Journal of X-Ray Science and Technology - Volume 31, issue 3

Authors: Yao, Yao | Jia, Chuanliang | Zhang, Haicheng | Mou, Yakui | Wang, Cai | Han, Xiao | Yu, Pengyi | Mao, Ning | Song, Xicheng

Article Type: Research Article

Abstract: PURPOSE: To identify the value of a computed tomography (CT)-based radiomics model to predict probability of early recurrence (ER) in patients diagnosed with laryngeal squamous cell carcinoma (LSCC) after surgery. MATERIALS AND METHOD: Pre-operative CT scans of 140 LSCC patients treated by surgery are reviewed and selected. These patients are randomly split into the training set (n  = 97) and test set (n  = 43). The regions of interest of each patient were delineated manually by two senior radiologists. Radiomics features are extracted from CT images acquired in non-enhanced, arterial, and venous phases. Variance threshold, one-way ANOVA, and least absolute …shrinkage and selection operator algorithm are used for feature selection. Then, radiomics models are built with five algorithms namely, k-nearest neighbor (KNN), logistic regression (LR), linear support vector machine (LSVM), radial basis function SVM (RSVM), and polynomial SVM (PSVM). Clinical factors are selected using univariate and multivariate logistic regressions. Last, a radiomics nomogram incorporating the radiomics signature and clinical factors is built to predict ER and its efficiency is evaluated by receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) is also used to evaluate clinical usefulness. RESULTS: Four features are remarkably associated with ER in patients with LSCC. Applying to test set, the area under the ROC curves (AUCs) of KNN, LR, LSVM, RSVM, and PSVM are 0.936, 0.855, 0.845, 0.829, and 0.794, respectively. The radiomics nomogram shows better discrimination (with AUC: 0.939, 95% CI: 0.867–0.989) than the best radiomics model and the clinical model. Predicted and actual ERs in the calibration curves are in good agreement. DCA shows that the radiomics nomogram is clinically useful. CONCLUSION: The radiomics nomogram, as a noninvasive prediction tool, exhibits favorable performance for ER prediction of LSCC patients after surgery. Show more

Keywords: Laryngeal squamous cell carcinoma, nomogram, early recurrence, radiomics

DOI: 10.3233/XST-221320

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 435-452, 2023

Authors: Tsai, Meng-Yuan | Liang, Huei-Lung | Chuo, Chiung-Chen | Li, Chia-Wei | Ai-Chih , Chen | Hsiao, Chia-Chi

Article Type: Research Article

Abstract: PURPOSE: This study aims to introduce a novel low-dose abdominal computed tomography (CT) protocol adapted with model-based iterative reconstruction (MBIR), To validate the adaptability of this protocol, objective image quality and subjective clinical scores of low-dose MBIR images are compared with the normal-dose images. METHODS: Normal-dose abdominal CT images of 58 patients and low-dose abdominal CT images of 52 patients are reconstructed using both conventional filtered back projection (FBP) and MBIR methods with and without smooth applying. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) are used to compare image quality between the normal-dose and low-dose CT …scans. CT dose indices (CTDI) of normal-dose and low-dose abdominal CT images on post-contrast venous phase are also compared. RESULTS: The SNR, CNR and clinical score of low-dose MBIR images all show significant higher values (Bonferroni p  < 0.05) than those of normal-dose images with conventional FBP method. A total of around 40% radiation dose reduction (CTDI: 5.3 vs 8.7 mGy) could be achieved via our novel abdominal CT protocol. CONCLUSIONS: With the higher SNR/CNR and clinical scores, the low-dose CT abdominal imaging protocol with MBIR could effectively reduce the radiation for patients and provide equal or even higher image quality and also its adaptability in clinical abdominal CT image diagnosis. Show more

Keywords: Low-dose computed tomography (low-dose CT), model-based iterative reconstruction (MBIR), filtered back projection (FBP), Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)

