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Alzubaidi M, Shah U, Agus M, Househ M. FetSAM: Advanced Segmentation Techniques for Fetal Head Biometrics in Ultrasound Imagery. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:281-295. [PMID: 38766538 PMCID: PMC11100952 DOI: 10.1109/ojemb.2024.3382487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/05/2024] [Accepted: 03/24/2024] [Indexed: 05/22/2024] Open
Abstract
Goal: FetSAM represents a cutting-edge deep learning model aimed at revolutionizing fetal head ultrasound segmentation, thereby elevating prenatal diagnostic precision. Methods: Utilizing a comprehensive dataset-the largest to date for fetal head metrics-FetSAM incorporates prompt-based learning. It distinguishes itself with a dual loss mechanism, combining Weighted DiceLoss and Weighted Lovasz Loss, optimized through AdamW and underscored by class weight adjustments for better segmentation balance. Performance benchmarks against prominent models such as U-Net, DeepLabV3, and Segformer highlight its efficacy. Results: FetSAM delivers unparalleled segmentation accuracy, demonstrated by a DSC of 0.90117, HD of 1.86484, and ASD of 0.46645. Conclusion: FetSAM sets a new benchmark in AI-enhanced prenatal ultrasound analysis, providing a robust, precise tool for clinical applications and pushing the envelope of prenatal care with its groundbreaking dataset and segmentation capabilities.
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Affiliation(s)
- Mahmood Alzubaidi
- College of Science and EngineeringHamad Bin Khalifa UniversityDoha34110Qatar
| | - Uzair Shah
- College of Science and EngineeringHamad Bin Khalifa UniversityDoha34110Qatar
| | - Marco Agus
- College of Science and EngineeringHamad Bin Khalifa UniversityDoha34110Qatar
| | - Mowafa Househ
- College of Science and EngineeringHamad Bin Khalifa UniversityDoha34110Qatar
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Balagalla UB, Jayasooriya J, de Alwis C, Subasinghe A. Automated segmentation of standard scanning planes to measure biometric parameters in foetal ultrasound images – a survey. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2023. [DOI: 10.1080/21681163.2023.2179343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Affiliation(s)
- U. B. Balagalla
- Department of Electrical and Electronic Engineering, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
| | - J.V.D. Jayasooriya
- Department of Electrical and Electronic Engineering, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
| | - C. de Alwis
- Department of Electrical and Electronic Engineering, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
| | - A. Subasinghe
- Department of Electrical and Electronic Engineering, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
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Fiorentino MC, Villani FP, Di Cosmo M, Frontoni E, Moccia S. A review on deep-learning algorithms for fetal ultrasound-image analysis. Med Image Anal 2023; 83:102629. [PMID: 36308861 DOI: 10.1016/j.media.2022.102629] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 07/12/2022] [Accepted: 09/10/2022] [Indexed: 11/07/2022]
Abstract
Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. A number of survey papers in the field is today available, but most of them are focusing on a broader area of medical-image analysis or not covering all fetal US DL applications. This paper surveys the most recent work in the field, with a total of 153 research papers published after 2017. Papers are analyzed and commented from both the methodology and the application perspective. We categorized the papers into (i) fetal standard-plane detection, (ii) anatomical structure analysis and (iii) biometry parameter estimation. For each category, main limitations and open issues are presented. Summary tables are included to facilitate the comparison among the different approaches. In addition, emerging applications are also outlined. Publicly-available datasets and performance metrics commonly used to assess algorithm performance are summarized, too. This paper ends with a critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working in the field to translate the research methodology into actual clinical practice.
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Affiliation(s)
| | | | - Mariachiara Di Cosmo
- Department of Information Engineering, Università Politecnica delle Marche, Italy
| | - Emanuele Frontoni
- Department of Information Engineering, Università Politecnica delle Marche, Italy; Department of Political Sciences, Communication and International Relations, Università degli Studi di Macerata, Italy
| | - Sara Moccia
- The BioRobotics Institute and Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Italy
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Verma D, Agrawal S, Iwendi C, Sharma B, Bhatia S, Basheer S. A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm. Diagnostics (Basel) 2022; 12:2643. [PMID: 36359487 PMCID: PMC9689292 DOI: 10.3390/diagnostics12112643] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/14/2022] [Accepted: 09/22/2022] [Indexed: 11/25/2023] Open
Abstract
In most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during pregnancy, birth defects and congenital abnormalities are related terms. Fetal abnormalities have been commonly observed in industrialized countries over the previous few decades. Three out of every 1000 pregnant mothers suffer a fetal anomaly. This research work proposes an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality. Findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. Parameters such an accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through the suggested technique is 98.642.%.
