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Yuan Y, Tan W, Xu L, Bao N, Zhu Q, Wang Z, Wang R. An end-to-end multi-scale airway segmentation framework based on pulmonary CT image. Phys Med Biol 2024. [PMID: 38657624 DOI: 10.1088/1361-6560/ad4300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
OBJECTIVE Automatic and accurate airway segmentation is necessary for lung disease diagnosis. The complex tree-like structures leads to gaps in the different generations of the airway tree, and thus airway segmentation is also considered to be a multi-scale problem. In recent years, convolutional neural networks have facilitated the development of medical image segmentation. In particular, 2D CNNs and 3D CNNs can extract different scale features. Hence, we propose a two-stage and 2D+3D framework for multi-scale airway tree segmentation. APPROACH In stage 1, we use a 2D Full Airway SegNet(2D FA-SegNet) to segment the complete airway tree. Multi-scale Atros Spatial Pyramid (MASP) and Atros Residual Skip connection (ARSc) modules are inserted to extract different scales feature. We designed a hard sample selection strategy to increase the proportion of intrapulmonary airway samples in stage 2. 3D Airway RefineNet (3D ARNet) as stage 2 takes the results of stage 1 as a priori information. Spatial information extracted by 3D convolutional kernel compensates for the loss of in 2D FA-SegNet. Furthermore, we added False Positive losses and False Negative losses to improve the segmentation performance of airway branches within the lungs. MAIN RESULTS We performed data enhancement on the publicly available dataset of ISICDM 2020 Challenge 3, and on which evaluated our method. Comprehensive experiments show that the proposed method has the highest DSC of 0.931, and IoU of 0.871 for the whole airway tree and DSC of 0.699, and IoU of 0.543 for the intrapulmonary bronchi tree. In addition, 3D ARNet proposed in this paper cascaded with other State-Of-The-Art methods to increase DLR by up to 46.33% and DBR by up to 42.97%. SIGNIFICANCE The quantitative and qualitative evaluation results show that our proposed method performs well in segmenting the airway at different scales.
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Affiliation(s)
- Ye Yuan
- College of Computer Science and Engineering, Northeastern University, No.11, Lane 3, Culture Road, Heping District, Shenyang, Liaoning Province, China, Shenyang, 110819, CHINA
| | - Wenjun Tan
- College of Computer Science and Engineering, Northeastern University, No.11, Lane 3, Culture Road, Heping District, Shenyang, Liaoning Province, China, Shenyang, Liaoning, 110819, CHINA
| | - Lisheng Xu
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, No.195, Innovation Road, Hunnan District, Shenyang, Liaoning, China, Shenyang, Liaoning, 110819, CHINA
| | - Nan Bao
- College of Medicine and Biological Information Engineering, Northeastern University, No.195, Innovation Road, Hunnan District, Shenyang, Liaoning, China, Shenyang, Liaoning, 110819, CHINA
| | - Quan Zhu
- The First Affiliated Hospital With Nanjing Medical University, No.300, Guangzhou Road, Nanjing, Jiangsu Province, Nanjing, Jiangsu, 210029, CHINA
| | - Zhe Wang
- Affiliated Zhongshan Hospital of Dalian University, No.6, Jiefang Street, Dalian, Liaoning Province, Dalian, Liaoning, 116001, CHINA
| | - Ruoyu Wang
- Affiliated Zhongshan Hospital of Dalian University, No.6, Jiefang Street, Dalian, Liaoning Province, Dalian, Liaoning, 116001, CHINA
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Yang M, Wang J, Quan S, Xu Q. High-precision bladder cancer diagnosis method: 2D Raman spectrum figures based on maintenance technology combined with automatic weighted feature fusion network. Anal Chim Acta 2023; 1282:341908. [PMID: 37923405 DOI: 10.1016/j.aca.2023.341908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/28/2023] [Accepted: 10/10/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Raman spectroscopy has been extensively utilized as a marker-free detection method in the complementary diagnosis of cancer. Multivariate statistical classification analysis is frequently employed for Raman spectral data classification. Nevertheless, traditional multivariate statistical classification analysis performs poorly when analyzing large