DOI: 10.3233/XST-221325

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 453-461, 2023

Authors: Wang, Chengxiang | Gordon, Richard

Article Type: Research Article

Abstract: BACKGROUND: The Mueller, Siddon and Joseph weighting algorithms are frequently used for projection and back-projection, which are relatively complicated when they are implemented in computer code. OBJECTIVE: This study aims to reduce the actual complexity of the projection and back-projection. METHODS: First, we neglect the exact shape of the pixel, so that its shadow is a rectangle projecting precisely to a detector bin, which implies that all the pixel weights are exactly 1 for each ray through them, otherwise are exactly 0. Next, a one-to-one reversible image rotation algorithm (RIRA) is proposed to compute the projection …and back-projection, where two one-to-one mapping lists namely, U and V, are used to store the coordinates of a rotated pixel and its corresponding new coordinates, respectively. For each 2D projection, the projection is simply the column sum in each orientation according to the lists U and V. For each 2D back-projection, it is simply to arrange the projection to the corresponding column element according to the lists U and V. Thus, there is no need for an interpolation in the projection and back-projection. Last, a rotating image computed tomography (RICT) based on RIRA is proposed to reconstruct the image. RESULTS: Experiments show the RICT reconstructs a good image that is close to the result of filtered back-projection (FBP) method according to the RMSE, PSNR and MSSIM values. What’s more, our weight, projection and back-projection are much easier to be implemented in computer code than the FBP method. CONCLUSION: This study demonstrates that the RIRA method has potential to be used to simplify many computed tomography image reconstruction algorithms. Show more

Keywords: Computed tomography (CT), image reconstruction, limited-angle CT, reversible image rotation

DOI: 10.3233/XST-221248

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 463-482, 2023

Authors: Deepak, Gerard | Madiajagan, M. | Kulkarni, Sanjeev | Ahmed, Ahmed Najat | Gopatoti, Anandbabu | Ammisetty, Veeraswamy

Article Type: Research Article

Abstract: BACKGROUND: COVID-19 is the most dangerous virus, and its accurate diagnosis saves lives and slows its spread. However, COVID-19 diagnosis takes time and requires trained professionals. Therefore, developing a deep learning (DL) model on low-radiated imaging modalities like chest X-rays (CXRs) is needed. OBJECTIVE: The existing DL models failed to diagnose COVID-19 and other lung diseases accurately. This study implements a multi-class CXR segmentation and classification network (MCSC-Net) to detect COVID-19 using CXR images. METHODS: Initially, a hybrid median bilateral filter (HMBF) is applied to CXR images to reduce image noise and enhance the COVID-19 infected …regions. Then, a skip connection-based residual network-50 (SC-ResNet50) is used to segment (localize) COVID-19 regions. The features from CXRs are further extracted using a robust feature neural network (RFNN). Since the initial features contain joint COVID-19, normal, pneumonia bacterial, and viral properties, the conventional methods fail to separate the class of each disease-based feature. To extract the distinct features of each class, RFNN includes a disease-specific feature separate attention mechanism (DSFSAM). Furthermore, the hunting nature of the Hybrid whale optimization algorithm (HWOA) is used to select the best features in each class. Finally, the deep-Q-neural network (DQNN) classifies CXRs into multiple disease classes. RESULTS: The proposed MCSC-Net shows the enhanced accuracy of 99.09% for 2-class, 99.16% for 3-class, and 99.25% for 4-class classification of CXR images compared to other state-of-art approaches. CONCLUSION: The proposed MCSC-Net enables to conduct multi-class segmentation and classification tasks applying to CXR images with high accuracy. Thus, together with gold-standard clinical and laboratory tests, this new method is promising to be used in future clinical practice to evaluate patients. Show more

Keywords: COVID-19, chest X-Ray, hybrid median bilateral filter, robust feature neural network, deep-Q-neural networks

