Early diagnosis and precise treatment of gastrointestinal (GI) diseases are crucial for reducing mortality and improving quality of life. In this context, the detection and classification of abnormalities in endoscopi...
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Early diagnosis and precise treatment of gastrointestinal (GI) diseases are crucial for reducing mortality and improving quality of life. In this context, the detection and classification of abnormalities in endoscopic images is an important support for specialists during the diagnostic process. In this study, an innovative deeplearning approach for the segmentation and classification of pathological regions in the GI system is presented. In the first phase of the study, a novel segmentation network called GISegNet was developed. GISegNet is a deeplearning-based architecture tailored for accurate detection of anomalies in the GI system. Experiments conducted on the Kvasir dataset showed that GISegNet achieved excellent results on performance metrics such as Jaccard and Dice coefficients and outperformed other segmentation models with a higher accuracy rate (93.16%). In the second phase, a hybrid deeplearning method was proposed for classifying anomalies in the GI system. The features extracted from the transformer-based models were fused and optimized using the Minimum Redundancy Maximum Relevance (mRMR) algorithm. The classification process was performed using Support Vector Machines (SVM). As a result of feature fusion and selection, the second model, which combined features from DeiT and ViT models, achieved the best performance with an accuracy rate of 95.2%. By selecting a subset of 300 features optimized by the mRMR algorithm, the accuracy (95.3%) was maintained while optimizing the computational cost. These results show that the proposed deeplearning approaches can serve as reliable tools for the detection and classification of diseases of the GI system.
Early stroke risk stratification in individuals with carotid atherosclerosis is of great importance, especially in high-risk asymptomatic (AS) cases. In this study, we present a new computer-aided diagnostic (CAD) sys...
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ISBN:
(纸本)9783031083419;9783031083402
Early stroke risk stratification in individuals with carotid atherosclerosis is of great importance, especially in high-risk asymptomatic (AS) cases. In this study, we present a new computer-aided diagnostic (CAD) system for the automated segmentation of the atherosclerotic plaque in carotid ultrasound (US) images and the extraction of a refined set of ultrasonic features to robustly characterize plaques in carotid US images and videos (AS vs symptomatic (SY)). So far, we trained a UNet model (16 to 256 neurons in the contracting path;the reverse, for the expanding path), starting from a dataset of 201 (AS = 109 and SY = 92) carotid US videos of atherosclerotic plaques, from which their first frames were extracted to prepare three subsets, a training, an internal validation, and final evaluation set, with 150, 30 and 15 images, respectively. The automated segmentations were evaluated based on manual segmentations, performed by a vascular surgeon. To assess our model's capacity to segment plaques in previously unseen images, we calculated 4 evaluation metrics (mean +/- std). The evaluation of the proposed model yielded a 0.736 +/- 0.10 Dice similarity score (DSC), a 0.583 +/- 0.12 intersection of union (IoU), a 0.728 +/- 0.10 Cohen's Kappa coefficient (KI) and a 0.65 +/- 0.19 Hausdorff distance. The proposed segmentation workflow will be further optimized and evaluated, using a larger dataset and more neurons in each UNet layer, as in the original model architecture. Our results are close to others published in relevant studies.
Coalbed methane (CBM) is an unconventional natural gas that possesses significant impacts on energy supply, mining safety, and environmental conservation. CBM is primarily stored within the pores of coal, highlighting...
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Coalbed methane (CBM) is an unconventional natural gas that possesses significant impacts on energy supply, mining safety, and environmental conservation. CBM is primarily stored within the pores of coal, highlighting the significance of pore structures for both methane storage and migration. However, comprehensively under-standing the intricate pore structures in coal pose challenges. This study employed focused ion beam-scanning electron microscopy (FIB-SEM) tomography and deep learning-based segmentation to characterize the pore structures within a Chinese anthracite sample. The obtained pore structures exhibited a considerable degree of disconnection, comprising numerous separate pore components. Isolated pores prevailed in number, while connected pores dominated in surface area and pore volume. Mesopores (100-1000 nm) contributed the most to pore number, surface area, and pore volume. Pore size distribution analysis revealed distinct patterns among different pore structure properties, with pore number exhibiting an intensive distribution while surface area and pore volume displaying dispersed distributions. Pore structure connectivities exhibited a hierarchical nature and held distinct meanings at the levels of pore, pore component, and pore network. The pore structure characteristics observed in this study have implications for primary CBM recovery, emphasizing the necessity to improve connectivity between pore components and fractures to enhance production rates and recoverability.
Identifying different features and phases within the biaxial warp-knitted composites enables accurate characterization of the internal changes of the composites under complex loading conditions and different processin...
