W-DRAG: A joint framework of WGAN with data random augmentation optimized for generative networks for bone marrow edema detection in dual energy CT.

Dual-energy computed tomography (CT) is an excellent substitute for identifying bone marrow edema in magnetic resonance imaging. However, it is rarely used in practice owing to its low contrast. To overcome this problem, we constructed a framework based on deep learning techniques to screen for diseases using axial bone images and to identify the local positions of bone lesions. To address the limited availability of labeled samples, we developed a new generative adversarial network (GAN) that extends expressions beyond conventional augmentation (CA) methods based on geometric transformations. We theoretically and experimentally determined that combining the concepts of data augmentation optimized for GAN training (DAG) and Wasserstein GAN yields a considerably stable generation of synthetic images and effectively aligns their distribution with that of real images, thereby achieving a high degree of similarity. The classification model was trained using real and synthetic samples. Consequently, the GAN technique used in the diagnostic test had an improved F1 score of approximately 7.8% compared with CA. The final F1 score was 80.24%, and the recall and precision were 84.3% and 88.7%, respectively. The results obtained using the augmented samples outperformed those obtained using pure real samples without augmentation. In addition, we adopted explainable AI techniques that leverage a class activation map (CAM) and principal component analysis to facilitate visual analysis of the network's results. The framework was designed to suggest an attention map and scattering plot to visually explain the disease predictions of the network.

Diagnostic performance for detecting bone marrow edema of the hip on dual-energy CT: Deep learning model vs. musculoskeletal physicians and radiologists.

PURPOSE: To compare the diagnostic performance of a deep learning (DL) model with that of musculoskeletal physicians and radiologists for detecting bone marrow edema on dual-energy CT (DECT). METHOD: This retrospective study included adult patients underwent hip DECT and MRI within 1 month between April 2018 and December 2020. A total of 8709 DECT images were divided into training/validation (85%, 7412 augmented images) and test (15%, 1297 images) sets. The images were labeled as present/absent bone marrow edema, with MRI as reference standard. We developed and trained a DL model to detect bone marrow edema from DECT images. Thereafter, DL model, two orthopedic surgeons, and three radiologists evaluated the presence of bone marrow edema on every test image. The diagnostic performance of the DL model and readers was compared. Inter-reader agreement was calculated using Fleiss-kappa statistics. RESULTS: A total of 73 patients (mean age, 59 ± 12 years; 38 female) were included. The DL model had a significantly higher area under the curve (AUC, 0.84 vs. 0.61-0.70, p < 0.001) and sensitivity (79% vs. 29-66%) without loss of specificity (90% vs. 74-93%) than the non- or less-experienced readers and similar to the trained reader (AUC, 0.83, p = 0.402; sensitivity, 71%; specificity, 94%). Additionally, AUCs were strongly dependent on the reader's DECT experience. Inter-reader agreement was fair (κ = 0.303). CONCLUSION: The DL model showed better diagnostic performance than less-experienced physicians in detecting bone marrow edema on DECT and comparable performance to a trained radiologist.

Prediction of bone mineral density in CT using deep learning with explainability.

Bone mineral density (BMD) is a key feature in diagnosing bone diseases. Although computational tomography (CT) is a common imaging modality, it seldom provides bone mineral density information in a clinic owing to technical difficulties. Thus, a dual-energy X-ray absorptiometry (DXA) is required to measure bone mineral density at the expense of additional radiation exposure. In this study, a deep learning framework was developed to estimate the bone mineral density from an axial cut of the L1 bone on computational tomography. As a result, the correlation coefficient between bone mineral density estimates and dual-energy X-ray absorptiometry bone mineral density was .90. When the samples were categorized into abnormal and normal groups using a standard (T-score = - 1.0 ), the maximum F1 score in the diagnostic test was .875. In addition, it was identified using explainable artificial intelligence techniques that the network intensively sees a local area spanning tissues around the vertebral foramen. This method is well suited as an auxiliary tool in clinical practice and as an automatic screener for identifying latent patients in computational tomography databases.

