1
|
Xu J, Zhou F, Shen J, Yan Z, Wan C, Yao J. Automatic height measurement of central serous chorioretinopathy lesion using a deep learning and adaptive gradient threshold based cascading strategy. Comput Biol Med 2024; 177:108610. [PMID: 38820776 DOI: 10.1016/j.compbiomed.2024.108610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/23/2024] [Accepted: 05/11/2024] [Indexed: 06/02/2024]
Abstract
Accurately quantifying the height of central serous chorioretinopathy (CSCR) lesion is of great significance for assisting ophthalmologists in diagnosing CSCR and evaluating treatment efficacy. The manual measurement results dominated by single optical coherence tomography (OCT) B-scan image in clinical practice face the dilemma of weak reference, poor reproducibility, and experience dependence. In this context, this paper constructs two schemes: Scheme Ⅰ draws on the idea of ensemble learning, namely, integrating multiple models for locating starting key point in the height direction of lesion in the inference stage, which appropriately improves the performance of a single model. Scheme Ⅱ designs an adaptive gradient threshold (AGT) technique, followed by the construction of cascading strategy, which involves preliminary location of starting key point through deep learning, and then employs AGT for precise adjustment. This strategy not only achieves effective location for starting key point, but also significantly reduces the large appetite of deep learning model for training samples. Subsequently, AGT continues to play a crucial role in locating the terminal key point in the height direction of lesion, further demonstrating its feasibility and effectiveness. Quantitative and qualitative key point location experiments in the height direction of lesion on 1152 samples, as well as the final height measurement display, consistently conveys the superiority of the constructed schemes, especially the cascading strategy, expanding another potential tool for the comprehensive analysis of CSCR.
Collapse
Affiliation(s)
- Jianguo Xu
- College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics &Astronautics, 210016, Nanjing, PR China.
| | - Fen Zhou
- The Affiliated Eye Hospital of Nanjing Medical University, 210029, Nanjing, PR China
| | - Jianxin Shen
- College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics &Astronautics, 210016, Nanjing, PR China
| | - Zhipeng Yan
- The Affiliated Eye Hospital of Nanjing Medical University, 210029, Nanjing, PR China
| | - Cheng Wan
- College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, PR China
| | - Jin Yao
- The Affiliated Eye Hospital of Nanjing Medical University, 210029, Nanjing, PR China.
| |
Collapse
|
2
|
Li B, Xu Y, Wang Y, Zhang B. DECTNet: Dual Encoder Network combined convolution and Transformer architecture for medical image segmentation. PLoS One 2024; 19:e0301019. [PMID: 38573957 PMCID: PMC10994332 DOI: 10.1371/journal.pone.0301019] [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: 10/27/2023] [Accepted: 03/09/2024] [Indexed: 04/06/2024] Open
Abstract
Automatic and accurate segmentation of medical images plays an essential role in disease diagnosis and treatment planning. Convolution neural networks have achieved remarkable results in medical image segmentation in the past decade. Meanwhile, deep learning models based on Transformer architecture also succeeded tremendously in this domain. However, due to the ambiguity of the medical image boundary and the high complexity of physical organization structures, implementing effective structure extraction and accurate segmentation remains a problem requiring a solution. In this paper, we propose a novel Dual Encoder Network named DECTNet to alleviate this problem. Specifically, the DECTNet embraces four components, which are a convolution-based encoder, a Transformer-based encoder, a feature fusion decoder, and a deep supervision module. The convolutional structure encoder can extract fine spatial contextual details in images. Meanwhile, the Transformer structure encoder is designed using a hierarchical Swin Transformer architecture to model global contextual information. The novel feature fusion decoder integrates the multi-scale representation from two encoders and selects features that focus on segmentation tasks by channel attention mechanism. Further, a deep supervision module is used to accelerate the convergence of the proposed method. Extensive experiments demonstrate that, compared to the other seven models, the proposed method achieves state-of-the-art results on four segmentation tasks: skin lesion segmentation, polyp segmentation, Covid-19 lesion segmentation, and MRI cardiac segmentation.
