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De A, Das N, Saha PK, Comellas A, Hoffman E, Basu S, Chakraborti T. MSO-GP: 3-D segmentation of large and complex conjoined tree structures. Methods 2024; 229:9-16. [PMID: 38838947 DOI: 10.1016/j.ymeth.2024.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 05/03/2024] [Accepted: 05/29/2024] [Indexed: 06/07/2024] Open
Abstract
Robust segmentation of large and complex conjoined tree structures in 3-D is a major challenge in computer vision. This is particularly true in computational biology, where we often encounter large data structures in size, but few in number, which poses a hard problem for learning algorithms. We show that merging multiscale opening with geodesic path propagation, can shed new light on this classic machine vision challenge, while circumventing the learning issue by developing an unsupervised visual geometry approach (digital topology/morphometry). The novelty of the proposed MSO-GP method comes from the geodesic path propagation being guided by a skeletonization of the conjoined structure that helps to achieve robust segmentation results in a particularly challenging task in this area, that of artery-vein separation from non-contrast pulmonary computed tomography angiograms. This is an important first step in measuring vascular geometry to then diagnose pulmonary diseases and to develop image-based phenotypes. We first present proof-of-concept results on synthetic data, and then verify the performance on pig lung and human lung data with less segmentation time and user intervention needs than those of the competing methods.
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Affiliation(s)
- Arijit De
- Department of Electronics & Telecommunication Engineering, Jadavpur University, Kolkata, India.
| | - Nirmal Das
- Department of Computer Science and Engineering (AIML), Institute of Engineering and Management, Kolkata, India; Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.
| | - Punam K Saha
- Department of Electrical and Computer Engineering & Department of Radiology, University of Iowa, Iowa City, IA 52242, USA.
| | | | - Eric Hoffman
- Department of Internal Medicine, University of Iowa, Iowa City, USA.
| | - Subhadip Basu
- Department of Computer Science and Engineering (AIML), Institute of Engineering and Management, Kolkata, India.
| | - Tapabrata Chakraborti
- Health Sciences Programme, The Alan Turing Institute, London, UK; Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
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Nan Y, Ser JD, Tang Z, Tang P, Xing X, Fang Y, Herrera F, Pedrycz W, Walsh S, Yang G. Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7391-7404. [PMID: 37204954 DOI: 10.1109/tnnls.2023.3269223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions, especially for cohorts with different lung diseases. The attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This article presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network (FANN) and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization, and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19, and pulmonary fibrosis.
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Chang GH, Wu MY, Yen LH, Huang DY, Lin YH, Luo YR, Liu YD, Xu B, Leong KW, Lai WS, Chiang AS, Wang KC, Lin CH, Wang SL, Chu LA. Isotropic multi-scale neuronal reconstruction from high-ratio expansion microscopy with contrastive unsupervised deep generative models. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107991. [PMID: 38185040 DOI: 10.1016/j.cmpb.2023.107991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/10/2023] [Accepted: 12/19/2023] [Indexed: 01/09/2024]
Abstract
BACKGROUND AND OBJECTIVE Current methods for imaging reconstruction from high-ratio expansion microscopy (ExM) data are limited by anisotropic optical resolution and the requirement for extensive manual annotation, creating a significant bottleneck in the analysis of complex neuronal structures. METHODS We devised an innovative approach called the IsoGAN model, which utilizes a contrastive unsupervised generative adversarial network to sidestep these constraints. This model leverages multi-scale and isotropic neuron/protein/blood vessel morphology data to generate high-fidelity 3D representations of these structures, eliminating the need for rigorous manual annotation and supervision. The IsoGAN model introduces simplified structures with idealized morphologies as shape priors to ensure high consistency in the generated neuronal profiles across all points in space and scalability for arbitrarily large volumes. RESULTS The efficacy of the IsoGAN model in accurately reconstructing complex neuronal structures was quantitatively assessed by examining the consistency between the axial and lateral views and identifying a reduction in erroneous imaging artifacts. The IsoGAN model accurately reconstructed complex neuronal structures, as evidenced by the consistency between the axial and lateral views and a reduction in erroneous imaging artifacts, and can be further applied to various biological samples. CONCLUSION With its ability to generate detailed 3D neurons/proteins/blood vessel structures using significantly fewer axial view images, IsoGAN can streamline the process of imaging reconstruction while maintaining the necessary detail, offering a transformative solution to the existing limitations in high-throughput morphology analysis across different structures.
