Interactive Whole-Heart Segmentation in Congenital Heart Disease

被引:92
作者
Pace, Danielle F. [1 ]
Dalca, Adrian V. [1 ]
Geva, Tal [2 ,3 ]
Powell, Andrew J. [2 ,3 ]
Moghari, Mehdi H. [2 ,3 ]
Golland, Polina [1 ]
机构
[1] MIT, Comp Sci & Artificial Intelligence Lab, Cambridge, MA 02139 USA
[2] Boston Childrens Hosp, Dept Cardiol, Boston, MA USA
[3] Harvard Univ, Sch Med, Dept Pediat, Boston, MA 02115 USA
来源
MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION, PT III | 2015年 / 9351卷
关键词
D O I
10.1007/978-3-319-24574-4_10
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We present an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease. Accurate whole-heart segmentation is necessary to create patient-specific 3D heart models for surgical planning in the presence of complex heart defects. Anatomical variability due to congenital defects precludes fully automatic atlas-based segmentation. Our interactive segmentation method exploits expert segmentations of a small set of short-axis slice regions to automatically delineate the remaining volume using patch-based segmentation. We also investigate the potential of active learning to automatically solicit user input in areas where segmentation error is likely to be high. Validation is performed on four subjects with double outlet right ventricle, a severe congenital heart defect. We show that strategies asking the user to manually segment regions of interest within short-axis slices yield higher accuracy with less user input than those querying entire short-axis slices.
引用
收藏
页码:80 / 88
页数:9
相关论文
共 15 条
  • [1] Reusability of Statistical Shape Models for the Segmentation of Severely Abnormal Hearts
    Alba, Xenia
    Lekadir, Karim
    Hoogendoorn, Corne
    Pereanez, Marco
    Swift, Andrew J.
    Wild, Jim M.
    Frangi, Alejandro F.
    [J]. Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, 2015, 8896 : 257 - 264
  • [2] [Anonymous], 2012, Synthesis Lectures on Artificial Intelligence and Machine Learning
  • [3] An active learning approach for stroke lesion segmentation on multimodal MRI data
    Chyzhyk, Darya
    Dacosta-Aguayo, Rosalia
    Mataro, Maria
    Grana, Manuel
    [J]. NEUROCOMPUTING, 2015, 150 : 26 - 36
  • [4] Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation
    Coupe, Pierrick
    Manjon, Jose V.
    Fonov, Vladimir
    Pruessner, Jens
    Robles, Montserrat
    Collins, D. Louis
    [J]. NEUROIMAGE, 2011, 54 (02) : 940 - 954
  • [5] Jacobs Stephan, 2008, Interact Cardiovasc Thorac Surg, V7, P6, DOI 10.1510/icvts.2007.156588
  • [6] Mahapatra D, 2013, LECT NOTES COMPUT SC, V8150, P214, DOI 10.1007/978-3-642-40763-5_27
  • [7] A Supervised Patch-Based Approach for Human Brain Labeling
    Rousseau, Franccois
    Habas, Piotr A.
    Studholme, Colin
    [J]. IEEE TRANSACTIONS ON MEDICAL IMAGING, 2011, 30 (10) : 1852 - 1862
  • [8] Three-dimensional printing in cardiac surgery and interventional cardiology: a single-centre experience
    Schmauss, Daniel
    Haeberle, Sandra
    Hagl, Christian
    Sodian, Ralf
    [J]. EUROPEAN JOURNAL OF CARDIO-THORACIC SURGERY, 2015, 47 (06) : 1044 - 1052
  • [9] Shi WZ, 2011, LECT NOTES COMPUT SC, V6666, P163, DOI 10.1007/978-3-642-21028-0_21
  • [10] Top A, 2011, LECT NOTES COMPUT SC, V6893, P603, DOI 10.1007/978-3-642-23626-6_74