Collapse
Affiliation(s)
- Boliang Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Yaming Xu
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Yan Wang
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Bo Zhang
- Sergeant Schools of Army Academy of Armored Forces, Changchun, Jilin, China
| |
Collapse
|
3
|
Murmu A, Kumar P. GIFNet: an effective global infection feature network for automatic COVID-19 lung lesions segmentation. Med Biol Eng Comput 2024:10.1007/s11517-024-03024-z. [PMID: 38308670 DOI: 10.1007/s11517-024-03024-z] [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: 08/09/2023] [Accepted: 01/11/2024] [Indexed: 02/05/2024]
Abstract
The ongoing COronaVIrus Disease 2019 (COVID-19) pandemic carried by the SARS-CoV-2 virus spread worldwide in early 2019, bringing about an existential health catastrophe. Automatic segmentation of infected lungs from COVID-19 X-ray and computer tomography (CT) images helps to generate a quantitative approach for treatment and diagnosis. The multi-class information about the infected lung is often obtained from the patient's CT dataset. However, the main challenge is the extensive range of infected features and lack of contrast between infected and normal areas. To resolve these issues, a novel Global Infection Feature Network (GIFNet)-based Unet with ResNet50 model is proposed for segmenting the locations of COVID-19 lung infections. The Unet layers have been used to extract the features from input images and select the region of interest (ROI) by using the ResNet50 technique for training it faster. Moreover, integrating the pooling layer into the atrous spatial pyramid pooling (ASPP) mechanism in the bottleneck helps for better feature selection and handles scale variation during training. Furthermore, the partial differential equation (PDE) approach is used to enhance the image quality and intensity value for particular ROI boundary edges in the COVID-19 images. The proposed scheme has been validated on two datasets, namely the SARS-CoV-2 CT scan and COVIDx-19, for detecting infected lung segmentation (ILS). The experimental findings have been subjected to a comprehensive analysis using various evaluation metrics, including accuracy (ACC), area under curve (AUC), recall (REC), specificity (SPE), dice similarity coefficient (DSC), mean absolute error (MAE), precision (PRE), and mean squared error (MSE) to ensure rigorous validation. The results demonstrate the superior performance of the proposed system compared to the state-of-the-art (SOTA) segmentation models on both X-ray and CT datasets.
Collapse
Affiliation(s)
- Anita Murmu
- Computer Science and Engineering Department, National Institute of Technology Patna, Ashok Rajpath, Patna, Bihar, 800005, India.
| | - Piyush Kumar
- Computer Science and Engineering Department, National Institute of Technology Patna, Ashok Rajpath, Patna, Bihar, 800005, India
| |
Collapse
|
4
|
Jiao R, Zhang Y, Ding L, Xue B, Zhang J, Cai R, Jin C. Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation. Comput Biol Med 2024; 169:107840. [PMID: 38157773 DOI: 10.1016/j.compbiomed.2023.107840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 10/30/2023] [Accepted: 12/07/2023] [Indexed: 01/03/2024]
Abstract
Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain, especially in the medical imaging domain where only experts can provide reliable and accurate annotations. Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarize both the technical novelties and empirical results. Furthermore, we analyze and discuss the limitations and several unsolved problems of existing approaches. We hope this review can inspire the research community to explore solutions to this challenge and further advance the field of medical image segmentation.
