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Liu J, Zhang Y, Jia F, Zhang H, Luo L, Liao Y, Ouyang M, Yi X, Zhu R, Bai W, Ning G, Li X, Qu H. Sex differences in fetal brain functional network topology. Cereb Cortex 2024; 34:bhae111. [PMID: 38517172 DOI: 10.1093/cercor/bhae111] [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: 12/14/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 03/23/2024] Open
Abstract
The fetal period is a critical stage in brain development, and understanding the characteristics of the fetal brain is crucial. Although some studies have explored aspects of fetal brain functional networks, few have specifically focused on sex differences in brain network characteristics. We adopted the graph theory method to calculate brain network functional connectivity and topology properties (including global and nodal properties), and further compared the differences in these parameters between male and female fetuses. We found that male fetuses showed an increased clustering coefficient and local efficiency than female fetuses, but no significant group differences concerning other graph parameters and the functional connectivity matrix. Our study suggests the existence of sex-related distinctions in the topological properties of the brain network at the fetal stage of development and demonstrates an increase in brain network separation in male fetuses compared with female fetuses.
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Affiliation(s)
- Jing Liu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Yujin Zhang
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Fenglin Jia
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Hongding Zhang
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Lekai Luo
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Yi Liao
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Minglei Ouyang
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Xiaoxue Yi
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Ruixi Zhu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Wanjing Bai
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Gang Ning
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Xuesheng Li
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Haibo Qu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
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Ciceri T, Squarcina L, Giubergia A, Bertoldo A, Brambilla P, Peruzzo D. Review on deep learning fetal brain segmentation from Magnetic Resonance images. Artif Intell Med 2023; 143:102608. [PMID: 37673558 DOI: 10.1016/j.artmed.2023.102608] [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: 12/21/2022] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 09/08/2023]
Abstract
Brain segmentation is often the first and most critical step in quantitative analysis of the brain for many clinical applications, including fetal imaging. Different aspects challenge the segmentation of the fetal brain in magnetic resonance imaging (MRI), such as the non-standard position of the fetus owing to his/her movements during the examination, rapid brain development, and the limited availability of imaging data. In recent years, several segmentation methods have been proposed for automatically partitioning the fetal brain from MR images. These algorithms aim to define regions of interest with different shapes and intensities, encompassing the entire brain, or isolating specific structures. Deep learning techniques, particularly convolutional neural networks (CNNs), have become a state-of-the-art approach in the field because they can provide reliable segmentation results over heterogeneous datasets. Here, we review the deep learning algorithms developed in the field of fetal brain segmentation and categorize them according to their target structures. Finally, we discuss the perceived research gaps in the literature of the fetal domain, suggesting possible future research directions that could impact the management of fetal MR images.
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Affiliation(s)
- Tommaso Ciceri
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Information Engineering, University of Padua, Padua, Italy
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alice Giubergia
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Information Engineering, University of Padua, Padua, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padua, Padua, Italy; University of Padua, Padova Neuroscience Center, Padua, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Denis Peruzzo
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
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3
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Vahedifard F, Ai HA, Supanich MP, Marathu KK, Liu X, Kocak M, Ansari SM, Akyuz M, Adepoju JO, Adler S, Byrd S. Automatic Ventriculomegaly Detection in Fetal Brain MRI: A Step-by-Step Deep Learning Model for Novel 2D-3D Linear Measurements. Diagnostics (Basel) 2023; 13:2355. [PMID: 37510099 PMCID: PMC10378043 DOI: 10.3390/diagnostics13142355] [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: 06/15/2023] [Revised: 07/07/2023] [Accepted: 07/09/2023] [Indexed: 07/30/2023] Open
Abstract
