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Iytha Sridhar R, Kamaleswaran R. Lung segment anything model (LuSAM): a decoupled prompt-integrated framework for automated lung segmentation on chest x-Ray images. Biomed Phys Eng Express 2024; 10:055002. [PMID: 38781939 DOI: 10.1088/2057-1976/ad4f8f] [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: 02/22/2024] [Accepted: 05/23/2024] [Indexed: 05/25/2024]
Abstract
Accurate lung segmentation in chest x-ray images plays a pivotal role in early disease detection and clinical decision-making. In this study, we introduce an innovative approach to enhance the precision of lung segmentation using the Segment Anything Model (SAM). Despite its versatility, SAM faces the challenge of prompt decoupling, often resulting in misclassifications, especially with intricate structures like the clavicle. Our research focuses on the integration of spatial attention mechanisms within SAM. This approach enables the model to concentrate specifically on the lung region, fostering adaptability to image variations and reducing the likelihood of false positives. This work has the potential to significantly advance lung segmentation, improving the identification and quantification of lung anomalies across diverse clinical contexts.
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Affiliation(s)
- Rishika Iytha Sridhar
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Rishikesan Kamaleswaran
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
- Division of Trauma, Acute, and Critical Care Surgery, Department of Surgery, Duke University School of Medicine, Durham, NC, United States of America
- Division of Cardiothoracic Anesthesiology, Department of Anesthesiology, Duke University School of Medicine, Durham, NC, United States of America
- Division of Critical Care Medicine, Department of Anesthesiology, Duke University School of Medicine, Durham, NC, United States of America
- Department of Biomedical Engineering, Duke University Pratt School of Engineering, Durham, NC, United States of America
- Department of Electrical and Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, United States of America
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Prodanovic T, Petrovic Savic S, Prodanovic N, Simovic A, Zivojinovic S, Djordjevic JC, Savic D. Advanced Diagnostics of Respiratory Distress Syndrome in Premature Infants Treated with Surfactant and Budesonide through Computer-Assisted Chest X-ray Analysis. Diagnostics (Basel) 2024; 14:214. [PMID: 38275461 PMCID: PMC10814713 DOI: 10.3390/diagnostics14020214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/28/2023] [Accepted: 01/05/2024] [Indexed: 01/27/2024] Open
Abstract
This research addresses the respiratory distress syndrome (RDS) in preterm newborns caused by insufficient surfactant synthesis, which can lead to serious complications, including pneumothorax, pulmonary hypertension, and pulmonary hemorrhage, increasing the risk of a fatal outcome. By analyzing chest radiographs and blood gases, we specifically focus on the significant contributions of these parameters to the diagnosis and analysis of the recovery of patients with RDS. The study involved 32 preterm newborns, and the analysis of gas parameters before and after the administration of surfactants and inhalation corticosteroid therapy revealed statistically significant changes in values of parameters such as FiO2, pH, pCO2, HCO3, and BE (Sig. < 0.05), while the pO2 parameter showed a potential change (Sig. = 0.061). Parallel to this, the research emphasizes the development of a lung segmentation algorithm implemented in the MATLAB programming environment. The key steps of the algorithm include preprocessing, segmentation, and visualization for a more detailed understanding of the recovery dynamics after RDS. These algorithms have achieved promising results, with a global accuracy of 0.93 ± 0.06, precision of 0.81 ± 0.16, and an F-score of 0.82 ± 0.14. These results highlight the potential application of algorithms in the analysis and monitoring of recovery in newborns with RDS, also underscoring the need for further development of software solutions in medicine, particularly in neonatology, to enhance the diagnosis and treatment of preterm newborns with respiratory distress syndrome.
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Affiliation(s)
- Tijana Prodanovic
- Department of Pediatrics, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, 34000 Kragujevac, Serbia; (T.P.); (A.S.); (S.Z.); (J.C.D.); (D.S.)
- Center for Neonatology, Pediatric Clinic, University Clinical Center Kragujevac, Zmaj Jovina 30, 34000 Kragujevac, Serbia
| | - Suzana Petrovic Savic
- Department for Production Engineering, Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia;
| | - Nikola Prodanovic
- Department of Surgery, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, 34000 Kragujevac, Serbia
- Clinic for Orthopaedic and Trauma Surgery, University Clinical Center Kragujevac, Zmaj Jovina 30, 34000 Kragujevac, Serbia
| | - Aleksandra Simovic
- Department of Pediatrics, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, 34000 Kragujevac, Serbia; (T.P.); (A.S.); (S.Z.); (J.C.D.); (D.S.)