Collapse
Affiliation(s)
- Rushi Jiao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; School of Engineering Medicine, Beihang University, Beijing, 100191, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
| | - Yichi Zhang
- School of Data Science, Fudan University, Shanghai, 200433, China; Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, 200433, China.
| | - Le Ding
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
| | - Bingsen Xue
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
| | - Jicong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China; Hefei Innovation Research Institute, Beihang University, Hefei, 230012, China.
| | - Rong Cai
- School of Engineering Medicine, Beihang University, Beijing, 100191, China; Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, 100191, China.
| | - Cheng Jin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China; Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
| |
Collapse
|
5
|
SuperMini-seg: An ultra lightweight network for COVID-19 lung infection segmentation from CT images. Biomed Signal Process Control 2023; 85:104896. [PMID: 36998783 PMCID: PMC10028361 DOI: 10.1016/j.bspc.2023.104896] [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: 09/13/2022] [Revised: 01/31/2023] [Accepted: 03/18/2023] [Indexed: 03/24/2023]
Abstract
The automatic segmentation of lung lesions from COVID-19 computed tomography (CT) images is helpful in establishing a quantitative model to diagnose and treat COVID-19. To this end, this study proposes a lightweight segmentation network called the SuperMini-Seg. We propose a new module called the transformer parallel convolution module (TPCB), which introduces both transformer and convolution operations in one module. SuperMini-seg adopts the structure of a double-branch parallel to downsample the image and designs a gated attention mechanism in the middle of the two parallel branches. At the same time, the attentive hierarchical spatial pyramid (AHSP) module and criss-cross attention module are adopted, and more than 100K parameters are present in the model. At the same time, the model is scalable, and the parameter quantity of SuperMini-seg-V2 reaches more than 70K. Compared with other advanced methods, the segmentation accuracy was almost reached the state-of-art method. The calculation efficiency was high, which is convenient for practical deployment.
Collapse
|
6
|
da Silveira TLT, Pinto PGL, Lermen TS, Jung CR. Omnidirectional 2.5D representation for COVID-19 diagnosis using chest CTs. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2023; 91:103775. [PMID: 36741546 PMCID: PMC9886432 DOI: 10.1016/j.jvcir.2023.103775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 01/18/2023] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
The Coronavirus Disease 2019 (COVID-19) has drastically overwhelmed most countries in the last two years, and image-based approaches using computerized tomography (CT) have been used to identify pulmonary infections. Recent methods based on deep learning either require time-consuming per-slice annotations (2D) or are highly data- and hardware-demanding (3D). This work proposes a novel omnidirectional 2.5D representation of volumetric chest CTs that allows exploring efficient 2D deep learning architectures while requiring volume-level annotations only. Our learning approach uses a siamese feature extraction backbone applied to each lung. It combines these features into a classification head that explores a novel combination of Squeeze-and-Excite strategies with Class Activation Maps. We experimented with public and in-house datasets and compared our results with state-of-the-art techniques. Our analyses show that our method provides better or comparable prediction quality and accurately distinguishes COVID-19 infections from other kinds of pneumonia and healthy lungs.
Collapse
Affiliation(s)
- Thiago L T da Silveira
- Institute of Informatics - Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Paulo G L Pinto
- Institute of Informatics - Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Thiago S Lermen
- Institute of Informatics - Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Cláudio R Jung
- Institute of Informatics - Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| |
Collapse
|
7
|
Xu J, Shen J, Jiang Q, Wan C, Zhou F, Zhang S, Yan Z, Yang W. A multi-modal fundus image based auxiliary location method of lesion boundary for guiding the layout of laser spot in central serous chorioretinopathy therapy. Comput Biol Med 2023; 155:106648. [PMID: 36805213 DOI: 10.1016/j.compbiomed.2023.106648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 01/14/2023] [Accepted: 02/07/2023] [Indexed: 02/15/2023]
Abstract
The lesion boundary of central serous chorioretinopathy (CSCR) is the guarantee to guide the ophthalmologist to accurately arrange the laser spots, so as to enable this ophthalmopathy to be treated precisely. Currently, the accuracy and rapidity of manually locating CSCR lesion boundary in clinic based on single-modal fundus image are limited by imaging quality and ophthalmologist experience, which is also accompanied by poor repeatability, weak reliability and low efficiency. Consequently, a multi-modal fundus image-based lesion boundary auxiliary location method is developed. Firstly, the initial location module (ILM) is employed to achieve the preliminary location of key boundary points of CSCR lesion area on the optical coherence tomography (OCT) B-scan image, then followed by the joint location module (JLM) created based on reinforcement learning for further enhancing the location accuracy. Secondly, the scanning line detection module (SLDM) is constructed to realize the location of lesion scanning line on the scanning laser ophthalmoscope (SLO) image, so as to facilitate the cross-modal mapping of key boundary points. Finally, a simple yet effective lesion boundary location module (LBLM) is designed to assist the automatic cross-modal mapping of key boundary points and enable the final location of lesion boundary. Extensive experiments show that each module can perform well on its corresponding sub task, such as JLM, which makes the correction rate (CR) of ILM increase to 92.11%, comprehensively indicating the effectiveness and feasibility of this method in providing effective lesion boundary guidance for assisting ophthalmologists to precisely arrange the laser spots, and also opening a new research idea for the automatic location of lesion boundary of other fundus diseases.
