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    Automated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach | American Journal of Neuroradiology  
    



















    

































    
  
  




    
    

  

      


    

      

  
  
  

    

  

    

  

                
    

      

  

    

  
      
  
  

    


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Research ArticlePediatrics

  
      
Open Access

  
      
Automated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach
  
    	
L. Zhao, J.D. Asis-Cruz, X. Feng, Y. Wu, K. Kapse, A. Largent, J. Quistorff, C. Lopez, D. Wu, K. Qing, C. Meyer and C. Limperopoulos

  
    	
American Journal of Neuroradiology March 2022,  43 (3) 448-454; DOI: https://doi.org/10.3174/ajnr.A7419 

  
  
  



  


  
  



			

		

	
	
 	
	  
  

		
		

			

			  

  
      
  
  

    
  


  
  



  
      
  
  

    

  

    

  
      
  
  

    

	  

		
		

			

			  

  
        
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vol. 43 no. 3 448-454 

  
  
  



  


  
  



			

		

	
	
 	
	  
  

		
		

			

			  

  
      
  
  

    
DOI 
https://doi.org/10.3174/ajnr.A7419
  


  
  



  
      
  
  

    
PubMed 
35177547
  


  
  



  
      
  
  

    
  


  
  



  
      
  
  

    
Published By 
American Journal of Neuroradiology
  


  
  



  
      
  
  

    
Print ISSN 
0195-6108
  


  
  



  
      
  
  

    
Online ISSN 
1936-959X
  


  
  



  
      
  
  

    
History 
  • Received  August 25, 2021
  • Accepted after revision  December 6, 2021
  • Published online March 10, 2022.
  


  
  



  
      
  
  

    
  


  
  



  
        
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Copyright & Usage 
© 2022 by American Journal of Neuroradiology Indicates open access to non-subscribers at www.ajnr.org
  


  
  



  
      
  
  

    
  


  
  



			

		

	
	
 	
	
	


  


  
  



  
        
Author Information

    
  
  

    
  1. L. Zhaoa,b, 
  2. J.D. Asis-Cruza, 
  3. X. Fengc, 
  4. Y. Wua, 
  5. K. Kapsea, 
  6. A. Largenta, 
  7. J. Quistorffa, 
  8. C. Lopeza, 
  9. D. Wub, 
  10. K. Qingd, 
  11. C. Meyerc and 
  12. C. Limperopoulosa
  1. aFrom the Department of Diagnostic Imaging and Radiology (L.Z., J.D.A.-C., Y.W., K.K., A.L., J.Q., C. Lopez, C. Limperopoulos), Developing Brain Institute, Children’s National, Washington, DC
  2. bDepartment of Biomedical Engineering (L.Z., D.W.), Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, China
  3. cDepartment of Biomedical Engineering (X.F., C.M.), University of Virginia, Charlottesville, Virginia
  4. dDepartment of Radiation Oncology (K.Q.), City of Hope National Center, Duarte, California
  1. Please address correspondence to Catherine Limperopoulos, PhD, Children’s National Medical Center, 111 Michigan Ave NW, Washington, DC 20010; e-mail: climpero{at}childrensnational.org
View Full Text
  


  
  



  
        
Funding

    
  
  

    
  • National Institute of Biomedical Imaging and Bioengineering 
    R21EB022309
  • National Center for Advancing Translation Sciences
    UL1TR001876
  • National Heart, Lung and Blood Institute
    R01HL116585
  


  
  



  
        
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Automated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach
  
    	
L. Zhao, J.D. Asis-Cruz, X. Feng, Y. Wu, K. Kapse, A. Largent, J. Quistorff, C. Lopez, D. Wu, K. Qing, C. Meyer, C. Limperopoulos

  
    	
 American Journal of Neuroradiology Mar 2022, 43 (3) 448-454; DOI: 10.3174/ajnr.A7419 

  
  
  



  


  
  



  
      
  
  

    

  
    
  
  
    
  
  
    
  


  


  
  



  
      
  
  

    
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