- Center for Neonatology, Pediatric Clinic, University Clinical Center Kragujevac, Zmaj Jovina 30, 34000 Kragujevac, Serbia
| | - Suzana Zivojinovic
- Department of Pediatrics, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, 34000 Kragujevac, Serbia; (T.P.); (A.S.); (S.Z.); (J.C.D.); (D.S.)
- Center for Neonatology, Pediatric Clinic, University Clinical Center Kragujevac, Zmaj Jovina 30, 34000 Kragujevac, Serbia
| | - Jelena Cekovic Djordjevic
- Department of Pediatrics, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, 34000 Kragujevac, Serbia; (T.P.); (A.S.); (S.Z.); (J.C.D.); (D.S.)
- Center for Neonatology, Pediatric Clinic, University Clinical Center Kragujevac, Zmaj Jovina 30, 34000 Kragujevac, Serbia
| | - Dragana Savic
- Department of Pediatrics, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, 34000 Kragujevac, Serbia; (T.P.); (A.S.); (S.Z.); (J.C.D.); (D.S.)
- Center for Neonatology, Pediatric Clinic, University Clinical Center Kragujevac, Zmaj Jovina 30, 34000 Kragujevac, Serbia
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Gay SS, Kisling KD, Anderson BM, Zhang L, Rhee DJ, Nguyen C, Netherton T, Yang J, Brock K, Jhingran A, Simonds H, Klopp A, Beadle BM, Court LE, Cardenas CE. Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy. J Appl Clin Med Phys 2023; 24:e14131. [PMID: 37670488 PMCID: PMC10691634 DOI: 10.1002/acm2.14131] [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: 04/16/2023] [Revised: 07/08/2023] [Accepted: 08/07/2023] [Indexed: 09/07/2023] Open
Abstract
PURPOSE Two-dimensional radiotherapy is often used to treat cervical cancer in low- and middle-income countries, but treatment planning can be challenging and time-consuming. Neural networks offer the potential to greatly decrease planning time through automation, but the impact of the wide range of hyperparameters to be set during training on model accuracy has not been exhaustively investigated. In the current study, we evaluated the effect of several convolutional neural network architectures and hyperparameters on 2D radiotherapy treatment field delineation. METHODS Six commonly used deep learning architectures were trained to delineate four-field box apertures on digitally reconstructed radiographs for cervical cancer radiotherapy. A comprehensive search of optimal hyperparameters for all models was conducted by varying the initial learning rate, image normalization methods, and (when appropriate) convolutional kernel size, the number of learnable parameters via network depth and the number of feature maps per convolution, and nonlinear activation functions. This yielded over 1700 unique models, which were all trained until performance converged and then tested on a separate dataset. RESULTS Of all hyperparameters, the choice of initial learning rate was most consistently significant for improved performance on the test set, with all top-performing models using learning rates of 0.0001. The optimal image normalization was not consistent across architectures. High overlap (mean Dice similarity coefficient = 0.98) and surface distance agreement (mean surface distance < 2 mm) were achieved between the treatment field apertures for all architectures using the identified best hyperparameters. Overlap Dice similarity coefficient (DSC) and distance metrics (mean surface distance and Hausdorff distance) indicated that DeepLabv3+ and D-LinkNet architectures were least sensitive to initial hyperparameter selection. CONCLUSION DeepLabv3+ and D-LinkNet are most robust to initial hyperparameter selection. Learning rate, nonlinear activation function, and kernel size are also important hyperparameters for improving performance.
