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Specktor-Fadida B, Link-Sourani D, Rabinowich A, Miller E, Levchakov A, Avisdris N, Ben-Sira L, Hiersch L, Joskowicz L, Ben-Bashat D. Deep learning-based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility. Eur Radiol 2024; 34:2072-2083. [PMID: 37658890 DOI: 10.1007/s00330-023-10038-y] [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: 02/28/2023] [Revised: 06/13/2023] [Accepted: 06/19/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVES To develop a deep-learning method for whole-body fetal segmentation based on MRI; to assess the method's repeatability, reproducibility, and accuracy; to create an MRI-based normal fetal weight growth chart; and to assess the sensitivity to detect fetuses with growth restriction (FGR). METHODS Retrospective data of 348 fetuses with gestational age (GA) of 19-39 weeks were included: 249 normal appropriate for GA (AGA), 19 FGR, and 80 Other (having various imaging abnormalities). A fetal whole-body segmentation model with a quality estimation module was developed and evaluated in 169 cases. The method was evaluated for its repeatability (repeated scans within the same scanner, n = 22), reproducibility (different scanners, n = 6), and accuracy (compared with birth weight, n = 7). A normal MRI-based growth chart was derived. RESULTS The method achieved a Dice = 0.973, absolute volume difference ratio (VDR) = 1.8% and VDR mean difference = 0.75% ([Formula: see text]: - 3.95%, 5.46), and high agreement with the gold standard. The method achieved a repeatability coefficient = 4.01%, ICC = 0.99, high reproducibility with a mean difference = 2.21% ([Formula: see text]: - 1.92%, 6.35%), and high accuracy with a mean difference between estimated fetal weight (EFW) and birth weight of - 0.39% ([Formula: see text]: - 8.23%, 7.45%). A normal growth chart (n = 246) was consistent with four ultrasound charts. EFW based on MRI correctly predicted birth-weight percentiles for all 18 fetuses ≤ 10thpercentile and for 14 out of 17 FGR fetuses below the 3rd percentile. Six fetuses referred to MRI as AGA were found to be < 3rd percentile. CONCLUSIONS The proposed method for automatic MRI-based EFW demonstrated high performance and sensitivity to identify FGR fetuses. CLINICAL RELEVANCE STATEMENT Results from this study support the use of the automatic fetal weight estimation method based on MRI for the assessment of fetal development and to detect fetuses at risk for growth restriction. KEY POINTS • An AI-based segmentation method with a quality assessment module for fetal weight estimation based on MRI was developed, achieving high repeatability, reproducibility, and accuracy. • An MRI-based fetal weight growth chart constructed from a large cohort of normal and appropriate gestational-age fetuses is proposed. • The method showed a high sensitivity for the diagnosis of small fetuses suspected of growth restriction.
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Affiliation(s)
- Bella Specktor-Fadida
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | | | - Aviad Rabinowich
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Elka Miller
- Department of Medical Imaging, The Hospital for Sick Children, University of Toronto, Toronto, Canada
- Department of Medical Imaging, CHEO, University of Ottawa, Ottawa, Canada
| | - Anna Levchakov
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Netanell Avisdris
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Liat Ben-Sira
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Liran Hiersch
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Obstetrics and Gynecology, Lis Hospital for Women's Health, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Leo Joskowicz
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dafna Ben-Bashat
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
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Costanzo A, Ertl-Wagner B, Sussman D. AFNet Algorithm for Automatic Amniotic Fluid Segmentation from Fetal MRI. Bioengineering (Basel) 2023; 10:783. [PMID: 37508809 PMCID: PMC10376488 DOI: 10.3390/bioengineering10070783] [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: 05/24/2023] [Revised: 06/25/2023] [Accepted: 06/27/2023] [Indexed: 07/30/2023] Open
Abstract
Amniotic Fluid Volume (AFV) is a crucial fetal biomarker when diagnosing specific fetal abnormalities. This study proposes a novel Convolutional Neural Network (CNN) model, AFNet, for segmenting amniotic fluid (AF) to facilitate clinical AFV evaluation. AFNet was trained and tested on a manually segmented and radiologist-validated AF dataset. AFNet outperforms ResUNet++ by using efficient feature mapping in the attention block and transposing convolutions in the decoder. Our experimental results show that AFNet achieved a mean Intersection over Union (mIoU) of 93.38% on our dataset, thereby outperforming other state-of-the-art models. While AFNet achieves performance scores similar to those of the UNet++ model, it does so while utilizing merely less than half the number of parameters. By creating a detailed AF dataset with an improved CNN architecture, we enable the quantification of AFV in clinical practice, which can aid in diagnosing AF disorders during gestation.