Collapse
Affiliation(s)
- Jianguo Xu
- College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, PR China
| | - Jianxin Shen
- College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, PR China.
| | - Qin Jiang
- The Affiliated Eye Hospital of Nanjing Medical University, 210029, Nanjing, PR China
| | - Cheng Wan
- College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, PR China
| | - Fen Zhou
- The Affiliated Eye Hospital of Nanjing Medical University, 210029, Nanjing, PR China
| | - Shaochong Zhang
- Shenzhen Eye Hospital, Jinan University, 518040, Shenzhen, PR China
| | - Zhipeng Yan
- The Affiliated Eye Hospital of Nanjing Medical University, 210029, Nanjing, PR China
| | - Weihua Yang
- Shenzhen Eye Hospital, Jinan University, 518040, Shenzhen, PR China.
| |
Collapse
|
8
|
Rao Y, Lv Q, Zeng S, Yi Y, Huang C, Gao Y, Cheng Z, Sun J. COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold. Biomed Signal Process Control 2023; 81:104486. [PMID: 36505089 PMCID: PMC9721288 DOI: 10.1016/j.bspc.2022.104486] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/23/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
The ground glass opacity (GGO) of the lung is one of the essential features of COVID-19. The GGO in computed tomography (CT) images has various features and low-intensity contrast between the GGO and edge structures. These problems pose significant challenges for segmenting the GGO. To tackle these problems, we propose a new threshold method for accurate segmentation of GGO. Specifically, we offer a framework for adjusting the threshold parameters according to the image contrast. Three functions include Attention mechanism threshold, Contour equalization, and Lung segmentation (ACL). The lung is divided into three areas using the attention mechanism threshold. Further, the segmentation parameters of the attention mechanism thresholds of the three parts are adaptively adjusted according to the image contrast. Only the segmentation regions restricted by the lung segmentation results are retained. Extensive experiments on four COVID datasets show that ACL can segment GGO images at low contrast well. Compared with the state-of-the-art methods, the similarity Dice of the ACL segmentation results is improved by 8.9%, the average symmetry surface distance ASD is reduced by 23%, and the required computational power F L O P s are only 0.09% of those of deep learning models. For GGO segmentation, ACL is more lightweight, and the accuracy is higher. Code will be released at https://github.com/Lqs-github/ACL.