DOI: 10.3233/XST-221360

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 483-509, 2023

Authors: Lian, Kai-Mei | Lin, Teng

Article Type: Research Article

Abstract: OBJECTIVE: To explore the value of color-coded virtual touch tissue imaging (CCV) using acoustic radiation force pulse technology (ARFI) in diagnosing malignant thyroid nodules. METHODS: Images including 189 thyroid nodules were collected as training samples and a binary logistic regression analysis was used to calculate regression coefficients for Thyroid Imaging Reporting and Data System (TI-RADS) and CCV. An integrated prediction model (TI-RADS+CCV) was then developed based on the regression coefficients. Another testing dataset involving 40 thyroid nodules was used to validate and compare the diagnostic performance of TI-RADS, CCV, and the integrated predictive models using the receiver operating …characteristic (ROC) curves. RESULTS: Both TI-RADS and CCV are independent predictors. The diagnostic performance advantage of CCV is insignificant compared to TI-RADS (P  = 0.61). However, the diagnostic performance of the integrated prediction model is significantly higher than that of TI-RADS or CCV (all P  < 0.05). Applying to the validation image dateset, the integrated predictive model yields an area under the curve (AUC) of 0.880. CONCLUSIONS: Developing a new predictive model that integrates the regression coefficients calculated from TI-RADS and CCV enables to achieve the superior performance of thyroid nodule diagnosis to that of using TI-RADS or CCV alone. Show more

Keywords: Elastography, acoustic radiation force impulse, thyroid imaging reporting and data system, thyroid nodules

DOI: 10.3233/XST-221359

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 511-523, 2023

Authors: Xu, Min | Wang, Lijia

Article Type: Research Article

Abstract: BACKGROUND: Cardiac cine magnetic resonance (CCMR) imaging plays an important role in the clinical cardiovascular disease (CVD) examination and evaluation. OBJECTIVE: To accurately reconstruct the displacement field and describe the motion of the left ventricular myocardium (LVM), this study proposes and tests a new approach for tracking myocardial motion of the left ventricle based on a biomechanical model. METHODS: CCMR imaging data acquired from 103 patients are enrolled, including two simulated and 101 clinical data. A non-rigid image registration method with a combination of a thin-plate spline function and random sample consensus is used to recover …the observed displacement field of LVM. Next, a biomechanical model and a material matrix are introduced to solve the dense displacement field of LVM using a finite element framework. Then, the tracking precision and error of results for the two groups are analyzed. RESULTS: Displacement results of the simulated data show correlation coefficient≥0.876 and mean square error≤0.159, while displacement results of the clinical data show Dice≥0.97 and mean contour distance≤0.464. Additionally, the strain results show correlation coefficient≥0.717. CONCLUSIONS: This study demonstrates that the proposed new method enables to accurately track the motion of the LVM and evaluate strain, which has clinical auxiliary value in the diagnosis of CVD. Show more

Keywords: Left ventricular myocardium, cine magnetic resonance images, biomechanical model, material matrix

DOI: 10.3233/XST-221331

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 525-543, 2023

Authors: Pu, Doudou | Yuan, Hui | Ma, Guangming | Duan, Haifeng | Zhang, Min | Yu, Nan

Article Type: Research Article

Abstract: OBJECTIVE: To explore the application of quantitative computed tomography (CT) in analyses of lung changes in patients with rheumatoid arthritis (RA). METHODS: A total of 150 clinically diagnosed RA patients underwent chest CT and 150 matched non-smokers subjects with normal chest CT are enrolled. A CT software is applied to analyze CT obtained from both groups. The quantitative indices of emphysema are expressed as the percentage of lung area with attenuation < –950HU to the total lung volume (LAA–950 %), and pulmonary fibrosis was expressed as the percentage of lung area with a attenuation of –200 to …–700HU to the total lung volume (LAA–200––700 %), quantitative indicators of pulmonary vascular include aortic diameter (AD), pulmonary artery diameter (PAD), the ratio of PAD to AD (PAD/AD ratio), the number of blood vessels (TNV), and the cross area of blood vessels (TAV). The receiver operating characteristic (ROC) curve is used to evaluate the ability of these indexes in identifying the changes in the lung in RA patients. RESULTS: Compared to the control group, the RA group has significantly lower TLV, larger AD, and smaller TNV and TAV (3921±1101 vs. 4490±1046, 33.26±4.20 vs. 32.95±3.76, 13.14±4.93 vs. 17.53±3.34, and 96.89±40.62 vs. 163.32±34.97, respectively, with all p  < 0.001). Peripheral vascular indicator TAV has the better ability to identify lung changes in RA patients (area under ROC curve AUC = 0.894) than TNV (AUC = 0.780) or LAA–200 &sim–700 % (AUC = 0.705). CONCLUSION: Quantitative CT can detect changes in lung density distribution and peripheral vascular injury in patients with RA and assess the severity. Show more