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Affiliation(s)
- Gary Han Chang
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan, ROC; Graduate School of Advanced Technology, National Taiwan University, Taipei, Taiwan, ROC.
| | - Meng-Yun Wu
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan, ROC
| | - Ling-Hui Yen
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ROC; Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan, ROC
| | - Da-Yu Huang
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan, ROC
| | - Ya-Hui Lin
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ROC; Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan, ROC
| | - Yi-Ru Luo
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ROC; Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan, ROC
| | - Ya-Ding Liu
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ROC; Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan, ROC
| | - Bin Xu
- Department of Psychiatry, Columbia University, New York, NY 10032, USA
| | - Kam W Leong
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | - Wen-Sung Lai
- Department of Psychology, National Taiwan University, Taipei, Taiwan, ROC
| | - Ann-Shyn Chiang
- Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan, ROC; Institute of System Neuroscience, National Tsing Hua University, Hsinchu, Taiwan, ROC
| | - Kuo-Chuan Wang
- Department of Neurosurgery, National Taiwan University Hospital, Taipei, Taiwan, ROC
| | - Chin-Hsien Lin
- Department of Neurosurgery, National Taiwan University Hospital, Taipei, Taiwan, ROC
| | - Shih-Luen Wang
- Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University, Boston, MA 02115, USA
| | - Li-An Chu
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ROC; Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan, ROC.
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Su H, Zhao D, Heidari AA, Cai Z, Chen H, Zhu J. Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats. Basic Clin Pharmacol Toxicol 2024; 134:250-271. [PMID: 37945549 DOI: 10.1111/bcpt.13959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 10/12/2023] [Accepted: 11/07/2023] [Indexed: 11/12/2023]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high-performance optimization method (SCBA) and added the Sobol sequence, vertical crossover and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA-KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA-KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2 and CYP1A1/2 have the most impact on rats. The SCBA-KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment.
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Affiliation(s)
- Hang Su
- College of Computer Science and Technology, Changchun Normal University, Changchun, China
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Zhennao Cai
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, China
| | - Huiling Chen
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, China
| | - Jiayin Zhu
- Laboratory Animal Center, Wenzhou Medical University, Wenzhou, China
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Materka A, Jurek J. Using Deep Learning and B-Splines to Model Blood Vessel Lumen from 3D Images. SENSORS (BASEL, SWITZERLAND) 2024; 24:846. [PMID: 38339562 PMCID: PMC10857344 DOI: 10.3390/s24030846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 01/20/2024] [Accepted: 01/26/2024] [Indexed: 02/12/2024]
Abstract
Accurate geometric modeling of blood vessel lumen from 3D images is crucial for vessel quantification as part of the diagnosis, treatment, and monitoring of vascular diseases. Our method, unlike other approaches which assume a circular or elliptical vessel cross-section, employs parametric B-splines combined with image formation system equations to accurately localize the highly curved lumen boundaries. This approach avoids the need for image segmentation, which may reduce the localization accuracy due to spatial discretization. We demonstrate that the model parameters can be reliably identified by a feedforward neural network which, driven by the cross-section images, predicts the parameter values many times faster than a reference least-squares (LS) model fitting algorithm. We present and discuss two example applications, modeling the lower extremities of artery-vein complexes visualized in steady-state contrast-enhanced magnetic resonance images (MRI) and the coronary arteries pictured in computed tomography angiograms (CTA). Beyond applications in medical diagnosis, blood-flow simulation and vessel-phantom design, the method can serve as a tool for automated annotation of image datasets to train machine-learning algorithms.