In this study, we developed an automated workflow using a deep learning model (DL) to measure the lateral ventricle linearly in fetal brain MRI, which are subsequently classified into normal or ventriculomegaly, defined as a diameter wider than 10 mm at the level of the thalamus and choroid plexus. To accomplish this, we first trained a UNet-based deep learning model to segment the brain of a fetus into seven different tissue categories using a public dataset (FeTA 2022) consisting of fetal T2-weighted images. Then, an automatic workflow was developed to perform lateral ventricle measurement at the level of the thalamus and choroid plexus. The test dataset included 22 cases of normal and abnormal T2-weighted fetal brain MRIs. Measurements performed by our AI model were compared with manual measurements performed by a general radiologist and a neuroradiologist. The AI model correctly classified 95% of fetal brain MRI cases into normal or ventriculomegaly. It could measure the lateral ventricle diameter in 95% of cases with less than a 1.7 mm error. The average difference between measurements was 0.90 mm in AI vs. general radiologists and 0.82 mm in AI vs. neuroradiologists, which are comparable to the difference between the two radiologists, 0.51 mm. In addition, the AI model also enabled the researchers to create 3D-reconstructed images, which better represent real anatomy than 2D images. When a manual measurement is performed, it could also provide both the right and left ventricles in just one cut, instead of two. The measurement difference between the general radiologist and the algorithm (p = 0.9827), and between the neuroradiologist and the algorithm (p = 0.2378), was not statistically significant. In contrast, the difference between general radiologists vs. neuroradiologists was statistically significant (p = 0.0043). To the best of our knowledge, this is the first study that performs 2D linear measurement of ventriculomegaly with a 3D model based on an artificial intelligence approach. The paper presents a step-by-step approach for designing an AI model based on several radiological criteria. Overall, this study showed that AI can automatically calculate the lateral ventricle in fetal brain MRIs and accurately classify them as abnormal or normal.
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Affiliation(s)
- Farzan Vahedifard
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - H Asher Ai
- Division for Diagnostic Medical Physics, Department of Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Mark P Supanich
- Division for Diagnostic Medical Physics, Department of Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Kranthi K Marathu
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Xuchu Liu
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Mehmet Kocak
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Shehbaz M Ansari
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Melih Akyuz
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Jubril O Adepoju
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Seth Adler
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Sharon Byrd
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
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Nichols ES, Correa S, Van Dyken P, Kai J, Kuehn T, de Ribaupierre S, Duerden EG, Khan AR. Funcmasker-flex: An Automated BIDS-App for Brain Segmentation of Human Fetal Functional MRI data. Neuroinformatics 2023; 21:565-573. [PMID: 37000360 DOI: 10.1007/s12021-023-09629-3] [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] [Accepted: 03/21/2023] [Indexed: 04/01/2023]
Abstract
Fetal functional magnetic resonance imaging (fMRI) offers critical insight into the developing brain and could aid in predicting developmental outcomes. As the fetal brain is surrounded by heterogeneous tissue, it is not possible to use adult- or child-based segmentation toolboxes. Manually-segmented masks can be used to extract the fetal brain; however, this comes at significant time costs. Here, we present a new BIDS App for masking fetal fMRI, funcmasker-flex, that overcomes these issues with a robust 3D convolutional neural network (U-net) architecture implemented in an extensible and transparent Snakemake workflow. Open-access fetal fMRI data with manual brain masks from 159 fetuses (1103 total volumes) were used for training and testing the U-net model. We also tested generalizability of the model using 82 locally acquired functional scans from 19 fetuses, which included over 2300 manually segmented volumes. Dice metrics were used to compare performance of funcmasker-flex to the ground truth manually segmented volumes, and segmentations were consistently robust (all Dice metrics ≥ 0.74). The tool is freely available and can be applied to any BIDS dataset containing fetal bold sequences. Funcmasker-flex reduces the need for manual segmentation, even when applied to novel fetal functional datasets, resulting in significant time-cost savings for performing fetal fMRI analysis.
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Affiliation(s)
- Emily S Nichols
- Faculty of Education, Western University, London, Canada.
- Western Institute for Neuroscience, Western University, London, Canada.
