of Face from 2D CT Scan Images

Kumar, T. Senthil; Vijai, Anupa

doi:10.1016/j.proeng.2012.01.952

Cited by 20 publications

(10 citation statements)

References 5 publications

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“…In some cases, particularly head scans, the pixel data themselves may be inherently identifiable [ 30–32 ]. This requires special-purpose software, however, because the pixel data are never exported beyond the Safe Haven environment and the use or installation of such software would not be permitted, and any research project would be denied access to the data for this purpose.…”

Section: Discussionmentioning

confidence: 99%

An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population

et al. 2020

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Aim To enable a world-leading research dataset of routinely collected clinical images linked to other routinely collected data from the whole Scottish national population. This includes more than 30 million different radiological examinations from a population of 5.4 million and >2 PB of data collected since 2010. Methods Scotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of those data, link it to other clinical or social datasets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment. Results An extensive software platform has been developed to host, extract, and link data from cohorts to answer research questions. The platform has been tested on 5 different test cases and is currently being further enhanced to support 3 exemplar research projects. Conclusions The data available are from a range of radiological modalities and scanner types and were collected under different environmental conditions. These real-world, heterogenous data are valuable for training algorithms to support clinical decision making, especially for deep learning where large data volumes are required. The resource is now available for international research access. The platform and data can support new health research using artificial intelligence and machine learning technologies, as well as enabling discovery science.

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Section: Discussionmentioning

confidence: 99%

An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population

et al. 2020

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“…These tools have been used for different goals in the field of reconstructive surgery, including assessment of the microvascular anatomy for microsurgical reconstructions, diagnosis, preoperative evaluation, virtual planning, modeling of body parts, 3D printing, intraoperative evaluation, and postoperative follow-up [16][17][18][19][20][21][22].…”

Section: Modern Imaging Techniques In Plastic Surgerymentioning

confidence: 99%

Using 3D Imaging to Preoperatively Plan Facelift Procedures for the Lower Third of the Face and Neck

Borisenko,

Sharobaro,

Avdeev

et al. 2024

Cosmetics

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The pursuit of youth and attractiveness is a fundamental desire in contemporary society. For many individuals, the appearance of visible signs of aging, such as skin laxity, gravitational changes in facial and neck soft tissues, and loss of harmonious proportions, can be psychologically distressing. The choice of effective methods for addressing these changes depends on their specific characteristics, the patient’s anatomical features, and the type of facial aging. The introduction of 3D imaging to determine the hyoid bone level, the volume of soft tissue structures in the cervico-mental area, ptosis of subplatysmal structures, and the volume of submandibular glands, in addition to ultrasound study of the submental projection at the preoperative stage, are key for making the best decisions regarding preoperative planning. This case report aims to illustrate how modern imaging techniques are a fundamental part of the preoperative assessment of the mid and lower third of the face to ensure the most favorable aesthetic outcomes for each patient.

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“…Medical data visualization technologies can be divided into multiplanar rendering, surface rendering, and volume rendering . Multiplanar rendering visualizes gray values of an arbitrary cross section of volumetric data.…”

Section: Introductionmentioning

confidence: 99%

“…Surface reconstruction algorithms can be classified into contour‐based surface reconstruction and iso‐surface extraction . Contour‐based surface reconstruction methods need to extract the cross‐sectional contour of each axial slice and then reconstruct the 3D surface through triangulation between the consecutive contours.…”

Section: Introductionmentioning

confidence: 99%

A PDE‐based head visualization method with CT data

Chen

et al. 2015

Computer Animation & Virtual

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In this paper, we extend theuse of the partial differential equation (PDE) method to head visualization with computed tomography (CT) data and show how the two primary medical visualization means, surface reconstruction, and volume rendering can be integrated into one single framework through PDEs. Our scheme first performs head segmentation from CT slices using a variational approach, the output of which can be readily used for extraction of a small set of PDE boundary conditions. With the extracted boundary conditions, head surface reconstruction is then executed. Because only a few slices are used, our method can perform head surface reconstruction more efficiently in both computational time and storage cost than the widely used marching cubes algorithm. By elaborately introducing a third parameter w to the PDE method, a solid head can be created, based on which the head volume is subsequently rendered with 3D texture mapping. Instead of designing a transfer function, we associate the alpha value of texels of the 3D texture with the PDE parameter w through a linear transform. This association enables the production of a visually translucent head volume. The experimental results demonstrate the feasibility of the developed head visualization method.

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3D Reconstruction of Face from 2D CT Scan Images

Cited by 20 publications

References 5 publications

An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population

An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population

Using 3D Imaging to Preoperatively Plan Facelift Procedures for the Lower Third of the Face and Neck

A PDE‐based head visualization method with CT data

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