Name
Abstract | ||
|---|---|---|
Between preoperative computed tomography (CT) image acquisition and endoscopic sinus surgery, the nasal cavity of a patient undergoes changes. These changes make it challenging for non-deformable vision-based registration algorithms to find accurate alignments between CT image and intraoperative video. Large alignment errors can lead to injuries to critical structures. In this paper, we present a deformable video-CT registration that deforms the patient shape extracted from CT according to statistics learned from population. We also associate confidence with regions of deformed shapes based on the location of matched video features. Experiments on both simulation and in vivo data produced < 1 mm errors (statistically significantly lower than prior work). |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-32689-0_12 | UNCERTAINTY FOR SAFE UTILIZATION OF MACHINE LEARNING IN MEDICAL IMAGING AND CLINICAL IMAGE-BASED PROCEDURES |
Keywords | DocType | Volume |
Statistical shape models, Deformable registration, Confidence | Conference | 11840 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ayushi Sinha | 1 | 24 | 6.72 |
| Xingtong Liu | 2 | 13 | 5.02 |
| Masaru Ishii | 3 | 0 | 0.68 |
| Hager Gregory D | 4 | 1946 | 159.37 |
| Russell H. Taylor | 5 | 1970 | 438.00 |