- Applied Psychology, Faculty of Education, Room 1131, 1137 Western Rd, N6G 1G7, London, ON, Canada.
| | - Susana Correa
- Neuroscience program, Schulich School of Medicine & Dentistry, Western University, London, Canada
| | - Peter Van Dyken
- Neuroscience program, Schulich School of Medicine & Dentistry, Western University, London, Canada
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Jason Kai
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Canada
- Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, Canada
| | - Tristan Kuehn
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Canada
- Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, Canada
| | - Sandrine de Ribaupierre
- Western Institute for Neuroscience, Western University, London, Canada
- Neuroscience program, Schulich School of Medicine & Dentistry, Western University, London, Canada
- Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, Canada
- Biomedical Engineering, Western University, London, Canada
- Clinical Neurological Sciences, Schulich School of Medicine & Dentistry, Western University, London, Canada
- Anatomy and Cell Biology, Schulich School of Medicine & Dentistry, Western University, London, Canada
| | - Emma G Duerden
- Faculty of Education, Western University, London, Canada
- Western Institute for Neuroscience, Western University, London, Canada
- Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, Canada
| | - Ali R Khan
- Western Institute for Neuroscience, Western University, London, Canada
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Canada
- Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, Canada
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5
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Vahedifard F, Adepoju JO, Supanich M, Ai HA, Liu X, Kocak M, Marathu KK, Byrd SE. Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging. World J Clin Cases 2023; 11:3725-3735. [PMID: 37383127 PMCID: PMC10294149 DOI: 10.12998/wjcc.v11.i16.3725] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 01/30/2023] [Accepted: 05/06/2023] [Indexed: 06/02/2023] Open
Abstract
Central nervous system abnormalities in fetuses are fairly common, happening in 0.1% to 0.2% of live births and in 3% to 6% of stillbirths. So initial detection and categorization of fetal Brain abnormalities are critical. Manually detecting and segmenting fetal brain magnetic resonance imaging (MRI) could be time-consuming, and susceptible to interpreter experience. Artificial intelligence (AI) algorithms and machine learning approaches have a high potential for assisting in the early detection of these problems, improving the diagnosis process and follow-up procedures. The use of AI and machine learning techniques in fetal brain MRI was the subject of this narrative review paper. Using AI, anatomic fetal brain MRI processing has investigated models to predict specific landmarks and segmentation automatically. All gestation age weeks (17-38 wk) and different AI models (mainly Convolutional Neural Network and U-Net) have been used. Some models' accuracy achieved 95% and more. AI could help preprocess and post-process fetal images and reconstruct images. Also, AI can be used for gestational age prediction (with one-week accuracy), fetal brain extraction, fetal brain segmentation, and placenta detection. Some fetal brain linear measurements, such as Cerebral and Bone Biparietal Diameter, have been suggested. Classification of brain pathology was studied using diagonal quadratic discriminates analysis, K-nearest neighbor, random forest, naive Bayes, and radial basis function neural network classifiers. Deep learning methods will become more powerful as more large-scale, labeled datasets become available. Having shared fetal brain MRI datasets is crucial because there aren not many fetal brain pictures available. Also, physicians should be aware of AI's function in fetal brain MRI, particularly neuroradiologists, general radiologists, and perinatologists.
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Affiliation(s)
- Farzan Vahedifard
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
| | - Jubril O Adepoju
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
| | - Mark Supanich
- Division for Diagnostic Medical Physics, Department of Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL 606012, United States
| | - Hua Asher Ai
- Division for Diagnostic Medical Physics, Department of Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL 606012, United States
| | - Xuchu Liu
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
| | - Mehmet Kocak
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
| | - Kranthi K Marathu
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
| | - Sharon E Byrd
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
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Scheinost D, Pollatou A, Dufford AJ, Jiang R, Farruggia MC, Rosenblatt M, Peterson H, Rodriguez RX, Dadashkarimi J, Liang Q, Dai W, Foster ML, Camp CC, Tejavibulya L, Adkinson BD, Sun H, Ye J, Cheng Q, Spann MN, Rolison M, Noble S, Westwater ML. Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer. Biol Psychiatry 2023; 93:893-904. [PMID: 36759257 PMCID: PMC10259670 DOI: 10.1016/j.biopsych.2022.10.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 09/10/2022] [Accepted: 10/07/2022] [Indexed: 12/01/2022]
Abstract
Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in children of these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief primer and systematic review on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline ethical and future considerations for neuroimaging researchers interested in predicting health outcomes in early life, including researchers who may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning has provided a foundation for accelerating the prediction of early-life trajectories across the full spectrum of illness and health.
