scholarly journals Reproducible Naevus Counts Using 3D Total Body Photography and Convolutional Neural Networks

Dermatology ◽  
2021 ◽  
pp. 1-8
Author(s):  
Brigid Betz-Stablein ◽  
Brian D’Alessandro ◽  
Uyen Koh ◽  
Elsemieke Plasmeijer ◽  
Monika Janda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gold Standard ◽  
Three Dimensional ◽  
Body Images ◽  
Cohen’S Kappa ◽  
Cohen's Kappa ◽  
Lesion Level ◽  
Total Body ◽  
Sensitivity Specificity

<b><i>Background:</i></b> The number of naevi on a person is the strongest risk factor for melanoma; however, naevus counting is highly variable due to lack of consistent methodology and lack of inter-rater agreement. Machine learning has been shown to be a valuable tool for image classification in dermatology. <b><i>Objectives:</i></b> To test whether automated, reproducible naevus counts are possible through the combination of convolutional neural networks (CNN) and three-dimensional (3D) total body imaging. <b><i>Methods:</i></b> Total body images from a study of naevi in the general population were used for the training (82 subjects, 57,742 lesions) and testing (10 subjects; 4,868 lesions) datasets for the development of a CNN. Lesions were labelled as naevi, or not (“non-naevi”), by a senior dermatologist as the gold standard. Performance of the CNN was assessed using sensitivity, specificity, and Cohen’s kappa, and evaluated at the lesion level and person level. <b><i>Results:</i></b> Lesion-level analysis comparing the automated counts to the gold standard showed a sensitivity and specificity of 79% (76–83%) and 91% (90–92%), respectively, for lesions ≥2 mm, and 84% (75–91%) and 91% (88–94%) for lesions ≥5 mm. Cohen’s kappa was 0.56 (0.53–0.59) indicating moderate agreement for naevi ≥2 mm, and substantial agreement (0.72, 0.63–0.80) for naevi ≥5 mm. For the 10 individuals in the test set, person-level agreement was assessed as categories with 70% agreement between the automated and gold standard counts. Agreement was lower in subjects with numerous seborrhoeic keratoses. <b><i>Conclusion:</i></b> Automated naevus counts with reasonable agreement to those of an expert clinician are possible through the combination of 3D total body photography and CNNs. Such an algorithm may provide a faster, reproducible method over the traditional in person total body naevus counts.

scholarly journals Diagnostic performance of 3D-multi-Echo-data-image-combination (MEDIC) for evaluating SLAP lesions of the shoulder

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Felix Wuennemann ◽  
Laurent Kintzelé ◽  
Felix Zeifang ◽  
Michael W. Maier ◽  
Iris Burkholder ◽  
...  
Keyword(s):  
Shoulder Pain ◽  
Predictive Value ◽  
Gold Standard ◽  
Proton Density ◽  
Diagnostic Challenge ◽  
Cohen’S Kappa ◽  
Slap Lesions ◽  
Cohen's Kappa ◽  
Sensitivity Specificity ◽  
Interreader Agreement

Abstract Background Superior labral anterior to posterior (SLAP) lesions remain a clinical and diagnostic challenge in routine (non-arthrographic) MR examinations of the shoulder. This study prospectively evaluated the ability of 3D-Multi-Echo-Data-Image-Combination (MEDIC) compared to that of routine high resolution 2D-proton-density weighted fat-saturated (PD fs) sequence using 3 T-MRI to detect SLAP lesions using arthroscopy as gold standard. Methods Seventeen consecutive patients (mean age, 51.6 ± 14.8 years, 11 males) with shoulder pain underwent 3 T MRI including 3D-MEDIC and 2D-PD fs followed by arthroscopy. The presence or absence of SLAP lesions was evaluated using both sequences by two independent raters with 4 and 14 years of experience in musculoskeletal MRI, respectively. During arthroscopy, SLAP lesions were classified according to Snyder’s criteria by two certified orthopedic shoulder surgeons. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 3D-MEDIC and 2D-PD fs for detection of SLAP lesions were calculated with reference to arthroscopy as a gold standard. Interreader agreement and sequence correlation were analyzed using Cohen’s kappa coefficient. Figure 1 demonstrates the excellent visibility of a proven SLAP lesion using the 3D-MEDIC and Fig. 2 demonstrates a false-positive case. Results Arthroscopy revealed SLAP lesions in 11/17 patients. Using 3D-MEDIC, SLAP lesions were diagnosed in 14/17 patients by reader 1 and in 13/17 patients by reader 2. Using 2D-PD fs, SLAP lesions were diagnosed in 11/17 patients by reader 1 and 12/17 patients for reader 2. Sensitivity, specificity, PPV, and NPV of 3D-MEDIC were 100.0, 50.0, 78.6, and 100.0% for reader 1; and 100.0, 66.7, 84.6, and 100% for reader 2, respectively. Sensitivity, specificity, PPV, and NPV of 2D-PD fs were 90.9, 83.3, 90.9, and 83.3% for reader 1 and 100.0, 83.3, 91.7, and 100.0% for reader 2. The combination of 2D-PD fs and 3D-MEDIC increased specificity from 50.0 to 83.3% for reader 1 and from 66.7 to 100.0% for reader 2. Interreader agreement was almost perfect with a Cohen’s kappa of 0.82 for 3D-MEDIC and 0.87 for PD fs. Conclusions With its high sensitivity and NPV, 3D-MEDIC is a valuable tool for the evaluation of SLAP lesions. As the combination with routine 2D-PD fs further increases specificity, we recommend incorporation of 3D-MEDIC as an additional sequence in conventional shoulder protocols in patients with non-specific shoulder pain.