samples and multicategory spectral data. In addition, with the advancement of computer vision, convolutional neural networks (CNNs) have demonstrated extraordinarily precise analysis of two-dimensional image processing. RESULT Combining 2D Raman spectrograms with automatic weighted feature fusion network (AWFFN) for bladder cancer detection is presented in this paper. Initially, the s-transform (ST) is implemented for the first time to convert 1D Raman data into 2D spectrograms, achieving 99.2% detection accuracy. Second, four upscaling techniques, including short time fourier transform (STFT), recurrence map (RP), markov transform field (MTF), and grammy angle field (GAF), were used to transform the 1D Raman spectral data into a variety of 2D Raman spectrograms. In addition, a particle swarm optimization (PSO) algorithm is combined with VGG19, ResNet50, and ResNet101 to construct a weighted feature fusion network, and this parallel network is employed for evaluating multiple spectrograms. Class activation mapping (CAM) is additionally employed to illustrate and evaluate the process of feature extraction via the three parallel network branches. The results demonstrate that the combination of a 2D Raman spectrogram along with a CNN for the diagnosis of bladder cancer obtains a 99.2% accuracy rate,which indicates that it is an extremely promising auxiliary technology for cancer diagnosis. SIGNIFICANCE The proposed two-dimensional Raman spectroscopy method has an improved precision than one-dimensional spectroscopic data, which presents a potential methodology for assisted cancer detection and providing crucial technical support for assisted diagnosis.
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Affiliation(s)
- Mengge Yang
- School of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Jiajia Wang
- School of Information Science and Engineering, Xinjiang University, Urumqi, China; The Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Xinjiang University, China; Post-doctoral Workstation of Xinjiang Uygur Autonomous Region Institute of Product Quality Supervision and Inspection, Urumqi, China.
| | - Siyu Quan
- School of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Qiqi Xu
- School of Information Science and Engineering, Xinjiang University, Urumqi, China
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Zhang X, Landsness EC, Chen W, Miao H, Tang M, Brier LM, Culver JP, Lee JM, Anastasio MA. Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning. J Neurosci Methods 2022; 366:109421. [PMID: 34822945 PMCID: PMC9006179 DOI: 10.1016/j.jneumeth.2021.109421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 11/09/2021] [Accepted: 11/13/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND Wide-field calcium imaging (WFCI) allows for monitoring of cortex-wide neural dynamics in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming and often suffers from low inter- and intra-rater reliability and invasiveness. Therefore, an automated sleep state classification method that operates on WFCI data alone is needed. NEW METHOD A hybrid, two-step method is proposed. In the first step, spatial-temporal WFCI data is mapped to multiplex visibility graphs (MVGs). Subsequently, a two-dimensional convolutional neural network (2D CNN) is employed on the MVGs to be classified as wakefulness, NREM and REM. RESULTS Sleep states were classified with an accuracy of 84% and Cohen's κ of 0.67. The method was also effectively applied on a binary classification of wakefulness/sleep (accuracy=0.82, κ = 0.62) and a four-class wakefulness/sleep/anesthesia/movement classification (accuracy=0.74, κ = 0.66). Gradient-weighted class activation maps revealed that the CNN focused on short- and long-term temporal connections of MVGs in a sleep state-specific manner. Sleep state classification performance when using individual brain regions was highest for the posterior area of the cortex and when cortex-wide activity was considered. COMPARISON WITH EXISTING METHOD On a 3-hour WFCI recording, the MVG-CNN achieved a κ of 0.65, comparable to a κ of 0.60 corresponding to the human EEG/EMG-based scoring. CONCLUSIONS The hybrid MVG-CNN method accurately classifies sleep states from WFCI data and will enable future sleep-focused studies with WFCI.