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Affiliation(s)
- Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut; Child Study Center, Yale School of Medicine, New Haven, Connecticut; Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut.
| | - Angeliki Pollatou
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York
| | - Alexander J Dufford
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Michael C Farruggia
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Hannah Peterson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | | | | | - Qinghao Liang
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
| | - Maya L Foster
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Chris C Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Jean Ye
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Qi Cheng
- Departments of Neuroscience and Psychology, Smith College, Northampton, Massachusetts
| | - Marisa N Spann
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York
| | - Max Rolison
- Child Study Center, Yale School of Medicine, New Haven, Connecticut
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Margaret L Westwater
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
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Ji L, Majbri A, Hendrix CL, Thomason ME. Fetal behavior during MRI changes with age and relates to network dynamics. Hum Brain Mapp 2023; 44:1683-1694. [PMID: 36564934 PMCID: PMC9921243 DOI: 10.1002/hbm.26167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 10/31/2022] [Accepted: 11/23/2022] [Indexed: 12/25/2022] Open
Abstract
Fetal motor behavior is an important clinical indicator of healthy development. However, our understanding of associations between fetal behavior and fetal brain development is limited. To fill this gap, this study introduced an approach to automatically and objectively classify long durations of fetal movement from a continuous four-dimensional functional magnetic resonance imaging (fMRI) data set, and paired behavior features with brain activity indicated by the fMRI time series. Twelve-minute fMRI scans were conducted in 120 normal fetuses. Postnatal motor function was evaluated at 7 and 36 months age. Fetal motor behavior was quantified by calculating the frame-wise displacement (FD) of fetal brains extracted by a deep-learning model along the whole time series. Analyzing only low motion data, we characterized the recurring coactivation patterns (CAPs) of the supplementary motor area (SMA). Results showed reduced motor activity with advancing gestational age (GA), likely due in part to loss of space (r = -.51, p < .001). Evaluation of individual variation in motor movement revealed a negative association between movement and the occurrence of coactivations within the left parietotemporal network, controlling for age and sex (p = .003). Further, we found that the occurrence of coactivations between the SMA to posterior brain regions, including visual cortex, was prospectively associated with postnatal motor function at 7 months (r = .43, p = .03). This is the first study to pair fetal movement and fMRI, highlighting potential for comparisons of fetal behavior and neural network development to enhance our understanding of fetal brain organization.
