A novel approach for evaluating bone mineral density of hips based on Sobel gradient-based map of radiographs utilizing convolutional neural network

Nguyen, Thong; Chae, Dong-Sik; Park, Sung-Jun; Yoon, Jonghun

doi:10.1016/j.compbiomed.2021.104298

Cited by 18 publications

(14 citation statements)

References 18 publications

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“…Because none of the previous studies that predicted osteoporosis from chest X-rays were able to predict BMD, this study represents significant progress in this research area [ 22 , 23 ]. In comparison with the results of studies that predicted BMD from radiographs of the hip and lumbar spine using deep learning models [ 18 , 19 ], the results of our study were slightly inferior (previous studies: R = 0.81, 0.89; this study: R = 0.75). This may be due to the following reasons.…”

Section: Discussioncontrasting

confidence: 78%

“…Subsequently, progress in orthopedics research has led to the use of deep learning models for osteoporosis screening [ 17 ]. In previous studies, diagnoses of osteoporosis based on radiographs of the lumbar spine and hip joint have been demonstrated [ 18 , 19 ], whereas the BMDs (g/cm 2 ) of the hip and lumbar spine have been measured from radiographs of these sites [ 20 , 21 ]. Chest X-rays have also been used in two studies to diagnose osteoporosis [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Bone Mineral Density and T-Score Prediction from Chest X-rays: A Multicenter Study

et al. 2022

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Although the number of patients with osteoporosis is increasing worldwide, diagnosis and treatment are presently inadequate. In this study, we developed a deep learning model to predict bone mineral density (BMD) and T-score from chest X-rays, which are one of the most common, easily accessible, and low-cost medical imaging examination methods. The dataset used in this study contained patients who underwent dual-energy X-ray absorptiometry (DXA) and chest radiography at six hospitals between 2010 and 2021. We trained the deep learning model through ensemble learning of chest X-rays, age, and sex to predict BMD using regression and T-score for multiclass classification. We assessed the following two metrics to evaluate the performance of the deep learning model: (1) correlation between the predicted and true BMDs and (2) consistency in the T-score between the predicted class and true class. The correlation coefficients for BMD prediction were hip = 0.75 and lumbar spine = 0.63. The areas under the curves for the T-score predictions of normal, osteopenia, and osteoporosis diagnoses were 0.89, 0.70, and 0.84, respectively. These results suggest that the proposed deep learning model may be suitable for screening patients with osteoporosis by predicting BMD and T-score from chest X-rays.

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Section: Discussioncontrasting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Deep Learning for Bone Mineral Density and T-Score Prediction from Chest X-rays: A Multicenter Study

et al. 2022

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“…Hu et al ( 10 ) used deep learning system to identify lymph node quantification and metastatic cancer. Nguyen et al ( 11 ) used convolutional neural network to evaluate bone mineral density of hips based on Sobel gradient-based map of radiographs. Barbios et al ( 12 ) used decision tree to guide performance of intraoperative liver biopsy during bariatric surgery.…”

Section: Introductionmentioning

confidence: 99%

Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study

Zhang

Zhong

et al. 2022

Front. Public Health

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BackgroundPancreatic cancer (PC) is one of the most common malignant types of cancer, with the lung being the frequent distant metastatic site. Currently, no population-based studies have been done on the risk and prognosis of pancreatic cancer with lung metastases (PCLM). As a result, we intend to create two novel nomograms to predict the risk and prognosis of PCLM.MethodsPC patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database from 2010 to 2016. A multivariable logistic regression analysis was used to identify risk factors for PCLM at the time of diagnosis. The multivariate Cox regression analysis was carried out to assess PCLM patient's prognostic factors for overall survival (OS). Following that, we used area under curve (AUC), time-dependent receiver operating characteristics (ROC) curves, calibration plots, consistency index (C-index), time-dependent C-index, and decision curve analysis (DCA) to evaluate the effectiveness and accuracy of the two nomograms. Finally, we compared differences in survival outcomes using Kaplan-Meier curves.ResultsA total of 803 (4.22%) out of 19,067 pathologically diagnosed PC patients with complete baseline information screened from SEER database had pulmonary metastasis at diagnosis. A multivariable logistic regression analysis revealed that age, histological subtype, primary site, N staging, surgery, radiotherapy, tumor size, bone metastasis, brain metastasis, and liver metastasis were risk factors for the occurrence of PCLM. According to multivariate Cox regression analysis, age, grade, tumor size, histological subtype, surgery, chemotherapy, liver metastasis, and bone metastasis were independent prognostic factors for PCLM patients' OS. Nomograms were constructed based on these factors to predict 6-, 12-, and 18-months OS of patients with PCLM. AUC, C-index, calibration curves, and DCA revealed that the two novel nomograms had good predictive power.ConclusionWe developed two reliable predictive models for clinical practice to assist clinicians in developing individualized treatment plans for patients.

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“…The filters used to retrieve features from images include CLAHE (contrast limited adaptive histogram equalization) [24], Gabor filter [25], Gamma Correction [26], Gaussian filter [27], Hessian [8], Laplacian operator [28], Median filter [29], Mean filter [30], Minimum filter [31], Bilateral filter [32], Sobel operator [33], Canny edge detector [34], as well as the ten filters predefined in the imageFilter module of Pillow [35], which are BLUR, CONTOUR, DE-TAIL, EDGE ENHANCE, EDGE ENHANCE MORE, EMBOSS, FIND EDGES, SMOOTH, SMOOTH MORE and SHARPEN. The mathematical definitions of these filters/operators are shown in Table 4.…”

Section: Feature Transformationmentioning

confidence: 99%

Compensating small data with large filters for accurate liver vessel segmentation

Chen

Zhao

Bian

et al. 2023

Preprint

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Background: Segmenting liver vessels on computed tomography images is essential for diagnosing liver diseases, planning surgeries and delivering radiotherapy. Nevertheless, identifying vessels is a challenging task due to the tiny cross-sectional areas occupied by vessels, which has posed great challenges for vessel segmentation, such as limited features to be learned and difficult to construct high-quality as well as large-volume data. Methods: We present an approach that only requires a few labeled vessels but delivers significantly improved results. Our model starts with vessel enhancement by fading out liver intensity and generates candidate vessels by a classifier fed with a large number of image filters. Afterwards, the initial segmentation is refined using Markov random fields. Results: In experiments on the well-known dataset 3D-IRCADb, the accuracy is improved to 0.99, and the averaged Dice coefficient is lifted to 0.63. These results are significantly better than those obtained from existing machine-learning approaches and comparable to those generated from deep-learning models. Conclusion: Sophisticated integration of large number filters is able to pinpoint effective features from liver images that are sufficient to distinguish vessels from other liver tissues under a scarcity of large-volume labeled data. The study can shed light on medical image segmentation, especially for those without sufficient data.

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A novel approach for evaluating bone mineral density of hips based on Sobel gradient-based map of radiographs utilizing convolutional neural network

Cited by 18 publications

References 18 publications

Deep Learning for Bone Mineral Density and T-Score Prediction from Chest X-rays: A Multicenter Study

Deep Learning for Bone Mineral Density and T-Score Prediction from Chest X-rays: A Multicenter Study

Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study

Compensating small data with large filters for accurate liver vessel segmentation

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