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Affiliation(s)
- Skylar S. Gay
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | | | | | - Lifei Zhang
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Dong Joo Rhee
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Callistus Nguyen
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Tucker Netherton
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Jinzhong Yang
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Kristy Brock
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- Department of Imaging PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Anuja Jhingran
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Hannah Simonds
- University Hospitals Plymouth NHS TrustPlymouthUnited Kingdom
| | - Ann Klopp
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Beth M. Beadle
- Department of Radiation OncologyStanford UniversityPalo AltoCaliforniaUSA
| | - Laurence E. Court
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Carlos E. Cardenas
- Department of Radiation OncologyThe University of Alabama at BirminghamBirminghamAlabamaUSA
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Khorshidi A. Tumor segmentation via enhanced area growth algorithm for lung CT images. BMC Med Imaging 2023; 23:189. [PMID: 37986046 PMCID: PMC10662793 DOI: 10.1186/s12880-023-01126-y] [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: 11/26/2022] [Accepted: 10/16/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND Since lung tumors are in dynamic conditions, the study of tumor growth and its changes is of great importance in primary diagnosis. METHODS Enhanced area growth (EAG) algorithm is introduced to segment the lung tumor in 2D and 3D modes on 60 patients CT images from four different databases by MATLAB software. The contrast augmentation, color intensity and maximum primary tumor radius determination, thresholding, start and neighbor points' designation in an array, and then modifying the points in the braid on average are the early steps of the proposed algorithm. To determine the new tumor boundaries, the maximum distance from the color-intensity center point of the primary tumor to the modified points is appointed via considering a larger target region and new threshold. The tumor center is divided into different subsections and then all previous stages are repeated from new designated points to define diverse boundaries for the tumor. An interpolation between these boundaries creates a new tumor boundary. The intersections with the tumor boundaries are firmed for edge correction phase, after drawing diverse lines from the tumor center at relevant angles. Each of the new regions is annexed to the core region to achieve a segmented tumor surface by meeting certain conditions. RESULTS The multipoint-growth-starting-point grouping fashioned a desired consequence in the precise delineation of the tumor. The proposed algorithm enhanced tumor identification by more than 16% with a reasonable accuracy acceptance rate. At the same time, it largely assurances the independence of the last outcome from the starting point. By significance difference of p < 0.05, the dice coefficients were 0.80 ± 0.02 and 0.92 ± 0.03, respectively, for primary and enhanced algorithms. Lung area determination alongside automatic thresholding and also starting from several points along with edge improvement may reduce human errors in radiologists' interpretation of tumor areas and selection of the algorithm's starting point. CONCLUSIONS The proposed algorithm enhanced tumor detection by more than 18% with a sufficient acceptance ratio of accuracy. Since the enhanced algorithm is independent of matrix size and image thickness, it is very likely that it can be easily applied to other contiguous tumor images. TRIAL REGISTRATION PAZHOUHAN, PAZHOUHAN98000032. Registered 4 January 2021, http://pazhouhan.gerums.ac.ir/webreclist/view.action?webreclist_code=19300.
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Affiliation(s)
- Abdollah Khorshidi
- School of Paramedical, Gerash University of Medical Sciences, P.O. Box: 7441758666, Gerash, Iran.
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Chen L, Yu Z, Huang J, Shu L, Kuosmanen P, Shen C, Ma X, Li J, Sun C, Li Z, Shu T, Yu G. Development of lung segmentation method in x-ray images of children based on TransResUNet. FRONTIERS IN RADIOLOGY 2023; 3:1190745. [PMID: 37492393 PMCID: PMC10365102 DOI: 10.3389/fradi.2023.1190745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 04/06/2023] [Indexed: 07/27/2023]
Abstract
Background Chest x-ray (CXR) is widely applied for the detection and diagnosis of children's lung diseases. Lung field segmentation in digital CXR images is a key section of many computer-aided diagnosis systems. Objective In this study, we propose a method based on deep learning to improve the lung segmentation quality and accuracy of children's multi-center CXR images. Methods The novelty of the proposed method is the combination of merits of TransUNet and ResUNet. The former can provide a self-attention module improving the feature learning ability of the model, while the latter can avoid the problem of network degradation. Results Applied on the test set containing multi-center data, our model achieved a Dice score of 0.9822. Conclusions This novel lung segmentation method proposed in this work based on TransResUNet is better than other existing medical image segmentation networks.