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Affiliation(s)
- Andrzej Materka
- Institute of Electronics, Lodz University of Technology, 90-924 Lodz, Poland;
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6
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Zhang M, Wu Y, Zhang H, Qin Y, Zheng H, Tang W, Arnold C, Pei C, Yu P, Nan Y, Yang G, Walsh S, Marshall DC, Komorowski M, Wang P, Guo D, Jin D, Wu Y, Zhao S, Chang R, Zhang B, Lu X, Qayyum A, Mazher M, Su Q, Wu Y, Liu Y, Zhu Y, Yang J, Pakzad A, Rangelov B, Estepar RSJ, Espinosa CC, Sun J, Yang GZ, Gu Y. Multi-site, Multi-domain Airway Tree Modeling. Med Image Anal 2023; 90:102957. [PMID: 37716199 DOI: 10.1016/j.media.2023.102957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/07/2023] [Accepted: 09/04/2023] [Indexed: 09/18/2023]
Abstract
Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and extensive clinical efforts for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Both quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://atm22.grand-challenge.org/).
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Affiliation(s)
- Minghui Zhang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yangqian Wu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Hanxiao Zhang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yulei Qin
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Hao Zheng
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Wen Tang
- InferVision Medical Technology Co., Ltd., Beijing, China
| | | | - Chenhao Pei
- InferVision Medical Technology Co., Ltd., Beijing, China
| | - Pengxin Yu
- InferVision Medical Technology Co., Ltd., Beijing, China
| | - Yang Nan
- Imperial College London, London, UK
| | | | | | | | | | - Puyang Wang
- Alibaba DAMO Academy, 969 West Wen Yi Road, Hangzhou, Zhejiang, China
| | - Dazhou Guo
- Alibaba DAMO Academy USA, 860 Washington Street, 8F, NY, USA
| | - Dakai Jin
- Alibaba DAMO Academy USA, 860 Washington Street, 8F, NY, USA
| | - Ya'nan Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shuiqing Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Runsheng Chang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Boyu Zhang
- A.I R&D Center, Sanmed Biotech Inc., No. 266 Tongchang Road, Xiangzhou District, Zhuhai, Guangdong, China
| | - Xing Lu
- A.I R&D Center, Sanmed Biotech Inc., T220 Trade st. SanDiego, CA, USA
| | - Abdul Qayyum
- ENIB, UMR CNRS 6285 LabSTICC, Brest, 29238, France
| | - Moona Mazher
- Department of Computer Engineering and Mathematics, University Rovira I Virgili, Tarragona, Spain
| | - Qi Su
- Shanghai Jiao Tong University, Shanghai, China
| | - Yonghuang Wu
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Ying'ao Liu
- University of Science and Technology of China, Hefei, Anhui, China
| | | | - Jiancheng Yang
- Dianei Technology, Shanghai, China; EPFL, Lausanne, Switzerland
| | - Ashkan Pakzad
- Medical Physics and Biomedical Engineering Department, University College London, London, UK
| | - Bojidar Rangelov
- Center for Medical Image Computing, University College London, London, UK
| | | | | | - Jiayuan Sun
- Department of Respiratory and Critical Care Medicine, Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai, China.
| | - Guang-Zhong Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Yun Gu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China.
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Cui C, Yang H, Wang Y, Zhao S, Asad Z, Coburn LA, Wilson KT, Landman BA, Huo Y. Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review. PROGRESS IN BIOMEDICAL ENGINEERING (BRISTOL, ENGLAND) 2023; 5:10.1088/2516-1091/acc2fe. [PMID: 37360402 PMCID: PMC10288577 DOI: 10.1088/2516-1091/acc2fe] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions.
