Chapter title |
Locally Affine Diffeomorphic Surface Registration for Planning of Metopic Craniosynostosis Surgery
|
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Chapter number | 54 |
Book title |
Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, September 2017
|
DOI | 10.1007/978-3-319-66185-8_54 |
Pubmed ID | |
Book ISBNs |
978-3-31-966184-1, 978-3-31-966185-8
|
Authors |
Antonio R. Porras, Beatriz Paniagua, Andinet Enquobahrie, Scott Ensel, Hina Shah, Robert Keating, Gary F. Rogers, Marius George Linguraru |
Abstract |
The outcome of cranial vault reconstruction for the surgical treatment of craniosynostosis heavily depends on the surgeon's expertise because of the lack of an objective target shape. We introduce a surface-based diffeomorphic registration framework to create the optimal post-surgical cranial shape during craniosynostosis treatment. Our framework estimates and labels where each bone piece needs to be cut using a reference template. Then, it calculates how much each bone piece needs to be translated and in which direction, using the closest normal shape from a multi-atlas as a reference. With our locally affine approach, the method also allows for bone bending, modeling independently the transformation of each bone piece while ensuring the consistency of the global transformation. We evaluated the optimal plan for 15 patients with metopic craniosynostosis. Our results showed that the automated surgical planning creates cranial shapes with a reduction in cranial malformations of 51.43% and curvature discrepancies of 35.09%, which are the two indices proposed in the literature to quantify cranial deformities objectively. In addition, the cranial shapes created were within healthy ranges. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 21 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 4 | 19% |
Student > Postgraduate | 3 | 14% |
Student > Ph. D. Student | 3 | 14% |
Other | 2 | 10% |
Student > Doctoral Student | 2 | 10% |
Other | 3 | 14% |
Unknown | 4 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 7 | 33% |
Engineering | 6 | 29% |
Computer Science | 2 | 10% |
Arts and Humanities | 1 | 5% |
Unknown | 5 | 24% |