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Affiliation(s)
- Lingdong Chen
- Department of Data and Information, The Children’s Hospital Zhejiang University School of Medicine, Hangzhou, China
- Medicine Engineering and Information Research Institute for Children's Health, Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China
- Medicine Engineering and Information Research Institute for Children's Health, National Clinical Research Center for Child Health, Hangzhou, China
| | - Zhuo Yu
- Department of Scientific Research, Huiying Medical Technology (Beijing) Co., Ltd, Beijing, China
| | - Jian Huang
- Department of Data and Information, The Children’s Hospital Zhejiang University School of Medicine, Hangzhou, China
- Medicine Engineering and Information Research Institute for Children's Health, Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China
- Medicine Engineering and Information Research Institute for Children's Health, National Clinical Research Center for Child Health, Hangzhou, China
| | - Liqi Shu
- Department of Neurology, The Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Pekka Kuosmanen
- Medicine Engineering and Information Research Institute for Children's Health, Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China
- Department of Scientific Research, Avaintec Oy Company, Helsinki, Finland
| | - Chen Shen
- Department of Data and Information, The Children’s Hospital Zhejiang University School of Medicine, Hangzhou, China
- Medicine Engineering and Information Research Institute for Children's Health, Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China
- Medicine Engineering and Information Research Institute for Children's Health, National Clinical Research Center for Child Health, Hangzhou, China
| | - Xiaohui Ma
- Department of Radiology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jing Li
- Department of Data and Information, The Children’s Hospital Zhejiang University School of Medicine, Hangzhou, China
- Medicine Engineering and Information Research Institute for Children's Health, Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China
- Medicine Engineering and Information Research Institute for Children's Health, National Clinical Research Center for Child Health, Hangzhou, China
| | - Chensheng Sun
- Department of Data and Information, The Children’s Hospital Zhejiang University School of Medicine, Hangzhou, China
- Medicine Engineering and Information Research Institute for Children's Health, Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China
- Medicine Engineering and Information Research Institute for Children's Health, National Clinical Research Center for Child Health, Hangzhou, China
| | - Zheming Li
- Department of Data and Information, The Children’s Hospital Zhejiang University School of Medicine, Hangzhou, China
- Medicine Engineering and Information Research Institute for Children's Health, Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China
- Medicine Engineering and Information Research Institute for Children's Health, National Clinical Research Center for Child Health, Hangzhou, China
| | - Ting Shu
- Department of Information Standardization Research,National Institute of Hospital Administration, NHC, Beijing, China
| | - Gang Yu
- Department of Data and Information, The Children’s Hospital Zhejiang University School of Medicine, Hangzhou, China
- Medicine Engineering and Information Research Institute for Children's Health, Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China
- Medicine Engineering and Information Research Institute for Children's Health, National Clinical Research Center for Child Health, Hangzhou, China
- Polytechnic Institute, Zhejiang University, Hangzhou, China
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Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans. Diagnostics (Basel) 2022; 12:diagnostics12092132. [PMID: 36140533 PMCID: PMC9497601 DOI: 10.3390/diagnostics12092132] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/26/2022] [Accepted: 08/30/2022] [Indexed: 12/16/2022] Open
Abstract
Background and Motivation: COVID-19 has resulted in a massive loss of life during the last two years. The current imaging-based diagnostic methods for COVID-19 detection in multiclass pneumonia-type chest X-rays are not so successful in clinical practice due to high error rates. Our hypothesis states that if we can have a segmentation-based classification error rate <5%, typically adopted for 510 (K) regulatory purposes, the diagnostic system can be adapted in clinical settings. Method: This study proposes 16 types of segmentation-based classification deep learning-based systems for automatic, rapid, and precise detection of COVID-19. The two deep learning-based segmentation networks, namely UNet and UNet+, along with eight classification models, namely VGG16, VGG19, Xception, InceptionV3, Densenet201, NASNetMobile, Resnet50, and MobileNet, were applied to select the best-suited combination of networks. Using the cross-entropy loss function, the system performance was evaluated by Dice, Jaccard, area-under-the-curve (AUC), and receiver operating characteristics (ROC) and validated using Grad-CAM in explainable AI framework. Results: The best performing segmentation model was UNet, which exhibited the accuracy, loss, Dice, Jaccard, and AUC of 96.35%, 0.15%, 94.88%, 90.38%, and 0.99 (p-value <0.0001), respectively. The best performing segmentation-based classification model was UNet+Xception, which exhibited the accuracy, precision, recall, F1-score, and AUC of 97.45%, 97.46%, 97.45%, 97.43%, and 0.998 (p-value <0.0001), respectively. Our system outperformed existing methods for segmentation-based classification models. The mean improvement of the UNet+Xception system over all the remaining studies was 8.27%. Conclusion: The segmentation-based classification is a viable option as the hypothesis (error rate <5%) holds true and is thus adaptable in clinical practice.