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Affiliation(s)
- Alejo Costanzo
- Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
- Institute for Biomedical Engineering, Science and Technology (iBEST), Toronto Metropolitan University and St. Michael's Hospital, Toronto, ON M5B 1T8, Canada
| | - Birgit Ertl-Wagner
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Department of Medical Imaging, Faculty of Medicine, University of Toronto, Toronto, ON M5T 1W7, Canada
| | - Dafna Sussman
- Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
- Institute for Biomedical Engineering, Science and Technology (iBEST), Toronto Metropolitan University and St. Michael's Hospital, Toronto, ON M5B 1T8, Canada
- Department of Obstetrics and Gynecology, Faculty of Medicine, University of Toronto, Toronto, ON M5G 1E2, Canada
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Shen J, Zhang L, Yang L, Xu H, Chen S, Ji J, Huang S, Liang H, Dong C, Lou X. Testing a Method Based on an Improved UNet and Skeleton Thinning Algorithm to Obtain Branch Phenotypes of Tall and Valuable Trees Using Abies beshanzuensis as the Research Sample. PLANTS (BASEL, SWITZERLAND) 2023; 12:2444. [PMID: 37447004 DOI: 10.3390/plants12132444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/19/2023] [Accepted: 06/22/2023] [Indexed: 07/15/2023]
Abstract
Sudden changes in the morphological characteristics of trees are closely related to plant health, and automated phenotypic measurements can help improve the efficiency of plant health monitoring, and thus aid in the conservation of old and valuable tress. The irregular distribution of branches and the influence of the natural environment make it very difficult to monitor the status of branches in the field. In order to solve the problem of branch phenotype monitoring of tall and valuable plants in the field environment, this paper proposes an improved UNet model to achieve accurate extraction of trunk and branches. This paper also proposes an algorithm that can measure the branch length and inclination angle by using the main trunk and branches separated in the previous stage, finding the skeleton line of a single branch via digital image morphological processing and the Zhang-Suen thinning algorithm, obtaining the number of pixel points as the branch length, and then using Euclidean distance to fit a straight line to calculate the inclination angle of each branch. These were carried out in order to monitor the change in branch length and inclination angle and to determine whether plant branch breakage or external stress events had occurred. We evaluated the method on video images of Abies beshanzuensis, and the experimental results showed that the present algorithm has more excellent performance at 94.30% MIoU as compared with other target segmentation algorithms. The coefficient of determination (R2) is higher than 0.89 for the calculation of the branch length and inclination angle. In summary, the algorithm proposed in this paper can effectively segment the branches of tall plants and measure their length and inclination angle in a field environment, thus providing an effective method to monitor the health of valuable plants.
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Affiliation(s)
- Jiahui Shen
- College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, China
- Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Zhejiang A & F University, Hangzhou 311300, China
- Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research of Zhejiang Province, Zhejiang A & F University, Hangzhou 311300, China
| | - Lihong Zhang
- Longquan Forestry Bureau, Longquan 323700, China
| | - Laibang Yang
- Hangzhou Ganzhi Technology Co., Ltd., Lin'an 311300, China
| | - Hao Xu
- Zhejiang Forestry Bureau, Hangzhou 310000, China
| | - Sheng Chen
- Center for Forest Resource Monitoring of Zhejiang Province, Hangzhou 310000, China
| | - Jingyong Ji
- Longquan Forestry Bureau, Longquan 323700, China
| | - Siqi Huang
- Longquan Urban Forestry Workstation, Longquan 323700, China
| | - Hao Liang
- College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, China
- Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Zhejiang A & F University, Hangzhou 311300, China
- Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research of Zhejiang Province, Zhejiang A & F University, Hangzhou 311300, China
| | - Chen Dong
- College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, China
- Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Zhejiang A & F University, Hangzhou 311300, China
- Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research of Zhejiang Province, Zhejiang A & F University, Hangzhou 311300, China
| | - Xiongwei Lou
- College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, China
- Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Zhejiang A & F University, Hangzhou 311300, China
- Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research of Zhejiang Province, Zhejiang A & F University, Hangzhou 311300, China
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Motion artifact correction in fetal MRI based on a Generative Adversarial network method. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Lo J, Lim A, Wagner MW, Ertl-Wagner B, Sussman D. Fetal Organ Anomaly Classification Network for Identifying Organ Anomalies in Fetal MRI. Front Artif Intell 2022; 5:832485. [PMID: 35372832 PMCID: PMC8972161 DOI: 10.3389/frai.2022.832485] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 02/14/2022] [Indexed: 11/13/2022] Open
