Chapter title |
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016
|
---|---|
Chapter number | 16 |
Book title |
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016
|
Published in |
Lecture notes in computer science, October 2016
|
DOI | 10.1007/978-3-319-46726-9_16 |
Pubmed ID | |
Book ISBNs |
978-3-31-946725-2, 978-3-31-946726-9
|
Authors |
Seth D. Billings, Ayushi Sinha, Austin Reiter, Simon Leonard, Masaru Ishii, Gregory D. Hager, Russell H. Taylor |
Editors |
Sebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells |
Abstract |
Functional endoscopic sinus surgery (FESS) is a surgical procedure used to treat acute cases of sinusitis and other sinus diseases. FESS is fast becoming the preferred choice of treatment due to its minimally invasive nature. However, due to the limited field of view of the endoscope, surgeons rely on navigation systems to guide them within the nasal cavity. State of the art navigation systems report registration accuracy of over 1mm, which is large compared to the size of the nasal airways. We present an anatomically constrained video-CT registration algorithm that incorporates multiple video features. Our algorithm is robust in the presence of outliers. We also test our algorithm on simulated and in-vivo data, and test its accuracy against degrading initializations. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 26 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 9 | 35% |
Researcher | 4 | 15% |
Other | 2 | 8% |
Student > Doctoral Student | 2 | 8% |
Student > Master | 2 | 8% |
Other | 2 | 8% |
Unknown | 5 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 9 | 35% |
Engineering | 9 | 35% |
Medicine and Dentistry | 2 | 8% |
Chemical Engineering | 1 | 4% |
Unknown | 5 | 19% |