An analytical method to create patient-specific deformed bone models using X-ray images and a healthy bone model

Ün, M. Kerem; Avşar, Ercan; Akcali, Ibrahim Deniz

doi:10.1016/j.compbiomed.2018.11.003

Cited by 9 publications

(6 citation statements)

References 29 publications

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“…The field of application of digital bone models is broad. Three-dimensional (3D) reconstructions of bones are used in biomechanics, biomedical engineering and medical image processing for musculoskeletal modelling 1 , 2 , finite element analyses 3 , statistical shape modelling 4 – 6 or 3D reconstruction from sparse imaging data, such as radiographs 7 , 8 or EOS images 9 . 3D reconstructions of the bones are used in orthopedics, traumatology or radiology for the development of implants 10 – 14 , surgical instruments 15 , 16 or procedures, for diagnosis and decision-making 17 , 18 , preoperative planning 19 , 20 and navigational guidance during computer assisted surgery 8 , 21 , the evaluation of outcome 22 , surgery simulation 23 , surgical education and training 24 , especially in the context of personalized, patient-specific, customized or individualized medicine.…”

Section: Background and Summarymentioning

confidence: 99%

Database of segmentations and surface models of bones of the entire lower body created from cadaver CT scans

Fischer

2023

Sci Data

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The range of applications of digital surface models of the bones in science and industry is wide. Three-dimensional reconstructions of bones are used in biomechanics, biomedical engineering, medical image processing, orthopedics, traumatology, radiology, patient education, anatomy, anthropometry, forensic anthropology, ergonomics, usability and human factors engineering, or accident and injury analysis and prevention. No open access database or repository of skeletal surface models of the full lower extremities exists. Therefore, the objective of this publication was to provide access to consistent complete bone models of the pelvis and lower limbs of multiple subjects, including biometric data. Segmentations and surface models of the bones of the lower extremities of more than twenty subjects were created from open access postmortem whole-body computed tomography scans. The database provides a broad range of applications by giving access to the data of the complete process chain, from the raw medical imaging data through the segmentations to the surface models.

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Section: Background and Summarymentioning

confidence: 99%

Database of segmentations and surface models of bones of the entire lower body created from cadaver CT scans

Fischer

2023

Sci Data

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“…Though vaccinations are stepping in to the society, it is not readily available for all the citizens. In order to keep the virus away from the human life, certain solutions can be adopted in terms of making the vaccine universally available, early prediction and prevention [2]. At present condition, the covid is detected through a primary technique called Polymerase Chain Reaction (PCR) test.…”

Section: Introductionmentioning

confidence: 99%

Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

Gopi¹,

Muthusamy²,

Suresh³

et al. 2022

Computers, Materials &Amp; Continua

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This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed in such a way so that, it automatically detects the virus present in lungs using chest image. This approach uses an image texture analysis technique called granulometric mathematical model. Selected features are heuristically processed for optimization using novel multi scaling deep learning called light weight residual-atrous spatial pyramid pooling (LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPP-Unet technique has a higher level of contracting solution by extracting major level of image features. Moreover, the corona virus has been detected using high resolution output. In the framework, atrous spatial pyramid pooling (ASPP) method is employed at its bottom level for incorporating the deep multi scale features in to the discriminative mode. The architectural working starts from the selecting the features from the image using granulometric mathematical model and the selected features are optimized using LightRES-ASPP-Unet. ASPP in the analysis of images has performed better than the existing Unet model. The proposed algorithm has achieved 99.6% of accuracy in detecting the virus at its early stage.

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“…Due to the delays with the production and implementation of the vaccination campaign, prevention plays a major role and a controlled prognosis is only possible if the protocol therapy [2] is applied since the very early stages. The Polymerase Chain Reaction (PCR) test is the primary method for detecting COVID-19 as it is more reliable than any other option available to practitioners in the health & care system [3][4][5][6]. However, PCR is laborious, very time-consuming, and sometimes difficult to process due to shortages of kits and delays in their delivery in many countries worldwide.…”

Section: Introductionmentioning

confidence: 99%

Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images

2021

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This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimising a basic convolutional neural network which represents the starting point for the evolution process. Subsequently, at most two additional convolutional layers are added, at a time, to the previous convolutional structure as a result of a further optimisation phase. Each performed phase maximises the the accuracy of the system, thus requiring training and assessment of the new model, which gets gradually deeper, with relevant COVID-19 chest X-ray images. This iterative process ends when no improvement, in terms of accuracy, is recorded. Hence, the proposed method evolves the most performing network with the minimum number of convolutional layers. In this light, we simultaneously achieve high accuracy while minimising the presence of redundant layers to guarantee a fast but reliable model. Our results show that the proposed implementation of such a framework achieves accuracy up to 99.11%, thus being particularly suitable for the early detection of COVID-19.

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An analytical method to create patient-specific deformed bone models using X-ray images and a healthy bone model

Cited by 9 publications

References 29 publications

Database of segmentations and surface models of bones of the entire lower body created from cadaver CT scans

Database of segmentations and surface models of bones of the entire lower body created from cadaver CT scans

Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images

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