Twin SVM-Based Classification of Alzheimer's Disease Using Complex Dual-Tree Wavelet Principal Coefficients and LDA.

Alzheimer's disease (AD) is a leading cause of dementia, which causes serious health and socioeconomic problems. A progressive neurodegenerative disorder, Alzheimer's causes the structural change in the brain, thereby affecting behavior, cognition, emotions, and memory. Numerous multivariate analysis algorithms have been used for classifying AD, distinguishing it from healthy controls (HC). Efficient early classification of AD and mild cognitive impairment (MCI) from HC is imperative as early preventive care could help to mitigate risk factors. Magnetic resonance imaging (MRI), a noninvasive biomarker, displays morphometric differences and cerebral structural changes. A novel approach for distinguishing AD from HC using dual-tree complex wavelet transforms (DTCWT), principal coefficients from the transaxial slices of MRI images, linear discriminant analysis, and twin support vector machine is proposed here. The prediction accuracy of the proposed method yielded up to 92.65 ± 1.18 over the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, with a specificity of 92.19 ± 1.56 and sensitivity of 93.11 ± 1.29, and 96.68 ± 1.44 over the Open Access Series of Imaging Studies (OASIS) dataset, with a sensitivity of 97.72 ± 2.34 and specificity of 95.61 ± 1.67. The accuracy, sensitivity, and specificity achieved using the proposed method are comparable or superior to those obtained by various conventional AD prediction methods.

2D discrete Fourier transform on sliding windows.

Discrete Fourier transform (DFT) is the most widely used method for determining the frequency spectra of digital signals. In this paper, a 2D sliding DFT (2D SDFT) algorithm is proposed for fast implementation of the DFT on 2D sliding windows. The proposed 2D SDFT algorithm directly computes the DFT bins of the current window using the precalculated bins of the previous window. Since the proposed algorithm is designed to accelerate the sliding transform process of a 2D input signal, it can be directly applied to computer vision and image processing applications. The theoretical analysis shows that the computational requirement of the proposed 2D SDFT algorithm is the lowest among existing 2D DFT algorithms. Moreover, the output of the 2D SDFT is mathematically equivalent to that of the traditional DFT at all pixel positions.

Edge-based intramode selection for depth-map coding in 3D-HEVC.

The 3D video extension of High Efficiency Video Coding (3D-HEVC) is the state-of-the-art video coding standard for the compression of the multiview video plus depth format. In the 3D-HEVC design, new depth-modeling modes (DMMs) are utilized together with the existing intraprediction modes for depth intracoding. The DMMs can provide more accurate prediction signals and thereby achieve better compression efficiency. However, testing the DMMs in the intramode decision process causes a drastic increase in the computational complexity. In this paper, we propose a fast mode decision algorithm for depth intracoding. The proposed algorithm first performs a simple edge classification in the Hadamard transform domain. Then, based on the edge classification results, the proposed algorithm selectively omits unnecessary DMMs in the mode decision process. Experimental results demonstrate that the proposed algorithm speeds up the mode decision process by up to 37.65% with negligible loss of coding efficiency.

Level set method with automatic selective local statistics for brain tumor segmentation in MR images.

The level set approach is a powerful tool for segmenting images. This paper proposes a method for segmenting brain tumor images from MR images. A new signed pressure function (SPF) that can efficiently stop the contours at weak or blurred edges is introduced. The local statistics of the different objects present in the MR images were calculated. Using local statistics, the tumor objects were identified among different objects. In this level set method, the calculation of the parameters is a challenging task. The calculations of different parameters for different types of images were automatic. The basic thresholding value was updated and adjusted automatically for different MR images. This thresholding value was used to calculate the different parameters in the proposed algorithm. The proposed algorithm was tested on the magnetic resonance images of the brain for tumor segmentation and its performance was evaluated visually and quantitatively. Numerical experiments on some brain tumor images highlighted the efficiency and robustness of this method.