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Affiliation(s)
- Lanxin Ji
- Department of Child & Adolescent PsychiatryNew York University School of MedicineNew YorkNew YorkUSA
| | - Amyn Majbri
- Department of Child & Adolescent PsychiatryNew York University School of MedicineNew YorkNew YorkUSA
| | - Cassandra L. Hendrix
- Department of Child & Adolescent PsychiatryNew York University School of MedicineNew YorkNew YorkUSA
| | - Moriah E. Thomason
- Department of Child & Adolescent PsychiatryNew York University School of MedicineNew YorkNew YorkUSA
- Department of Population HealthNew York University School of MedicineNew YorkNew YorkUSA
- Neuroscience InstituteNew York University School of MedicineNew YorkNew YorkUSA
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RS-FetMRI: a MATLAB-SPM Based Tool for Pre-processing Fetal Resting-State fMRI Data. Neuroinformatics 2022; 20:1137-1154. [PMID: 35834105 DOI: 10.1007/s12021-022-09592-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/25/2022] [Indexed: 12/31/2022]
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) most recently has proved to open a measureless window on functional neurodevelopment in utero. Fetal brain activation and connectivity maps can be heavily influenced by 1) fetal-specific motion effects on the time-series and 2) the accuracy of time-series spatial normalization to a standardized gestational-week (GW) specific fetal template space.Due to the absence of a standardized and generalizable image processing protocol, the objective of the present work was to implement a validated fetal rs-fMRI preprocessing pipeline (RS-FetMRI) divided into 6 inter-dependent preprocessing modules (i.e., M1 to M6) and designed to work entirely as an extension for Statistical Parametric Mapping (SPM).RS-FetMRI pipeline output analyses on rs-fMRI time-series sampled from a cohort of fetuses acquired on both 1.5 T and 3 T MRI scanning systems showed increased efficacy of estimation of the degree of movement coupled with an efficient motion censoring procedure, resulting in increased number of motion-uncorrupted volumes and temporal continuity in fetal rs-fMRI time-series data. Moreover, a "structural-free" SPM-based spatial normalization procedure granted a high degree of spatial overlap with high reproducibility and a significant improvement in whole-brain and parcellation-specific Temporal Signal-to-Noise Ratio (TSNR) mirrored by functional connectivity analysis.To our knowledge, the RS-FetMRI pipeline is the first semi-automatic and easy-to-use standardized fetal rs-fMRI preprocessing pipeline completely integrated in MATLAB-SPM able to remove entry barriers for new research groups into the field of fetal rs-fMRI, for both research or clinical purposes, and ultimately to make future fetal brain connectivity investigations more suitable for comparison and cross-validation.
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9
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Scheinost D, Chang J, Lacadie C, Brennan-Wydra E, Foster R, Boxberger A, Macari S, Vernetti A, Constable RT, Ment LR, Chawarska K. Hypoconnectivity between anterior insula and amygdala associates with future vulnerabilities in social development in a neurodiverse sample of neonates. Sci Rep 2022; 12:16230. [PMID: 36171268 PMCID: PMC9517994 DOI: 10.1038/s41598-022-20617-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 09/15/2022] [Indexed: 11/25/2022] Open
Abstract
Altered resting state functional connectivity (FC) involving the anterior insula (aINS), a key node in the salience network, has been reported consistently in autism. Here we examined, for the first time, FC between the aINS and the whole brain in a sample of full-term, postmenstrual age (PMA) matched neonates (mean 44.0 weeks, SD = 1.5) who due to family history have high likelihood (HL) for developing autism (n = 12) and in controls (n = 41) without family history of autism (low likelihood, LL). Behaviors associated with autism were evaluated between 12 and 18 months (M = 17.3 months, SD = 2.5) in a subsample (25/53) of participants using the First Year Inventory (FYI). Compared to LL controls, HL neonates showed hypoconnectivity between left aINS and left amygdala. Lower connectivity between the two nodes was associated with higher FYI risk scores in the social domain (r(25) = -0.561, p = .003) and this association remained robust when maternal mental health factors were considered. Considering that a subsample of LL participants (n = 14/41) underwent brain imaging during the fetal period at PMA 31 and 34 weeks, in an exploratory analysis, we evaluated prospectively development of the LaINS-Lamy connectivity and found that the two areas strongly coactivate throughout the third trimester of pregnancy. The study identifies left lateralized anterior insula-amygdala connectivity as a potential target of further investigation into neural circuitry that enhances likelihood of future onset of social behaviors associated with autism during neonatal and potentially prenatal periods.
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Affiliation(s)
- Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Joseph Chang
- Department of Statistics and Data Science, Yale University, New Haven, CT, 06520, USA
| | - Cheryl Lacadie
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | | | - Rachel Foster
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
| | | | - Suzanne Macari
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Angelina Vernetti
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Laura R Ment
- Department of Pediatrics, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Katarzyna Chawarska
- Department of Statistics and Data Science, Yale University, New Haven, CT, 06520, USA.