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Affiliation(s)
- Can Cui
- Department of Computer Science, Vanderbilt University, Nashville, TN 37235, United States of America
| | - Haichun Yang
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37215, United States of America
| | - Yaohong Wang
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37215, United States of America
| | - Shilin Zhao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37215, United States of America
| | - Zuhayr Asad
- Department of Computer Science, Vanderbilt University, Nashville, TN 37235, United States of America
| | - Lori A Coburn
- Division of Gastroenterology Hepatology, and Nutrition, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States of America
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN 37212, United States of America
| | - Keith T Wilson
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37215, United States of America
- Division of Gastroenterology Hepatology, and Nutrition, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States of America
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN 37212, United States of America
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN 37235, United States of America
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, United States of America
| | - Yuankai Huo
- Department of Computer Science, Vanderbilt University, Nashville, TN 37235, United States of America
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, United States of America
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Wang Y, Fan J, Tong Y, Wang L, Wang L, Weng C, Lai C, Song J, Zhang W. Bioinformatics analysis of ferroptosis-related gene AKR1C3 as a potential biomarker of asthma and its identification in BEAS-2B cells. Comput Biol Med 2023; 158:106740. [PMID: 36996663 DOI: 10.1016/j.compbiomed.2023.106740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/24/2023] [Accepted: 03/02/2023] [Indexed: 03/17/2023]
Abstract
Ferroptosis is a newly discovered type of cell death and has recently been shown to be associated with asthma. However, the relationship between them at the genetic level has not been elucidated via informatics analysis. In this study, bioinformatics analyses are conducted using asthma and ferroptosis datasets to identify candidate ferroptosis-related genes using the R software. Weighted gene co-expression network analysis is performed to identify co-expressed genes. Protein-protein interaction networks, the Kyoto encyclopedia of genes and genomes, and gene ontology enrichment analysis are used to identify the potential functions of the candidate genes. We experimentally validate the results of our analysis using small interfering RNAs and plasmids to silence and upregulate the expression of the candidate gene in human bronchial epithelial cells (BEAS-2B). The ferroptosis signature levels are examined. Bioinformatics analysis of the asthma dataset GDS4896 shows that the level of the aldo-keto reductase family 1 member C3 (AKR1C3) gene in the peripheral blood of patients with severe therapy-resistant asthma and controlled persistent mild asthma (MA) is significantly upregulated. The AUC values for asthma diagnosis and MA are 0.823 and 0.915, respectively. The diagnostic value of AKR1C3 is verified using the GSE64913 dataset. The gene module of AKR1C3 is evident in MA and functions through redox reactions and metabolic processes. Ferroptosis indicators are downregulated by the overexpression of AKR1C3 and upregulated by silencing AKR1C3. The ferroptosis-related gene AKR1C3 can be used as a diagnostic biomarker for asthma, particularly for MA, and regulates ferroptosis in BEAS-2B cells.
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Affiliation(s)
- Yufei Wang
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Junwen Fan
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Yu Tong
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Lei Wang
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Lingya Wang
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Cuiye Weng
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China; Department of Neonatology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Chuqiao Lai
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Jingjing Song
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China.
| | - Weixi Zhang
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China.
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Large-Kernel Attention for 3D Medical Image Segmentation. Cognit Comput 2023. [DOI: 10.1007/s12559-023-10126-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
AbstractAutomated segmentation of multiple organs and tumors from 3D medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep learning methods can aid in diagnosing and treating cancer. However, organs often overlap and are complexly connected, characterized by extensive anatomical variation and low contrast. In addition, the diversity of tumor shape, location, and appearance, coupled with the dominance of background voxels, makes accurate 3D medical image segmentation difficult. In this paper, a novel 3D large-kernel (LK) attention module is proposed to address these problems to achieve accurate multi-organ segmentation and tumor segmentation. The advantages of biologically inspired self-attention and convolution are combined in the proposed LK attention module, including local contextual information, long-range dependencies, and channel adaptation. The module also decomposes the LK convolution to optimize the computational cost and can be easily incorporated into CNNs such as U-Net. Comprehensive ablation experiments demonstrated the feasibility of convolutional decomposition and explored the most efficient and effective network design. Among them, the best Mid-type 3D LK attention-based U-Net network was evaluated on CT-ORG and BraTS 2020 datasets, achieving state-of-the-art segmentation performance when compared to avant-garde CNN and Transformer-based methods for medical image segmentation. The performance improvement due to the proposed 3D LK attention module was statistically validated.
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