Abstract
Rapid development in Magnetic Resonance Imaging (MRI) has played a key role in prenatal diagnosis over the last few years. Deep learning (DL) architectures can facilitate the process of anomaly detection and affected-organ classification, making diagnosis more accurate and observer-independent. We propose a novel DL image classification architecture, Fetal Organ Anomaly Classification Network (FOAC-Net), which uses squeeze-and-excitation (SE) and naïve inception (NI) modules to automatically identify anomalies in fetal organs. This architecture can identify normal fetal anatomy, as well as detect anomalies present in the (1) brain, (2) spinal cord, and (3) heart. In this retrospective study, we included fetal 3-dimensional (3D) SSFP sequences of 36 participants. We classified the images on a slice-by-slice basis. FOAC-Net achieved a classification accuracy of 85.06, 85.27, 89.29, and 82.20% when predicting brain anomalies, no anomalies (normal), spinal cord anomalies, and heart anomalies, respectively. In a comparison study, FOAC-Net outperformed other state-of-the-art classification architectures in terms of class-average F1 and accuracy. This work aims to develop a novel classification architecture identifying the affected organs in fetal MRI.
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Affiliation(s)
- Justin Lo
- Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Sciences, Ryerson University, Toronto, ON, Canada
- Institute for Biomedical Engineering, Science and Technology (iBEST), a partnership between St. Michael's Hospital and Ryerson University, Toronto, ON, Canada
| | - Adam Lim
- Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Sciences, Ryerson University, Toronto, ON, Canada
- Institute for Biomedical Engineering, Science and Technology (iBEST), a partnership between St. Michael's Hospital and Ryerson University, Toronto, ON, Canada
| | - Matthias W. Wagner
- Division of Neuroradiology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Birgit Ertl-Wagner
- Division of Neuroradiology, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Medical Imaging, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Dafna Sussman
- Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Sciences, Ryerson University, Toronto, ON, Canada
- Institute for Biomedical Engineering, Science and Technology (iBEST), a partnership between St. Michael's Hospital and Ryerson University, Toronto, ON, Canada
- Department of Obstetrics and Gynecology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- *Correspondence: Dafna Sussman
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Meshaka R, Gaunt T, Shelmerdine SC. Artificial intelligence applied to fetal MRI: A scoping review of current research. Br J Radiol 2022:20211205. [PMID: 35286139 DOI: 10.1259/bjr.20211205] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Artificial intelligence (AI) is defined as the development of computer systems to perform tasks normally requiring human intelligence. A subset of AI, known as machine learning (ML), takes this further by drawing inferences from patterns in data to 'learn' and 'adapt' without explicit instructions meaning that computer systems can 'evolve' and hopefully improve without necessarily requiring external human influences. The potential for this novel technology has resulted in great interest from the medical community regarding how it can be applied in healthcare. Within radiology, the focus has mostly been for applications in oncological imaging, although new roles in other subspecialty fields are slowly emerging.In this scoping review, we performed a literature search of the current state-of-the-art and emerging trends for the use of artificial intelligence as applied to fetal magnetic resonance imaging (MRI). Our search yielded several publications covering AI tools for anatomical organ segmentation, improved imaging sequences and aiding in diagnostic applications such as automated biometric fetal measurements and the detection of congenital and acquired abnormalities. We highlight our own perceived gaps in this literature and suggest future avenues for further research. It is our hope that the information presented highlights the varied ways and potential that novel digital technology could make an impact to future clinical practice with regards to fetal MRI.
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Affiliation(s)
- Riwa Meshaka
- Department of Clinical Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, Great Ormond Street, London, UK
| | - Trevor Gaunt
- Department of Radiology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Susan C Shelmerdine
- Department of Clinical Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, Great Ormond Street, London, UK.,UCL Great Ormond Street Institute of Child Health, Great Ormond Street Hospital for Children, London, UK.,NIHR Great Ormond Street Hospital Biomedical Research Centre, 30 Guilford Street, Bloomsbury, London, UK.,Department of Radiology, St. George's Hospital, Blackshaw Road, London, UK
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