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Identifying different features and phases within the biaxial warp-knitted composites enables accurate characterization of the internal changes of the composites under complex loading conditions and different processing parameters. However, obtaining 3D reconstructions that can be accurately segmented and quantitatively analyzed is still a challenge, primarily due to the low attenuation of materials, especially the low contrast between the weft yarn and warp yarns. Here, we use deeplearning approaches to identify the boundary of the section of the tow on the 2D image, extract individual warp or weft tows automatically, and this is followed by computational modeling of composite materials. The results show that the performance of ResUNet++ model is better than that of other models for the biaxial warp-knitted composite specimen segmentation tasks. The special improved dataset augmentation algorithm is also a positive effective way to enhance network performance.
The purpose of this study was to demonstrate the performance of a fully automated, deeplearning-based brain segmentation (DLS) method in healthy controls and in patients with neurodevelopmental disorders, SCN1A mutat...
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The purpose of this study was to demonstrate the performance of a fully automated, deeplearning-based brain segmentation (DLS) method in healthy controls and in patients with neurodevelopmental disorders, SCN1A mutation, under eleven. The whole, cortical, and subcortical volumes of previously enrolled 21 participants, under 11 years of age, with a SCN1A mutation, and 42 healthy controls, were obtained using a DLS method, and compared to volumes measured by Freesurfer with manual correction. Additionally, the volumes which were calculated with the DLS method between the patients and the control group. The volumes of total brain gray and white matter using DLS method were consistent with that volume which were measured by Freesurfer with manual correction in healthy controls. Among 68 cortical parcellated volume analysis, the volumes of only 7 areas measured by DLS methods were significantly different from that measured by Freesurfer with manual correction, and the differences decreased with increasing age in the subgroup analysis. The subcortical volume measured by the DLS method was relatively smaller than that of the Freesurfer volume analysis. Further, the DLS method could perfectly detect the reduced volume identified by the Freesurfer software and manual correction in patients with SCN1A mutations, compared with healthy controls. In a pediatric population, this new, fully automated DLS method is compatible with the classic, volumetric analysis with Freesurfer software and manual correction, and it can also well detect brain morphological changes in children with a neurodevelopmental disorder.
Accurately identifying various meso-morphological features and sub-phases within the damaged braided composite fabric is crucial for assessing the mechanical properties of composites under complex loading conditions a...
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Accurately identifying various meso-morphological features and sub-phases within the damaged braided composite fabric is crucial for assessing the mechanical properties of composites under complex loading conditions and different processing parameters. However, obtaining 3D reconstructions that can be quantitatively analyzed is still a challenge, primarily due to the low contrast of the meso-morphological features (yarn tow and matrix crack networks). In this work, a new data enhancement algorithm is proposed to generate a realistic-looking artificial training dataset for enriching the information of the meso-morphological features. Then, the effect of various training networks and training parameters (the size of real and hybrid training dataset, number of epochs) on segmentation performance were examined. Finally, the meso-morphological features were statistically analyzed to accurately evaluate the mechanical properties of the composite material. The work presented here provides an effective tool that enables the quantitative analysis of the dynamic crack evolution process under the axial load of the composite materials.
Background As differentiating between lipomas and atypical lipomatous tumors (ALTs) based on imaging is challenging and requires biopsies, radiomics has been proposed to aid the diagnosis. This study aimed to external...
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Background As differentiating between lipomas and atypical lipomatous tumors (ALTs) based on imaging is challenging and requires biopsies, radiomics has been proposed to aid the diagnosis. This study aimed to externally and prospectively validate a radiomics model differentiating between lipomas and ALTs on MRI in three large, multicenter cohorts, and extend it with automatic and minimally interactive segmentation methods to increase clinical feasibility. Methods Three study cohorts were formed, two for external validation containing data from medical centers in the United States (US) collected from 2008 until 2018 and the United Kingdom (UK) collected from 2011 until 2017, and one for prospective validation consisting of data collected from 2020 until 2021 in the Netherlands. Patient characteristics, MDM2 amplification fi cation status, and MRI scans were collected. An automatic segmentation method was developed to segment all tumors on T1-weighted MRI scans of the validation cohorts. segmentations were subsequently quality scored. In case of insufficient quality, an interactive segmentation method was used. Radiomics performance was evaluated for all cohorts and compared to two radiologists. Findings The validation cohorts included 150 (54% ALT), 208 (37% ALT), and 86 patients (28% ALT) from the US, UK and NL. Of the 444 cases, 78% were automatically segmented. For 22%, interactive segmentation was necessary due to insufficient quality, with only 3% of all patients requiring manual adjustment. External validation resulted in an AUC of 0.74 (95% CI: 0.66, 0.82) in US data and 0.86 (0.80, 0.92) in UK data. Prospective validation resulted in an AUC of 0.89 (0.83, 0.96). The radiomics model performed similar to the two radiologists (US: 0.79 and 0.76, UK: 0.86 and 0.86, NL: 0.82 and 0.85). Interpretation The radiomics model extended with automatic and minimally interactive segmentation methods accurately differentiated between lipomas and ALTs in two large, multi-cent
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