- Department of Pediatrics, Yale School of Medicine, New Haven, CT, 06510, USA.
- Yale Child Study Center, Yale School of Medicine, 300 George Street, Suite 900, New Haven, CT, 06510, USA.
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10
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Scheinost D, Chang J, Lacadie C, Brennan-Wydra E, Constable RT, Chawarska K, Ment LR. Functional connectivity for the language network in the developing brain: 30 weeks of gestation to 30 months of age. Cereb Cortex 2022; 32:3289-3301. [PMID: 34875024 PMCID: PMC9340393 DOI: 10.1093/cercor/bhab415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 09/20/2021] [Accepted: 10/12/2021] [Indexed: 11/14/2022] Open
Abstract
Although the neural scaffolding for language is putatively present before birth, the maturation of functional connections among the key nodes of the language network, Broca's and Wernicke's areas, is less known. We leveraged longitudinal and cross-sectional data from three sites collected through six studies to track the development of functional circuits between Broca's and Wernicke's areas from 30 weeks of gestation through 30 months of age in 127 unique participants. Using resting-state fMRI data, functional connectivity was calculated as the correlation between fMRI time courses from pairs of regions, defined as Broca's and Wernicke's in both hemispheres. The primary analysis evaluated 23 individuals longitudinally imaged from 30 weeks postmenstrual age (fetal) through the first postnatal month (neonatal). A secondary analysis in 127 individuals extended these curves into older infants and toddlers. These data demonstrated significant growth of interhemispheric connections including left Broca's and its homolog and left Wernicke's and its homolog from 30 weeks of gestation through the first postnatal month. In contrast, intrahemispheric connections did not show significant increases across this period. These data represent an important baseline for language systems in the developing brain against which to compare those neurobehavioral disorders with the potential fetal onset of disease.
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Affiliation(s)
- Dustin Scheinost
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA
| | - Joseph Chang
- Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA
| | - Cheryl Lacadie
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
| | | | - R Todd Constable
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06510, USA
| | - Katarzyna Chawarska
- Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Pediatrics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Laura R Ment
- Department of Pediatrics, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT 06510, USA
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11
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Ji L, Hendrix CL, Thomason ME. Empirical evaluation of human fetal fMRI preprocessing steps. Netw Neurosci 2022; 6:702-721. [PMID: 36204420 PMCID: PMC9531599 DOI: 10.1162/netn_a_00254] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 05/09/2022] [Indexed: 11/04/2022] Open
Abstract
Increased study and methodological innovation have led to growth in the field of fetal brain fMRI. An important gap yet to be addressed is optimization of fetal fMRI preprocessing. Rapid developmental changes, imaged within the maternal compartment using an abdominal coil, introduce novel constraints that challenge established methods used in adult fMRI. This study evaluates the impact of (1) normalization to a group mean-age template versus normalization to an age-matched template; (2) independent components analysis (ICA) denoising at two criterion thresholds; and (3) smoothing using three kernel sizes. Data were collected from 121 fetuses (25-39 weeks, 43.8% female). Results indicate that the mean age template is superior in older fetuses, but less optimal in younger fetuses. ICA denoising at a more stringent threshold is superior to less stringent denoising. A larger smoothing kernel can enhance cross-hemisphere functional connectivity. Overall, this study provides improved understanding of the impact of specific steps on fetal image quality. Findings can be used to inform a common set of best practices for fetal fMRI preprocessing.