Keywords: Rheumatoid arthritis, quantitative analysis, pulmonary, interstitial lung disease, computed tomography

DOI: 10.3233/XST-221329

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 545-553, 2023

Authors: Ji, Dongjiang | Xue, Xiying | Xu, Chunyu

Article Type: Research Article

Abstract: BACKGROUND: In medical applications, computed tomography (CT) is widely used to evaluate various sample characteristics. However, image quality of CT reconstruction can be degraded due to artifacts. OBJECTIVE: To propose and test a truncated total variation (truncation TV) model to solve the problem of large penalties for the total variation (TV) model. METHODS: In this study, a truncated TV image denoising model in the fractional B-spline wavelet domain is developed to obtain the best solution. The method is validated by the analysis of CT reconstructed images of actual biological Pigeons samples. For this purpose, several indices …including the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and mean square error (MSE) are used to evaluate the quality of images. RESULTS: Comparing to the conventional truncated TV model that yields 22.55, 0.688 and 361.17 in PSNR, SSIM and MSE, respectively, using the proposed fractional B-spline-truncated TV model, the computed values of these evaluation indices change to 24.24, 0.898 and 244.98, respectively, indicating substantial reduction of image noise with higher PSNR and SSIM, and lower MSE. CONCLUSIONS: Study results demonstrate that compared with many classic image denoising methods, the new denoising algorithm proposed in this study can more effectively suppresses the reconstructed CT image artifacts while maintaining the detailed image structure. Show more

Keywords: Digital image processing, image denoising, fractional B-spline wavelet, truncated total variation (truncated TV) model, Split-Bregman iteration

DOI: 10.3233/XST-221326

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 555-572, 2023

Authors: Qin, Yanwei | Zhang, Yinghui | Lu, Xin | Zhao, Yunsong | Zhao, Xing

Article Type: Research Article

Abstract: Limited-angle CT scan is an effective way for nondestructive inspection of planar objects, and various methods have been proposed accordingly. When the scanned object contains high-absorption material, such as metal, existing methods may fail due to the beam hardening of X-rays. In order to overcome this problem, we adopt a dual spectral limited-angle CT scan and propose a corresponding image reconstruction algorithm, which takes the polychromatic property of the X-ray into consideration, makes basis material images free of beam hardening artifacts and metal artifacts, and then helps depress the limited-angle artifacts. Experimental results on both simulated PCB data and real …data demonstrate the effectiveness of the proposed algorithm. Show more

Keywords: Planar objects, dual spectral limited-angle CT imaging, the high-absorption material

DOI: 10.3233/XST-221302

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 573-592, 2023

Authors: Wang, Lei | Liu, Yi | Wu, Rui | Yan, Rongbiao | Liu, Yuhang | Chen, Yang | Yang, Chunfeng | Gui, Zhiguo

Article Type: Research Article

Abstract: Background: Low-Dose computed tomography (LDCT) reduces radiation damage to patients, however, the reconstructed images contain severe noise, which affects doctors’ diagnosis of the disease. The convolutional dictionary learning has the advantage of the shift-invariant property. The deep convolutional dictionary learning algorithm (DCDicL) combines deep learning and convolutional dictionary learning, which has great suppression effects on Gaussian noise. However, applying DCDicL to LDCT images cannot get satisfactory results. Objective: To address this challenge, this study proposes and tests an improved deep convolutional dictionary learning algorithm for LDCT image processing and denoising. Methods: First, we use a …modified DCDicL algorithm to improve the input network and make it do not need to input noise intensity parameter. Second, we use DenseNet121 to replace the shallow convolutional network to learn the prior on the convolutional dictionary, which can obtain more accurate convolutional dictionary. Last, in the loss function, we add MSSIM to enhance the detail retention ability of the model. Results: The experimental results on the Mayo dataset show that the proposed model obtained an average value of 35.2975 dB in PSNR, which is 0.2954 –1.0573 dB higher than the mainstream LDCT algorithm, indicating the excellent denoising performance. Conclusion: The study demonstrates that the proposed new algorithm can effectively improve the quality of LDCT images acquired in the clinical practice. Show more

Keywords: Low-dose computed tomography, DenseNet, deep learning, convolutional dictionary learning.