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Wu Y, Zhao S, Yang X, Yang C, Shi Z, Liu Q, Wang Y, Qin M, Zhang L. Ultrasound Lung Image under Artificial Intelligence Algorithm in Diagnosis of Neonatal Respiratory Distress Syndrome. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1817341. [PMID: 35387221 PMCID: PMC8977311 DOI: 10.1155/2022/1817341] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/25/2022] [Accepted: 03/02/2022] [Indexed: 12/13/2022]
Abstract
In order to analyze the application of ultrasonic lung imaging diagnosis model based on artificial intelligence algorithm in neonatal respiratory distress syndrome (NRDS), an ultrasonic lung imaging diagnosis model based on a deep residual network (DRN) was proposed. In this study, 90 premature infants in the hospital were selected as the research object and divided into the experimental group (45 cases) and control group (45 cases) according to whether or not they have NRDS. DRN was compared with the deep residual network (DRWSR) based on wavelet domain, deep residual network detection with normalization framework (Fisher-DRN), and distorted image edge detection preprocessor (DIEDP). Then, it was applied to the diagnosis of NRDS. The clinical data and ultrasound imaging results of infants with NRDS and ordinary premature infants were compared. The results showed that the gestational age, birth weight, and Apgar scores of the NRDS group were remarkably lower than those of ordinary children (P < 0.05). In addition, the segmentation accuracy, image feature extraction accuracy, algorithm convergence, and time loss of the DRN algorithm were better than the other three algorithms, and the differences were considerable (P < 0.05). In children with NRDS, the positive rate of abnormal pleural line, disappearance of A line, appearance of B line, and alveolar interstitial syndrome (AIS) test in the results of lung ultrasound examination in children with NRDS were all 100%. The lung consolidation became 70.8%, and the white lung-like change was 50.1%, both of which were higher than those of ordinary preterm infants, and the differences were considerable (P < 0.05). The diagnostic model of this study predicted that the AUC area of grade 1-2, grade 2-3, and grade 3-4 NRDS were 0.962, 0.881, and 0.902, respectively. To sum up, the ultrasound lung imaging diagnosis model based on the DRN algorithm had good diagnostic performance in children with NRDS and can provide useful information for clinical NRDS diagnosis and treatment.
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Affiliation(s)
- Yuhan Wu
- Department of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China
| | - Sheng Zhao
- Department of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China
| | - Xiaohong Yang
- Department of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China
| | - Chunxue Yang
- Department of Ultrasound, Caidian District People's Hospital of Wuhan, Hubei Province 430100, China
| | - Zhen Shi
- Department of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China
| | - Qin Liu
- Department of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China
| | - Yubo Wang
- Department of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China
| | - Meilan Qin
- Department of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China
| | - Li Zhang
- Department of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China
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Segmentation and Quantitative Analysis of Photoacoustic Imaging: A Review. PHOTONICS 2022. [DOI: 10.3390/photonics9030176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Photoacoustic imaging is an emerging biomedical imaging technique that combines optical contrast and ultrasound resolution to create unprecedented light absorption contrast in deep tissue. Thanks to its fusional imaging advantages, photoacoustic imaging can provide multiple structural and functional insights into biological tissues such as blood vasculatures and tumors and monitor the kinetic movements of hemoglobin and lipids. To better visualize and analyze the regions of interest, segmentation and quantitative analyses were used to extract several biological factors, such as the intensity level changes, diameter, and tortuosity of the tissues. Over the past 10 years, classical segmentation methods and advances in deep learning approaches have been utilized in research investigations. In this review, we provide a comprehensive review of segmentation and quantitative methods that have been developed to process photoacoustic imaging in preclinical and clinical experiments. We focus on the parametric reliability of quantitative analysis for semantic and instance-level segmentation. We also introduce the similarities and alternatives of deep learning models in qualitative measurements using classical segmentation methods for photoacoustic imaging.