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Affiliation(s)
- Xiaohui Zhang
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Eric C Landsness
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Wei Chen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Hanyang Miao
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Michelle Tang
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Lindsey M Brier
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Joseph P Culver
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University School of Engineering, St. Louis, MO 63130, USA; Department of Electrical and Systems Engineering, Washington University School of Engineering, St. Louis, MO 63130, USA; Department of Physics, Washington University School of Arts and Science, St. Louis, MO 63130, USA
| | - Jin-Moo Lee
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University School of Engineering, St. Louis, MO 63130, USA
| | - Mark A Anastasio
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
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Noreen I, Hamid M, Akram U, Malik S, Saleem M. Hand Pose Recognition Using Parallel Multi Stream CNN. Sensors (Basel) 2021; 21:8469. [PMID: 34960562 PMCID: PMC8708730 DOI: 10.3390/s21248469] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/13/2021] [Accepted: 12/13/2021] [Indexed: 11/17/2022]
Abstract
Recently, several computer applications provided operating mode through pointing fingers, waving hands, and with body movement instead of a mouse, keyboard, audio, or touch input such as sign language recognition, robot control, games, appliances control, and smart surveillance. With the increase of hand-pose-based applications, new challenges in this domain have also emerged. Support vector machines and neural networks have been extensively used in this domain using conventional RGB data, which are not very effective for adequate performance. Recently, depth data have become popular due to better understating of posture attributes. In this study, a multiple parallel stream 2D CNN (two-dimensional convolution neural network) model is proposed to recognize the hand postures. The proposed model comprises multiple steps and layers to detect hand poses from image maps obtained from depth data. The hyper parameters of the proposed model are tuned through experimental analysis. Three publicly available benchmark datasets: Kaggle, First Person, and Dexter, are used independently to train and test the proposed approach. The accuracy of the proposed method is 99.99%, 99.48%, and 98% using the Kaggle hand posture dataset, First Person hand posture dataset, and Dexter dataset, respectively. Further, the results obtained for F1 and AUC scores are also near-optimal. Comparative analysis with state-of-the-art shows that the proposed model outperforms the previous methods.
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Affiliation(s)
- Iram Noreen
- Department of Computer Science, Lahore Campus, Bahria University, Islamabad 54000, Pakistan;
| | - Muhammad Hamid
- Department of Statistics and Computer Science, University of Veterinary and Animal Sciences (UVAS), Lahore 54000, Pakistan;
| | - Uzma Akram
- Department of Computer Science, Lahore Campus, Bahria University, Islamabad 54000, Pakistan;
| | - Saadia Malik
- Department of Information Systems, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Muhammad Saleem
- Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
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Ullah A, Rehman SU, Tu S, Mehmood RM, Fawad, Ehatisham-Ul-Haq M. A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal. Sensors (Basel) 2021; 21:951. [PMID: 33535397 DOI: 10.3390/s21030951] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 01/09/2021] [Accepted: 01/15/2021] [Indexed: 11/21/2022]
Abstract
Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness.
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Xing X, Liang G, Blanton H, Rafique MU, Wang C, Lin AL, Jacobs N. Dynamic Image for 3D MRI Image Alzheimer's Disease Classification. Comput Vis ECCV 2020; 12535:355-364. [PMID: 37283785 PMCID: PMC10243959 DOI: 10.1007/978-3-030-66415-2_23] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN. We show our proposed CNN model achieves 9.5% better Alzheimer's disease classification accuracy than the baseline 3D models. We also show that our method allows for efficient training, requiring only 20% of the training time compared to 3D CNN models. The code is available online: https://github.com/UkyVision/alzheimer-project.
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Affiliation(s)
- Xin Xing
- University of Kentucky, Lexington KY 40506, USA
| | | | | | | | - Chris Wang
- University of Kentucky, Lexington KY 40506, USA
| | - Ai-Ling Lin
- University of Kentucky, Lexington KY 40506, USA
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