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Automatic detection of gastrointestinal system abnormalities using deep learning-based segmentation and classification methods

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HEALTH INFORMATION SCIENCE AND SYSTEMS 2025年第1期13卷 1-25页

作者： Sener, Abdullah Ergen, Burhan Firat Univ Fac Econ & Adm Sci Management Informat Syst TR-23100 Elazig Turkiye Firat Univ Fac Engn Comp Engn TR-23100 Elazig Turkiye

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 deep learning 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 deep learning-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 deep learning 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 deep learning approaches can serve as reliable tools for the detection and classification of diseases of the GI system.

关键词： Gastrointestinal system abnormalities GISegNet deep learning-based segmentation Automated classification and diagnosis Endoscopic & imath mage analysis

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deep learning-based segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic Images 11th

Deep Learning-Based Segmentation of the Atherosclerotic Caro...

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18th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Liapi, Georgia D. Kyriacou, Efthyvoulos Loizou, Christos P. Panayides, Andreas S. Pattichis, Constantinos S. Nicolaides, Andrew N. Cyprus Univ Technol Dept Elect Engn Comp Engn & Informat CY-3036 Limassol Cyprus CYENS Ctr Excellence Nicosia Cyprus Univ Cyprus Dept Comp Sci Nicosia Cyprus Vasc Screening & Diagnost Ctr Nicosia Cyprus

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.

关键词： Carotid ultrasound video Atherosclerotic carotid plaques Automated segmentation deep learning-based segmentation Computer-aided diagnosis

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Characterization of the pore structure in Chinese anthracite coal using FIB-SEM tomography and deep learning-based segmentation

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ENERGY 2023年第1期282卷

作者： Zang, Jie Liu, Jialong He, Jiabei Zhang, Xiapeng China Univ Min & Technol Fac Emergency Management & Safety Engn Beijing 100083 Peoples R China Beijing Univ Chem Technol Sch Math & Phys Dept Phys & Elect Beijing 100029 Peoples R China Chinese Acad Sci Inst Geol & Geophys Key Lab Shale Gas & Geoengn Beijing 100029 Peoples R China Shanxi Xinjing Coal Min Co Yangquan 045000 Peoples R China D11 Xueyuan Rd Beijing Peoples R China

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.

关键词： FIB-SEM tomography deep learning-based segmentation Pore structure in coal Connectivity Primary CBM recovery

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Improved 3D image segmentation for X-ray tomographic data of biaxial warp-knitted composites

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JOURNAL OF REINFORCED PLASTICS AND COMPOSITES 2025年第7-8期44卷 344-357页

作者： Zheng, Kehong Wu, Chenglie Chen, Hao Zhang, Xiyan Wang, Zhenyu Pan, Zhongxiang Qiu, Bingjing Wu, Zhenyu Zhejiang Sci Tech Univ Coll Mech Engn Xiasha Zhejiang Peoples R China Zhejiang Univ Coll Civil Engn & Architecture Hangzhou Peoples R China CenterSinohydro Bur 12 Co Ltd Hangzhou Zhejiang Peoples R China Zhejiang Sci Tech Univ Coll Mat & Text Hangzhou Peoples R China Zhejiang Univ Ctr Hypergrav Expt & Interdisciplinary Res Hangzhou Peoples R China

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 deep learning 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.

关键词： X-ray computed tomography (XCT) images dataset augmentation ResUNet plus plus model deep learning-based segmentation image-based modeling

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deep learning-based, fully automated, pediatric brain segmentation

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Kim, Min-Jee Hong, Eunpyeong Yum, Mi-Sun Lee, Yun-Jeong Kim, Jinyoung Ko, Tae-Sung Ulsan Univ Coll Med Dept Pediat Asan Med CtrChildrens Hosp 88 Olymp Ro 43-Gil Seoul 05505 South Korea Vuno Inc Seoul 06541 South Korea Kyungpook Natl Univ Kyungpook Natl Univ Hosp Dept Pediat Daegu South Korea Kyungpook Natl Univ Sch Med Daegu South Korea

The purpose of this study was to demonstrate the performance of a fully automated, deep learning-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.

关键词： Dravet syndrome deep learning-based segmentation Convolutional neural network VUNO Med-deepBrain

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Improved XCT image automatic segmentation for quantitative characterization of the meso-morphological features in the damaged braided composite fabric

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COMPOSITES SCIENCE AND TECHNOLOGY 2024年 247卷

作者： Zheng, Kehong Cao, Xiaoqi Jiang, Zheyang Chen, Hao Qiu, Bingjing Lu, Wenpan Wu, Chenglie Pan, Zhongxiang Wu, Zhenyu Zhejiang Scitech Univ Hangzhou Coll Mech Engn Xiasha 310018 Zhejiang Peoples R China Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China Zhejiang Sci Tech Univ Coll Mat & Text Hangzhou 310018 Peoples R China Zhejiang Univ Ctr Hypergrav Expt & Interdisciplinary Res Hangzhou 310058 Peoples R China Natl Res Inst Rural Electrificat Hangzhou Reg Ctr Asia Pacific Small Hydro Power Hangzhou 310012 Peoples R China

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.

关键词： X-ray computed tomography (XCT) deep learning-based segmentation Dataset augmentation Damaged braided composite fabric Meso-morphological feature

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Multi-center external validation of an automated method segmenting and differentiating atypical lipomatous tumors from lipomas using radiomics and deep-learning on MRI

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ECLINICALMEDICINE 2024年 76卷 102802-102802页

作者： Spaanderman, D. J. Hakkesteegt, S. N. Hanff, D. F. Schut, A. R. W. Schiphouwer, L. M. Vos, M. Messiou, C. Doran, S. J. Jones, R. L. Hayes, A. J. Nardo, L. Abdelhafez, Y. G. Moawad, A. W. Elsayes, K. M. Lee, S. Link, T. M. Niessen, W. J. van Leenders, G. J. L. H. Visser, J. J. Klein, S. Grunhagen, D. J. Verhoef, C. Starmans, M. P. A. Erasmus MC Dept Radiol & Nucl Med Rotterdam Netherlands Erasmus Univ Dept Surg Oncol & Gastrointestinal Surg Erasmus MC Canc Inst Med Ctr Rotterdam Netherlands Royal Marsden Hosp Inst Canc Res London London England UC Davis Hlth Dept Radiol Sacramento CA USA Univ Texas MD Anderson Canc Ctr Dept Diagnost Imaging Houston TX USA Mercy Catholic Med Ctr Dept Gastroenterol Darby PA USA Univ Calif Irvine Dept Radiol Sci Irvine CA USA Univ Calif San Francisco Dept Radiol & Biomed Imaging San Francisco CA USA Univ Groningen Fac Med Sci Groningen Netherlands Erasmus MC Dept Pathol Rotterdam Netherlands

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

关键词： Lipomatous tumors Radiomics External validation Prospective validation deep learning-based segmentation Magnetic resonance imaging

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