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Maity A, Nair TR, Mehta S, Prakasam P. Automatic lung parenchyma segmentation using a deep convolutional neural network from chest X-rays. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103398] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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10
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Padash S, Mohebbian MR, Adams SJ, Henderson RDE, Babyn P. Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review. Pediatr Radiol 2022; 52:1568-1580. [PMID: 35460035 PMCID: PMC9033522 DOI: 10.1007/s00247-022-05368-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/28/2022] [Accepted: 03/24/2022] [Indexed: 10/24/2022]
Abstract
Most artificial intelligence (AI) studies have focused primarily on adult imaging, with less attention to the unique aspects of pediatric imaging. The objectives of this study were to (1) identify all publicly available pediatric datasets and determine their potential utility and limitations for pediatric AI studies and (2) systematically review the literature to assess the current state of AI in pediatric chest radiograph interpretation. We searched PubMed, Web of Science and Embase to retrieve all studies from 1990 to 2021 that assessed AI for pediatric chest radiograph interpretation and abstracted the datasets used to train and test AI algorithms, approaches and performance metrics. Of 29 publicly available chest radiograph datasets, 2 datasets included solely pediatric chest radiographs, and 7 datasets included pediatric and adult patients. We identified 55 articles that implemented an AI model to interpret pediatric chest radiographs or pediatric and adult chest radiographs. Classification of chest radiographs as pneumonia was the most common application of AI, evaluated in 65% of the studies. Although many studies report high diagnostic accuracy, most algorithms were not validated on external datasets. Most AI studies for pediatric chest radiograph interpretation have focused on a limited number of diseases, and progress is hindered by a lack of large-scale pediatric chest radiograph datasets.
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Affiliation(s)
- Sirwa Padash
- Department of Medical Imaging, University of Saskatchewan, 103 Hospital Drive, Saskatoon, Saskatchewan, S7N 0W8, Canada. .,Department of Radiology, Mayo Clinic, Rochester, MN, USA.
| | - Mohammad Reza Mohebbian
- grid.25152.310000 0001 2154 235XDepartment of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Saskatchewan Canada
| | - Scott J. Adams
- grid.25152.310000 0001 2154 235XDepartment of Medical Imaging, University of Saskatchewan, 103 Hospital Drive, Saskatoon, Saskatchewan S7N 0W8 Canada
| | - Robert D. E. Henderson
- grid.25152.310000 0001 2154 235XDepartment of Medical Imaging, University of Saskatchewan, 103 Hospital Drive, Saskatoon, Saskatchewan S7N 0W8 Canada
| | - Paul Babyn
- grid.25152.310000 0001 2154 235XDepartment of Medical Imaging, University of Saskatchewan, 103 Hospital Drive, Saskatoon, Saskatchewan S7N 0W8 Canada
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Hosseinzadeh Taher MR, Haghighi F, Feng R, Gotway MB, Liang J. A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis. DOMAIN ADAPTATION AND REPRESENTATION TRANSFER, AND AFFORDABLE HEALTHCARE AND AI FOR RESOURCE DIVERSE GLOBAL HEALTH : THIRD MICCAI WORKSHOP, DART 2021 AND FIRST MICCAI WORKSHOP, FAIR 2021 : HELD IN CONJUNCTION WITH MICCAI 2021 : STRASBOU... 2021; 12968:3-13. [PMID: 35713581 PMCID: PMC9197759 DOI: 10.1007/978-3-030-87722-4_1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Transfer learning from supervised ImageNet models has been frequently used in medical image analysis. Yet, no large-scale evaluation has been conducted to benchmark the efficacy of newly-developed pre-training techniques for medical image analysis, leaving several important questions unanswered. As the first step in this direction, we conduct a systematic study on the transferability of models pre-trained on iNat2021, the most recent large-scale fine-grained dataset, and 14 top self-supervised ImageNet models on 7 diverse medical tasks in comparison with the supervised ImageNet model. Furthermore, we present a practical approach to bridge the domain gap between natural and medical images by continually (pre-)training supervised ImageNet models on medical images. Our comprehensive evaluation yields new insights: (1) pre-trained models on fine-grained data yield distinctive local representations that are more suitable for medical segmentation tasks, (2) self-supervised ImageNet models learn holistic features more effectively than supervised ImageNet models, and (3) continual pre-training can bridge the domain gap between natural and medical images. We hope that this large-scale open evaluation of transfer learning can direct the future research of deep learning for medical imaging. As open science, all codes and pre-trained models are available on our GitHub page https://github.com/JLiangLab/BenchmarkTransferLearning.
