Chapter title |
Interactive Whole-Heart Segmentation in Congenital Heart Disease.
|
---|---|
Chapter number | 10 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, January 2015
|
DOI | 10.1007/978-3-319-24574-4_10 |
Pubmed ID | |
Book ISBNs |
978-3-31-924573-7, 978-3-31-924574-4
|
Authors |
Danielle F. Pace, Adrian V. Dalca, Tal Geva, Andrew J. Powell, Mehdi H. Moghari, Polina Golland, Pace, Danielle F., Dalca, Adrian V., Geva, Tal, Powell, Andrew J., Moghari, Mehdi H., Golland, Polina |
Editors |
Nassir Navab, Joachim Hornegger, William M. Wells, Alejandro F. Frangi |
Abstract |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 2% |
Unknown | 47 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 13 | 27% |
Student > Master | 8 | 17% |
Researcher | 5 | 10% |
Student > Doctoral Student | 3 | 6% |
Other | 3 | 6% |
Other | 6 | 13% |
Unknown | 10 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 11 | 23% |
Engineering | 10 | 21% |
Medicine and Dentistry | 8 | 17% |
Mathematics | 1 | 2% |
Agricultural and Biological Sciences | 1 | 2% |
Other | 3 | 6% |
Unknown | 14 | 29% |