TY - GEN
T1 - Recovering Physiological Changes in Nasal Anatomy with Confidence Estimates
AU - Sinha, Ayushi
AU - Liu, Xingtong
AU - Ishii, Masaru
AU - Hager, Gregory D.
AU - Taylor, Russell H.
N1 - Funding Information:
Acknowledgment. This work was funded by the Johns Hopkins University (JHU) Provost’s Postdoctoral Fellowship and other JHU internal funds. We would also like to thank Seth D. Billings for his invaluable feedback.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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).
AB - 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).
KW - Confidence
KW - Deformable registration
KW - Statistical shape models
UR - http://www.scopus.com/inward/record.url?scp=85075739439&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075739439&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32689-0_12
DO - 10.1007/978-3-030-32689-0_12
M3 - Conference contribution
AN - SCOPUS:85075739439
SN - 9783030326883
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 115
EP - 124
BT - Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures - 1st International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, Held in Conjunction with MICCAI 2019, Proceedings
A2 - Greenspan, Hayit
A2 - Tanno, Ryutaro
A2 - Erdt, Marius
A2 - Arbel, Tal
A2 - Baumgartner, Christian
A2 - Dalca, Adrian
A2 - Sudre, Carole H.
A2 - Wells, William M.
A2 - Drechsler, Klaus
A2 - Erdt, Marius
A2 - Linguraru, Marius George
A2 - Shekhar, Raj
A2 - Oyarzun Laura, Cristina
A2 - Wesarg, Stefan
A2 - González Ballester, Miguel Ángel
PB - Springer
T2 - 1st International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 17 October 2019 through 17 October 2019
ER -