scholarly journals Generation and Annotation of Simulation-Real Ship Images for Convolutional Neural Networks Training and Testing

2021 ◽  
Vol 11 (13) ◽  
pp. 5931
Author(s):  
Ji’an You ◽  
Zhaozheng Hu ◽  
Chao Peng ◽  
Zhiqiang Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Annotation ◽  
Three Dimensional ◽  
Image Data ◽  
Detection Algorithm ◽  
Simulation Software ◽  
High Quality ◽  
Annotation Method ◽  
Detection Of Objects

Large amounts of high-quality image data are the basis and premise of the high accuracy detection of objects in the field of convolutional neural networks (CNN). It is challenging to collect various high-quality ship image data based on the marine environment. A novel method based on CNN is proposed to generate a large number of high-quality ship images to address this. We obtained ship images with different perspectives and different sizes by adjusting the ships’ postures and sizes in three-dimensional (3D) simulation software, then 3D ship data were transformed into 2D ship image according to the principle of pinhole imaging. We selected specific experimental scenes as background images, and the target ships of the 2D ship images were superimposed onto the background images to generate “Simulation–Real” ship images (named SRS images hereafter). Additionally, an image annotation method based on SRS images was designed. Finally, the target detection algorithm based on CNN was used to train and test the generated SRS images. The proposed method is suitable for generating a large number of high-quality ship image samples and annotation data of corresponding ship images quickly to significantly improve the accuracy of ship detection. The annotation method proposed is superior to the annotation methods that label images with the image annotation software of Label-me and Label-img in terms of labeling the SRS images.

scholarly journals Identification of Mode Shapes of a Composite Cylinder Using Convolutional Neural Networks

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2801
Author(s):  
Bartosz Miller ◽  
Leonard Ziemiański
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Composite Structures ◽  
Three Dimensional ◽  
Mode Shapes ◽  
Identification Algorithm ◽  
Material Degradation ◽  
Local Material ◽  
Noisy Input ◽  
Shape Vector

The aim of the following paper is to discuss a newly developed approach for the identification of vibration mode shapes of multilayer composite structures. To overcome the limitations of the approaches based on image analysis (two-dimensional structures, high spatial resolution of mode shapes description), convolutional neural networks (CNNs) are applied to create a three-dimensional mode shapes identification algorithm with a significantly reduced number of mode shape vector coordinates. The CNN-based procedure is accurate, effective, and robust to noisy input data. The appearance of local damage is not an obstacle. The change of the material and the occurrence of local material degradation do not affect the accuracy of the method. Moreover, the application of the proposed identification method allows identifying the material degradation occurrence.

scholarly journals Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application

PLoS ONE ◽  
2020 ◽  
Vol 15 (8) ◽  
pp. e0237213 ◽  
Author(s):  
Nikolaos Papandrianos ◽  
Elpiniki Papageorgiou ◽  
Athanasios Anagnostis ◽  
Konstantinos Papageorgiou
Keyword(s):  
Prostate Cancer ◽  
Neural Networks ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Convolutional Neural Networks ◽  
Whole Body ◽  
Body Images

scholarly journals Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data

2019 ◽  
Vol 12 (1) ◽  
pp. 108 ◽  
Author(s):  
Juhyun Lee ◽  
Jungho Im ◽  
Dong-Hyun Cha ◽  
Haemi Park ◽  
Seongmun Sim
Keyword(s):  
Neural Networks ◽  
Tropical Cyclone ◽  
Convolutional Neural Networks ◽  
Mean Squared Error ◽  
Single Channel ◽  
Three Dimensional ◽  
Geostationary Satellite ◽  
Lower Atmosphere ◽  
Long Time ◽  
3D Cnn

For a long time, researchers have tried to find a way to analyze tropical cyclone (TC) intensity in real-time. Since there is no standardized method for estimating TC intensity and the most widely used method is a manual algorithm using satellite-based cloud images, there is a bias that varies depending on the TC center and shape. In this study, we adopted convolutional neural networks (CNNs) which are part of a state-of-art approach that analyzes image patterns to estimate TC intensity by mimicking human cloud pattern recognition. Both two dimensional-CNN (2D-CNN) and three-dimensional-CNN (3D-CNN) were used to analyze the relationship between multi-spectral geostationary satellite images and TC intensity. Our best-optimized model produced a root mean squared error (RMSE) of 8.32 kts, resulting in better performance (~35%) than the existing model using the CNN-based approach with a single channel image. Moreover, we analyzed the characteristics of multi-spectral satellite-based TC images according to intensity using a heat map, which is one of the visualization means of CNNs. It shows that the stronger the intensity of the TC, the greater the influence of the TC center in the lower atmosphere. This is consistent with the results from the existing TC initialization method with numerical simulations based on dynamical TC models. Our study suggests the possibility that a deep learning approach can be used to interpret the behavior characteristics of TCs.

Sign in / Sign up

Export Citation Format

Share Document