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Affiliation(s)
- Lanxin Ji
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA
| | - Cassandra L. Hendrix
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA
| | - Moriah E. Thomason
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA
- Department of Population Health, New York University School of Medicine, New York, NY, USA
- Neuroscience Institute, New York University School of Medicine, New York, NY, USA
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12
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Pollatou A, Filippi CA, Aydin E, Vaughn K, Thompson D, Korom M, Dufford AJ, Howell B, Zöllei L, Martino AD, Graham A, Scheinost D, Spann MN. An ode to fetal, infant, and toddler neuroimaging: Chronicling early clinical to research applications with MRI, and an introduction to an academic society connecting the field. Dev Cogn Neurosci 2022; 54:101083. [PMID: 35184026 PMCID: PMC8861425 DOI: 10.1016/j.dcn.2022.101083] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/17/2021] [Accepted: 02/04/2022] [Indexed: 12/14/2022] Open
Abstract
Fetal, infant, and toddler neuroimaging is commonly thought of as a development of modern times (last two decades). Yet, this field mobilized shortly after the discovery and implementation of MRI technology. Here, we provide a review of the parallel advancements in the fields of fetal, infant, and toddler neuroimaging, noting the shifts from clinical to research use, and the ongoing challenges in this fast-growing field. We chronicle the pioneering science of fetal, infant, and toddler neuroimaging, highlighting the early studies that set the stage for modern advances in imaging during this developmental period, and the large-scale multi-site efforts which ultimately led to the explosion of interest in the field today. Lastly, we consider the growing pains of the community and the need for an academic society that bridges expertise in developmental neuroscience, clinical science, as well as computational and biomedical engineering, to ensure special consideration of the vulnerable mother-offspring dyad (especially during pregnancy), data quality, and image processing tools that are created, rather than adapted, for the young brain.
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Affiliation(s)
- Angeliki Pollatou
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Courtney A Filippi
- Section on Development and Affective Neuroscience, National Institute of Mental Health, Bethesda, MD, USA; Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
| | - Ezra Aydin
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Kelly Vaughn
- Department of Pediatrics, University of Texas Health Sciences Center, Houston, TX, USA
| | - Deanne Thompson
- Clinical Sciences, Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Marta Korom
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - Alexander J Dufford
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Brittany Howell
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA; Department of Human Development and Family Science, Virginia Tech, Blacksburg, VA, USA
| | - Lilla Zöllei
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | - Alice Graham
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | | | - Dustin Scheinost
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Marisa N Spann
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA; Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA.
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13
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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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14
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De Asis-Cruz J, Krishnamurthy D, Jose C, Cook KM, Limperopoulos C. FetalGAN: Automated Segmentation of Fetal Functional Brain MRI Using Deep Generative Adversarial Learning and Multi-Scale 3D U-Net. Front Neurosci 2022; 16:887634. [PMID: 35747213 PMCID: PMC9209698 DOI: 10.3389/fnins.2022.887634] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/16/2022] [Indexed: 01/02/2023] Open
Abstract
An important step in the preprocessing of resting state functional magnetic resonance images (rs-fMRI) is the separation of brain from non-brain voxels. Widely used imaging tools such as FSL's BET2 and AFNI's 3dSkullStrip accomplish this task effectively in children and adults. In fetal functional brain imaging, however, the presence of maternal tissue around the brain coupled with the non-standard position of the fetal head limit the usefulness of these tools. Accurate brain masks are thus generated manually, a time-consuming and tedious process that slows down preprocessing of fetal rs-fMRI. Recently, deep learning-based segmentation models such as convolutional neural networks (CNNs) have been increasingly used for automated segmentation of medical images, including the fetal brain. Here, we propose a computationally efficient end-to-end generative adversarial neural network (GAN) for segmenting the fetal brain. This method, which we call FetalGAN, yielded whole brain masks that closely approximated the manually labeled ground truth. FetalGAN performed better than 3D U-Net model and BET2: FetalGAN, Dice score = 0.973 ± 0.013, precision = 0.977 ± 0.015; 3D U-Net, Dice score = 0.954 ± 0.054, precision = 0.967 ± 0.037; BET2, Dice score = 0.856 ± 0.084, precision = 0.758 ± 0.113. FetalGAN was also faster than 3D U-Net and the manual method (7.35 s vs. 10.25 s vs. ∼5 min/volume). To the best of our knowledge, this is the first successful implementation of 3D CNN with GAN on fetal fMRI brain images and represents a significant advance in fully automating processing of rs-MRI images.