Collapse
Affiliation(s)
- Yunbo Rao
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Qingsong Lv
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Shaoning Zeng
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313000, China
| | - Yuling Yi
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Cheng Huang
- Fifth Clinical College of Chongqing Medical University, Chongqing, 402177, China
| | - Yun Gao
- Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Zhanglin Cheng
- Advanced Technology Chinese Academy of Sciences, Shenzhen, 610042, China
| | - Jihong Sun
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310014, China
| |
Collapse
|
9
|
Uncertainty-guided mutual consistency learning for semi-supervised medical image segmentation. Artif Intell Med 2022; 138:102476. [PMID: 36990583 DOI: 10.1016/j.artmed.2022.102476] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 11/20/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Medical image segmentation is a fundamental and critical step in many clinical approaches. Semi-supervised learning has been widely applied to medical image segmentation tasks since it alleviates the heavy burden of acquiring expert-examined annotations and takes the advantage of unlabeled data which is much easier to acquire. Although consistency learning has been proven to be an effective approach by enforcing an invariance of predictions under different distributions, existing approaches cannot make full use of region-level shape constraint and boundary-level distance information from unlabeled data. In this paper, we propose a novel uncertainty-guided mutual consistency learning framework to effectively exploit unlabeled data by integrating intra-task consistency learning from up-to-date predictions for self-ensembling and cross-task consistency learning from task-level regularization to exploit geometric shape information. The framework is guided by the estimated segmentation uncertainty of models to select out relatively certain predictions for consistency learning, so as to effectively exploit more reliable information from unlabeled data. Experiments on two publicly available benchmark datasets showed that: (1) Our proposed method can achieve significant performance improvement by leveraging unlabeled data, with up to 4.13% and 9.82% in Dice coefficient compared to supervised baseline on left atrium segmentation and brain tumor segmentation, respectively. (2) Compared with other semi-supervised segmentation methods, our proposed method achieve better segmentation performance under the same backbone network and task settings on both datasets, demonstrating the effectiveness and robustness of our method and potential transferability for other medical image segmentation tasks.
Collapse
|
10
|
Kumar S, Nagar R, Bhatnagar S, Vaddi R, Gupta SK, Rashid M, Bashir AK, Alkhalifah T. Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19. COMPUTERS & ELECTRICAL ENGINEERING : AN INTERNATIONAL JOURNAL 2022; 103:108391. [PMID: 36119394 PMCID: PMC9472671 DOI: 10.1016/j.compeleceng.2022.108391] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 05/27/2023]
Abstract
All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models. The proposed framework performs early classification of patients based on collected samples of chest X-ray images and Coswara cough (sound) samples of possibly infected people. The captured cough samples are pre-processed using speech signal processing techniques and Mel frequency cepstral coefficient features are extracted using deep convolutional neural networks. Finally, the proposed system fuses extracted features to provide 98.70% and 82.7% based on Chest-X ray images and cough (audio) samples for early diagnosis using the weighted sum-rule fusion method.
Collapse
Affiliation(s)
- Santosh Kumar
- Department of Computer Science and Engineering, International Institute of Information Technology, Naya Raipur, Raipur, Chhattisgarh, 493661, India
| | - Rishab Nagar
- Department of Computer Science and Engineering, International Institute of Information Technology, Naya Raipur, Raipur, Chhattisgarh, 493661, India
| | - Saumya Bhatnagar
- Department of Computer Science and Engineering, International Institute of Information Technology, Naya Raipur, Raipur, Chhattisgarh, 493661, India
| | - Ramesh Vaddi
- Department of Electronics and Communication Engineering, School of Engineering and Applied Sciences, SRM University, Amaravati, Guntur, Andhra Pradesh, 522240, India
| | - Sachin Kumar Gupta
- School of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Katra, India
| | - Mamoon Rashid
- Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, India
- Vishwakarma University Research Center of Excellence for Health Informatics, Pune, India
| | - Ali Kashif Bashir
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK
| | - Tamim Alkhalifah
- Department of computer science, College of Science and Arts in Ar Rass, Qassim University, Saudi Arabia
| |
Collapse
|
11
|