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Affiliation(s)
| | | | - Ruibin Feng
- Stanford University, Stanford, California 94305, USA
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12
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Reamaroon N, Sjoding MW, Gryak J, Athey BD, Najarian K, Derksen H. Automated detection of acute respiratory distress syndrome from chest X-Rays using Directionality Measure and deep learning features. Comput Biol Med 2021; 134:104463. [PMID: 33993014 DOI: 10.1016/j.compbiomed.2021.104463] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 04/15/2021] [Accepted: 04/28/2021] [Indexed: 11/29/2022]
Abstract
Acute respiratory distress syndrome (ARDS) is a life-threatening lung injury with global prevalence and high mortality. Chest x-rays (CXR) are critical in the early diagnosis and treatment of ARDS. However, imaging findings may not result in proper identification of ARDS due to a number of reasons, including nonspecific appearance of radiological features, ambiguity in a patient's case due to the pathological stage of the disease, and poor inter-rater reliability from interpretations of CXRs by multiple clinical experts. This study demonstrates the potential capability of methodologies in artificial intelligence, machine learning, and image processing to overcome these challenges and quantitatively assess CXRs for presence of ARDS. We propose and describe Directionality Measure, a novel feature engineering technique used to capture the "cloud-like" appearance of diffuse alveolar damage as a mathematical concept. This study also examines the effectiveness of using an off-the-shelf, pretrained deep learning model as a feature extractor in addition to standard features extracted from the histogram and gray-level co-occurrence matrix (GLCM). Data was collected from hospitalized patients at Michigan Medicine's intensive care unit and the cohort's inclusion criteria was specifically designed to be representative of patients at risk of developing ARDS. Multiple machine learning models were used to evaluate these features with 5-fold cross-validation and the final performance was reported on a hold-out, temporally distinct test set. With AdaBoost, Directionality Measure achieved an accuracy of 78% and AUC of 74% - outperforming classification results using features from the histogram (75% accuracy and 73% AUC), GLCM (76% accuracy and 73% AUC), and ResNet-50 (77% accuracy and 73% AUC). Further experimental results demonstrated that using all feature sets in combination achieved the best overall performance, yielding an accuracy of 83% and AUC of 79% with AdaBoost. These results demonstrate the potential capability of using the proposed methodologies to complement current clinical analysis for detection of ARDS from CXRs.
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Affiliation(s)
- Narathip Reamaroon
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.
| | - Michael W Sjoding
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Brian D Athey
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Harm Derksen
- Department of Mathematics, Northeastern University, Boston, MA, United States
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Automatic Lung Segmentation Algorithm on Chest X-ray Images Based on Fusion Variational Auto-Encoder and Three-Terminal Attention Mechanism. Symmetry (Basel) 2021. [DOI: 10.3390/sym13050814] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Automatic segmentation of the lungs in Chest X-ray images (CXRs) is a key step in the screening and diagnosis of related diseases. There are many opacities in the lungs in the CXRs of patients, which makes the lungs difficult to segment. In order to solve this problem, this paper proposes a segmentation algorithm based on U-Net. This article introduces variational auto-encoder (VAE) in each layer of the decoder-encoder. VAE can extract high-level semantic information, such as the symmetrical relationship between the left and right thoraxes in most cases. The fusion of the features of VAE and the features of convolution can improve the ability of the network to extract features. This paper proposes a three-terminal attention mechanism. The attention mechanism uses the channel and spatial attention module to automatically highlight the target area and improve the performance of lung segmentation. At the same time, the three-terminal attention mechanism uses the advanced semantics of high-scale features to improve the positioning and recognition capabilities of the attention mechanism, suppress background noise, and highlight target features. Experimental results on two different datasets show that the accuracy (ACC), recall (R), F1-Score and Jaccard values of the algorithm proposed in this paper are the highest on the two datasets, indicating that the algorithm in this paper is better than other state-of-the-art algorithms.
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