Motion grading of high-resolution quantitative computed tomography supported by deep convolutional neural networks

Walle, Matthias; Eggemann, Dominic; Atkins, Penny R.; Kendall, Jack J.; Stock, Kerstin; Müller, Ralph; Collins, Caitlyn J.

doi:10.1016/j.bone.2022.116607

Cited by 6 publications

(10 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy of the 3D-CNN to predict the image quality rating was 92% to 95% with an assessment time of only 2.5 s per scaphoid. The importance of such an automated rating system becomes apparent when considering the variability and bias of operator-based motion scoring systems [ 17 , 18 , 20 , 21 ]. As the abilities of a neural network strongly depend on the quantity of the training data and the quality of the training labels, we collected a large set of training datasets (1451 stacks), all graded by three experienced and independent professionals.…”

Section: Discussionmentioning

confidence: 99%

“…Imaging artifacts are major limitations of HR-pQCT measurements that can hamper the precision and reproducibility of HR-pQCT measurements by patient movements [ 13 , 14 , 15 , 16 , 17 , 18 ], particularly at the scaphoid bone. This was already confirmed in an earlier study revealing a considerable influence of motion on bone morphometry parameters of the scaphoid [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, this non-standardized and subjective approach leads to distinctive operator disagreement [ 17 , 18 , 20 , 21 ]. As a result, low-quality HR-pQCT scans might be considered of sufficient quality and vice versa; good-quality scans might be regarded as insufficient, providing inaccurate and incomplete imaging data sets.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, neural networks, especially convolutional neural networks (CNNs), are helpful tools for rating and analyzing CT data [ 25 ]. Walle et al [ 17 ] already used a CNN to grade HR-pQCT scans, but this approach analyzed the structure of single slices to grade the image quality. This may weaken the ability to detect artifacts in the axial scanning direction.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep Convolutional Neural Networks Provide Motion Grading for High-Resolution Peripheral Quantitative Computed Tomography of the Scaphoid

Benedikt,

Zelger,

Horling

et al. 2024

Diagnostics

Self Cite

View full text Add to dashboard Cite

In vivo high-resolution peripheral quantitative computed tomography (HR-pQCT) studies on bone characteristics are limited, partly due to the lack of standardized and objective techniques to describe motion artifacts responsible for lower-quality images. This study investigates the ability of such deep-learning techniques to assess image quality in HR-pQCT datasets of human scaphoids. In total, 1451 stacks of 482 scaphoid images from 53 patients, each with up to six follow-ups within one year, and each with one non-displaced fractured and one contralateral intact scaphoid, were independently graded by three observers using a visual grading scale for motion artifacts. A 3D-CNN was used to assess image quality. The accuracy of the 3D-CNN to assess the image quality compared to the mean results of three skilled operators was between 92% and 96%. The 3D-CNN classifier reached an ROC-AUC score of 0.94. The average assessment time for one scaphoid was 2.5 s. This study demonstrates that a deep-learning approach for rating radiological image quality provides objective assessments of motion grading for the scaphoid with a high accuracy and a short assessment time. In the future, such a 3D-CNN approach can be used as a resource-saving and cost-effective tool to classify the image quality of HR-pQCT datasets in a reliable, reproducible and objective way.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Convolutional Neural Networks Provide Motion Grading for High-Resolution Peripheral Quantitative Computed Tomography of the Scaphoid

Benedikt,

Zelger,

Horling

et al. 2024

Diagnostics

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, color becomes very important as a problem of image or video-based learning and recognition. Color problems can be solved with the Convolutional Neural Network algorithm (Coulibaly et al, 2022), (Chen & Ge, 2022), (Walle et al, 2023).…”

Section: Methodsmentioning

confidence: 99%

Comparison of Convolutional Neural Network and Artificial Neural Network for Rice Detection

Suherman

Hindarto

Makmur

et al. 2023

SinkrOn

View full text Add to dashboard Cite

Rice is a staple food for people in tropical countries. Indonesia is a country that needs a lot of rice for its people in providing food. This country has implemented various ways to plant rice properly. Many agricultural fields have implemented harvests up to three times a year, due to the role of technology which has helped a lot in agriculture. Planting to harvest already uses advanced technology and tools. A good rice harvest can improve the welfare of the surrounding community. Meanwhile with lots of rice products because many rice plants produce with lots of rice. The type of rice from different regions of origin, the yield of rice is also different from other regions of origin. But with advances in technology, it is possible to plant rice whose types of plants come from other regions. The rice sold to the public varies, so that people who are unfamiliar with the types of rice find it difficult to detect the types of rice. Machine learning is present in detecting various kinds of rice. Machine learning, especially deep learning can make better detection, because one of the deep learning methods works similar to the human brain. In the human brain there are millions or even billions of neurons. This research uses neural networks in experiments using public datasets. Experiments using Artificial Neural Networks achieve an training accuracy of 98.2%, loss: 0.2351. It takes about 10 minutes of training. Testing accuracy reaches accuracy: 96%, loss: 0.6641. By conducting experiments using the Convolution Neural Network, it achieves an accuracy of 99.3% and the training time requires around 18 hours. The purpose of this research is to classify the rice image dataset and detect the rice image.

show abstract

Precision of bone mechanoregulation assessment in humans using longitudinal high-resolution peripheral quantitative computed tomography in vivo

Walle¹,

Whittier²,

Schenk³

et al. 2023

Bone

View full text Add to dashboard Cite

Motion grading of high-resolution quantitative computed tomography supported by deep convolutional neural networks

Cited by 6 publications

References 20 publications

Deep Convolutional Neural Networks Provide Motion Grading for High-Resolution Peripheral Quantitative Computed Tomography of the Scaphoid

Deep Convolutional Neural Networks Provide Motion Grading for High-Resolution Peripheral Quantitative Computed Tomography of the Scaphoid

Comparison of Convolutional Neural Network and Artificial Neural Network for Rice Detection

Precision of bone mechanoregulation assessment in humans using longitudinal high-resolution peripheral quantitative computed tomography in vivo

Contact Info

Product

Resources

About