Chen Y, Zhou T, Chen Y, Feng L, Zheng C, Liu L, Hu L, Pan B. HADCNet: Automatic segmentation of COVID-19 infection based on a hybrid attention dense connected network with dilated convolution. Comput Biol Med 2022; 149:105981. [PMID: 36029749 PMCID: PMC9391231 DOI: 10.1016/j.compbiomed.2022.105981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 08/03/2022] [Accepted: 08/14/2022] [Indexed: 12/01/2022]
Abstract
the automatic segmentation of lung infections in CT slices provides a rapid and effective strategy for diagnosing, treating, and assessing COVID-19 cases. However, the segmentation of the infected areas presents several difficulties, including high intraclass variability and interclass similarity among infected areas, as well as blurred edges and low contrast. Therefore, we propose HADCNet, a deep learning framework that segments lung infections based on a dual hybrid attention strategy. HADCNet uses an encoder hybrid attention module to integrate feature information at different scales across the peer hierarchy to refine the feature map. Furthermore, a decoder hybrid attention module uses an improved skip connection to embed the semantic information of higher-level features into lower-level features by integrating multi-scale contextual structures and assigning the spatial information of lower-level features to higher-level features, thereby capturing the contextual dependencies of lesion features across levels and refining the semantic structure, which reduces the semantic gap between feature maps at different levels and improves the model segmentation performance. We conducted fivefold cross-validations of our model on four publicly available datasets, with final mean Dice scores of 0.792, 0.796, 0.785, and 0.723. These results show that the proposed model outperforms popular state-of-the-art semantic segmentation methods and indicate its potential use in the diagnosis and treatment of COVID-19.
Collapse
Affiliation(s)
- Ying Chen
- School of Software, Nanchang Hangkong University, Nanchang, 330063, PR China.
| | - Taohui Zhou
- School of Software, Nanchang Hangkong University, Nanchang, 330063, PR China.
| | - Yi Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, PR China.
| | - Longfeng Feng
- School of Software, Nanchang Hangkong University, Nanchang, 330063, PR China.
| | - Cheng Zheng
- School of Software, Nanchang Hangkong University, Nanchang, 330063, PR China.
| | - Lan Liu
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, 330029, PR China.
| | - Liping Hu
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, 330029, PR China.
| | - Bujian Pan
- Department of Hepatobiliary Surgery, Wenzhou Central Hospital, The Dingli Clinical Institute of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, PR China.
| |
Collapse
|
12
|
Ren Z, Chang Y, Bartl-Pokorny KD, Pokorny FB, Schuller BW. The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection. J Voice 2022:S0892-1997(22)00166-7. [PMID: 35835648 PMCID: PMC9197794 DOI: 10.1016/j.jvoice.2022.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/25/2022] [Accepted: 06/09/2022] [Indexed: 12/05/2022]
Abstract
OBJECTIVES The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19's transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge of the acoustic characteristics of COVID-19 cough sounds is limited but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds. METHODS By applying conventional inferential statistics, we analyze the acoustic correlates of COVID-19 cough sounds based on the ComParE feature set, i.e., a standardized set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions. RESULTS The experimental results demonstrate that a set of acoustic parameters of cough sounds, e.g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, bear essential acoustic information in terms of effect sizes for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our general automatic COVID-19 detection model performs significantly above chance level, i.e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positive/negative: 210/1,201). CONCLUSIONS Based on the acoustic correlates analysis on the ComParE feature set and the feature analysis in the effective COVID-19 detection approach, we find that several acoustic features that show higher effects in conventional group difference testing are also higher weighted in the machine learning models.
Collapse
Affiliation(s)
- Zhao Ren
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany; L3S Research Center, Hannover, Germany.
| | - Yi Chang
- GLAM - Group on Language, Audio, & Music, Imperial College London, London, United Kingdom
| | - Katrin D Bartl-Pokorny
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany; Division of Phoniatrics, Medical University of Graz, Graz, Austria; Division of Physiology, Medical University of Graz, Graz, Austria.
| | - Florian B Pokorny
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany; Division of Phoniatrics, Medical University of Graz, Graz, Austria; Division of Physiology, Medical University of Graz, Graz, Austria
| | - Björn W Schuller
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany; GLAM - Group on Language, Audio, & Music, Imperial College London, London, United Kingdom
| |
Collapse
|