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Affiliation(s)
- Deepti Verma
- Department of Computer Application, SAGE University, Indore 452020, India
| | - Shweta Agrawal
- Institute of Advance Computing, SAGE University, Indore 452020, India
| | - Celestine Iwendi
- School of Creative Technologies, University of Bolton, Bolton BL3 5AB, UK
| | - Bhisham Sharma
- Department of Computer Science & Engineering, School of Engineering and Technology, Chitkara University, Baddi 174103, India
| | - Surbhi Bhatia
- Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 36362, Saudi Arabia
| | - Shakila Basheer
- Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. BOX 84428, Riyadh 11671, Saudi Arabia
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Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding. INFORMATICS 2022. [DOI: 10.3390/informatics9020034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child’s outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field.
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An improved semantic segmentation with region proposal network for cardiac defect interpretation. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07217-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Weichert J, Welp A, Scharf JL, Dracopoulos C, Becker WH, Gembicki M. The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics - an Assessment of the State of Play. Geburtshilfe Frauenheilkd 2021; 81:1203-1216. [PMID: 34754270 PMCID: PMC8568505 DOI: 10.1055/a-1522-3029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 06/01/2021] [Indexed: 11/20/2022] Open
Abstract
The long-awaited progress in digitalisation is generating huge amounts of medical data every day, and manual analysis and targeted, patient-oriented evaluation of this data is becoming increasingly difficult or even infeasible. This state of affairs and the associated, increasingly complex requirements for individualised precision medicine underline the need for modern software solutions and algorithms across the entire healthcare system. The utilisation of state-of-the-art equipment and techniques in almost all areas of medicine over the past few years has now indeed enabled automation processes to enter - at least in part - into routine clinical practice. Such systems utilise a wide variety of artificial intelligence (AI) techniques, the majority of which have been developed to optimise medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection and classification and, as an emerging field of research, radiogenomics. Tasks handled by AI are completed significantly faster and more precisely, clearly demonstrated by now in the annual findings of the ImageNet Large-Scale Visual Recognition Challenge (ILSVCR), first conducted in 2015, with error rates well below those of humans. This review article will discuss the potential capabilities and currently available applications of AI in gynaecological-obstetric diagnostics. The article will focus, in particular, on automated techniques in prenatal sonographic diagnostics.
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Affiliation(s)
- Jan Weichert
- Klinik für Frauenheilkunde und Geburtshilfe, Bereich Pränatalmedizin und Spezielle Geburtshilfe, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
- Zentrum für Pränatalmedizin an der Elbe, Hamburg, Germany
| | - Amrei Welp
- Klinik für Frauenheilkunde und Geburtshilfe, Bereich Pränatalmedizin und Spezielle Geburtshilfe, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Jann Lennard Scharf
- Klinik für Frauenheilkunde und Geburtshilfe, Bereich Pränatalmedizin und Spezielle Geburtshilfe, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Christoph Dracopoulos
- Klinik für Frauenheilkunde und Geburtshilfe, Bereich Pränatalmedizin und Spezielle Geburtshilfe, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | | | - Michael Gembicki
- Klinik für Frauenheilkunde und Geburtshilfe, Bereich Pränatalmedizin und Spezielle Geburtshilfe, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
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Espinosa C, Becker M, Marić I, Wong RJ, Shaw GM, Gaudilliere B, Aghaeepour N, Stevenson DK. Data-Driven Modeling of Pregnancy-Related Complications. Trends Mol Med 2021; 27:762-776. [PMID: 33573911 DOI: 10.1016/j.molmed.2021.01.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 12/01/2020] [Accepted: 01/20/2021] [Indexed: 12/11/2022]
Abstract
A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.
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Affiliation(s)
- Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Ivana Marić
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Ronald J Wong
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Gary M Shaw
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA; Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - David K Stevenson
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA.
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Garcia-Canadilla P, Sanchez-Martinez S, Crispi F, Bijnens B. Machine Learning in Fetal Cardiology: What to Expect. Fetal Diagn Ther 2020; 47:363-372. [PMID: 31910421 DOI: 10.1159/000505021] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 11/25/2019] [Indexed: 11/19/2022]
Abstract
In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approaches are currently used to evaluate fetal cardiac structure and function, including conventional 2-D imaging and M-mode and tissue Doppler imaging among others. However, assessment of the fetal heart is still challenging mainly due to involuntary movements of the fetus, the small size of the heart, and the lack of expertise in fetal echocardiography of some sonographers. Therefore, the use of new technologies to improve the primary acquired images, to help extract measurements, or to aid in the diagnosis of cardiac abnormalities is of great importance for optimal assessment of the fetal heart. Machine leaning (ML) is a computer science discipline focused on teaching a computer to perform tasks with specific goals without explicitly programming the rules on how to perform this task. In this review we provide a brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of fetal cardiac remodeling and abnormalities.
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Affiliation(s)
- Patricia Garcia-Canadilla
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain, .,Institute of Cardiovascular Science, University College London, London, United Kingdom,
| | | | - Fatima Crispi
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain.,Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia Obstetricia i Neonatologia, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Bart Bijnens
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain.,Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.,ICREA, Barcelona, Spain
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