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Affiliation(s)
- Josepheen De Asis-Cruz
- Developing Brain Institute, Department of Diagnostic Radiology, Children’s National Hospital, Washington, DC, United States
| | - Dhineshvikram Krishnamurthy
- Developing Brain Institute, Department of Diagnostic Radiology, Children’s National Hospital, Washington, DC, United States
| | - Chris Jose
- Department of Computer Science, University of Maryland, College Park, MD, United States
| | - Kevin M. Cook
- Developing Brain Institute, Department of Diagnostic Radiology, Children’s National Hospital, Washington, DC, United States
| | - Catherine Limperopoulos
- Developing Brain Institute, Department of Diagnostic Radiology, Children’s National Hospital, Washington, DC, United States
- *Correspondence: Catherine Limperopoulos,
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15
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Chen WF, Ou HY, Pan CT, Liao CC, Huang W, Lin HY, Cheng YF, Wei CP. Recognition Rate Advancement and Data Error Improvement of Pathology Cutting with H-DenseUNet for Hepatocellular Carcinoma Image. Diagnostics (Basel) 2021; 11:diagnostics11091599. [PMID: 34573941 PMCID: PMC8470617 DOI: 10.3390/diagnostics11091599] [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: 08/04/2021] [Revised: 08/29/2021] [Accepted: 08/29/2021] [Indexed: 11/16/2022] Open
Abstract
Due to the fact that previous studies have rarely investigated the recognition rate discrepancy and pathology data error when applied to different databases, the purpose of this study is to investigate the improvement of recognition rate via deep learning-based liver lesion segmentation with the incorporation of hospital data. The recognition model used in this study is H-DenseUNet, which is applied to the segmentation of the liver and lesions, and a mixture of 2D/3D Hybrid-DenseUNet is used to reduce the recognition time and system memory requirements. Differences in recognition results were determined by comparing the training files of the standard LiTS competition data set with the training set after mixing in an additional 30 patients. The average error value of 9.6% was obtained by comparing the data discrepancy between the actual pathology data and the pathology data after the analysis of the identified images imported from Kaohsiung Chang Gung Memorial Hospital. The average error rate of the recognition output after mixing the LiTS database with hospital data for training was 1%. In the recognition part, the Dice coefficient was 0.52 after training 50 epochs using the standard LiTS database, while the Dice coefficient was increased to 0.61 after adding 30 hospital data to the training. After importing 3D Slice and ITK-Snap software, a 3D image of the lesion and liver segmentation can be developed. It is hoped that this method could be used to stimulate more research in addition to the general public standard database in the future, as well as to study the applicability of hospital data and improve the generality of the database.
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Affiliation(s)
- Wen-Fan Chen
- Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 80424, Taiwan;
| | - Hsin-You Ou
- Liver Transplantation Program and Departments of Diagnostic Radiology, Surgery Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833401, Taiwan; (H.-Y.O.); (C.-C.L.)
| | - Cheng-Tang Pan
- Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan; (C.-T.P.); (W.H.); (H.-Y.L.)
| | - Chien-Chang Liao
- Liver Transplantation Program and Departments of Diagnostic Radiology, Surgery Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833401, Taiwan; (H.-Y.O.); (C.-C.L.)
| | - Wen Huang
- Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan; (C.-T.P.); (W.H.); (H.-Y.L.)
| | - Han-Yu Lin
- Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan; (C.-T.P.); (W.H.); (H.-Y.L.)
| | - Yu-Fan Cheng
- Liver Transplantation Program and Departments of Diagnostic Radiology, Surgery Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833401, Taiwan; (H.-Y.O.); (C.-C.L.)
- Correspondence: (Y.-F.C.); (C.-P.W.); Tel.: +886-773-17123-3027 (Y.-F.C.); +886-752-52000-4189 (C.-P.W.)
| | - Chia-Po Wei
- Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
- Correspondence: (Y.-F.C.); (C.-P.W.); Tel.: +886-773-17123-3027 (Y.-F.C.); +886-752-52000-4189 (C.-P.W.)
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