DOI: 10.3233/XST-221358

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 593-609, 2023

Authors: Luo, Zhendong | Li, Jing | Liao, YuTing | Huang, Wenxiao | Li, Yulin | Shen, Xinping

Article Type: Research Article

Abstract: PURPOSE: This study aims to evaluate the value of applying X-ray and magnetic resonance imaging (MRI) models based on radiomics feature to predict response of extremity high-grade osteosarcoma to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: A retrospective dataset was assembled involving 102 consecutive patients (training dataset, n  = 72; validation dataset, n  = 30) diagnosed with extremity high-grade osteosarcoma. The clinical features of age, gender, pathological type, lesion location, bone destruction type, size, alkaline phosphatase (ALP), and lactate dehydrogenase (LDH) were evaluated. Imaging features were extracted from X-ray and multi-parametric MRI (T1-weighted, T2-weighted, and contrast-enhanced T1-weighted) data. Features were selected …using a two-stage process comprising minimal-redundancy-maximum-relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression. Logistic regression (LR) modelling was then applied to establish models based on clinical, X-ray, and multi-parametric MRI data, as well as combinations of these datasets. Each model was evaluated using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). RESULTS: AUCs of 5 models using clinical, X-ray radiomics, MRI radiomics, X-ray plus MRI radiomics, and combination of all were 0.760 (95% CI: 0.583–0.937), 0.706 (95% CI: 0.506–0.905), 0.751 (95% CI: 0.572–0.930), 0.796 (95% CI: 0.629–0.963), 0.828 (95% CI: 0.676–0.980), respectively. The DeLong test showed no significant difference between any pair of models (p  > 0.05). The combined model yielded higher performance than the clinical and radiomics models as demonstrated by net reclassification improvement (NRI) and integrated difference improvement (IDI) values, respectively. This combined model was also found to be clinically useful in the decision curve analysis (DCA). CONCLUSION: Modelling based on combination of clinical and radiomics data improves the ability to predict pathological responses to NAC in extremity high-grade osteosarcoma compared to the models based on either clinical or radiomics data. Show more

Keywords: Radiomics, prognostic indicator, X-ray, Magnetic resonance imaging, Osteosarcoma, chemotherapy response

DOI: 10.3233/XST-221352

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 611-626, 2023

Authors: Haraguchi, Takafumi | Kobayashi, Yasuyuki | Hirahara, Daisuke | Kobayashi, Tatsuaki | Takaya, Eichi | Nagai, Mariko Takishita | Tomita, Hayato | Okamoto, Jun | Kanemaki, Yoshihide | Tsugawa, Koichiro

Article Type: Research Article

Abstract: BACKGROUND: In breast cancer diagnosis and treatment, non-invasive prediction of axillary lymph node (ALN) metastasis can help avoid complications related to sentinel lymph node biopsy. OBJECTIVE: This study aims to develop and evaluate machine learning models using radiomics features extracted from diffusion-weighted whole-body imaging with background signal suppression (DWIBS) examination for predicting the ALN status. METHODS: A total of 100 patients with histologically proven, invasive, clinically N0 breast cancer who underwent DWIBS examination consisting of short tau inversion recovery (STIR) and DWIBS sequences before surgery were enrolled. Radiomic features were calculated using segmented primary lesions in …DWIBS and STIR sequences and were divided into training (n  = 75) and test (n  = 25) datasets based on the examination date. Using the training dataset, optimal feature selection was performed using the least absolute shrinkage and selection operator algorithm, and the logistic regression model and support vector machine (SVM) classifier model were constructed with DWIBS, STIR, or a combination of DWIBS and STIR sequences to predict ALN status. Receiver operating characteristic curves were used to assess the prediction performance of radiomics models. RESULTS: For the test dataset, the logistic regression model using DWIBS, STIR, and a combination of both sequences yielded an area under the curve (AUC) of 0.765 (95% confidence interval: 0.548–0.982), 0.801 (0.597–1.000), and 0.779 (0.567–0.992), respectively, whereas the SVM classifier model using DWIBS, STIR, and a combination of both sequences yielded an AUC of 0.765 (0.548–0.982), 0.757 (0.538–0.977), and 0.779 (0.567–0.992), respectively. CONCLUSIONS: Use of machine learning models incorporating with the quantitative radiomic features derived from the DWIBS and STIR sequences can potentially predict ALN status. Show more

Keywords: Diffusion-weighted whole-body imaging, background signal suppression, DWIBS, radiomics, axillary lymph node status, breast cancer, machine learning

DOI: 10.3233/XST-230009

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 627-640, 2023

Authors: Yang, Fan | Weng, Xin | Wu, Yuhui | Miao, Yuehong | Lei, Pinggui | Hu, Zuquan

Article Type: Research Article

Abstract: BACKGROUND: Ulna and radius segmentation of dual-energy X-ray absorptiometry (DXA) images is essential for measuring bone mineral density (BMD). OBJECTIVE: To develop and test a novel deep learning network architecture for robust and efficient ulna and radius segmentation on DXA images. METHODS: This study used two datasets including 360 cases. The first dataset included 300 cases that were randomly divided into five groups for five-fold cross-validation. The second dataset including 60 cases was used for independent testing. A deep learning network architecture with dual residual dilated convolution module and feature fusion block based on residual U-Net …(DFR-U-Net) to enhance segmentation accuracy of ulna and radius regions on DXA images was developed. The Dice similarity coefficient (DSC), Jaccard, and Hausdorff distance (HD) were used to evaluate the segmentation performance. A one-tailed paired t -test was used to assert the statistical significance of our method and the other deep learning-based methods (P  < 0.05 indicates a statistical significance). RESULTS: The results demonstrated our method achieved the promising segmentation performance, with DSC of 98.56±0.40% and 98.86±0.25%, Jaccard of 97.14±0.75% and 97.73±0.48%, and HD of 6.41±11.67 pixels and 8.23±7.82 pixels for segmentation of ulna and radius, respectively. According to statistics data analysis results, our method yielded significantly higher performance than other deep learning-based methods. CONCLUSIONS: The proposed DFR-U-Net achieved higher segmentation performance for ulna and radius on DXA images than the previous work and other deep learning approaches. This methodology has potential to be applied to ulna and radius segmentation to help doctors measure BMD more accurately in the future Show more

Keywords: DXA images, deep learning, ulna and radius segmentation, feature fusion block, dual residual dilated convolution module

DOI: 10.3233/XST-230010

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 641-653, 2023

Authors: Li, Jin | Yin, Wei | Wang, Yuanjun

Article Type: Research Article

Abstract: BACKGROUND: Automatic segmentation of the pancreas and its tumor region is a prerequisite for computer-aided diagnosis. OBJECTIVE: In this study, we focus on the segmentation of pancreatic cysts in abdominal computed tomography (CT) scan, which is challenging and has the clinical auxiliary diagnostic significance due to the variability of location and shape of pancreatic cysts. METHODS: We propose a convolutional neural network architecture for segmentation of pancreatic cysts, which is called pyramid attention and pooling on convolutional neural network (PAPNet). In PAPNet, we propose a new atrous pyramid attention module to extract high-level features at different …scales, and a spatial pyramid pooling module to fuse contextual spatial information, which effectively improves the segmentation performance. RESULTS: The model was trained and tested using 1,346 CT slice images obtained from 107 patients with the pathologically confirmed pancreatic cancer. The mean dice similarity coefficient (DSC) and mean Jaccard index (JI) achieved using the 5-fold cross-validation method are 84.53% and 75.81%, respectively. CONCLUSIONS: The experimental results demonstrate that the proposed new method in this study enables to achieve effective results of pancreatic cyst segmentation. Show more

Keywords: Pancreatic cyst, medical image segmentation, convolutional neural network, computed tomography

DOI: 10.3233/XST-230011

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 655-668, 2023