Recovering Physiological Changes in Nasal Anatomy with Confidence Estimates

Ayushi Sinha, Xingtong Liu, Masaru Ishii, Gregory D. Hager, Russell H. Taylor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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).

Original languageEnglish (US)
Title of host publicationUncertainty 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
EditorsHayit Greenspan, Ryutaro Tanno, Marius Erdt, Tal Arbel, Christian Baumgartner, Adrian Dalca, Carole H. Sudre, William M. Wells, Klaus Drechsler, Marius Erdt, Marius George Linguraru, Raj Shekhar, Cristina Oyarzun Laura, Stefan Wesarg, Miguel Ángel González Ballester
PublisherSpringer
Pages115-124
Number of pages10
ISBN (Print)9783030326883
DOIs
StatePublished - Jan 1 2019
Event1st 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 - Shenzhen, China
Duration: Oct 17 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11840 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st 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
CountryChina
CityShenzhen
Period10/17/1910/17/19

    Fingerprint

Keywords

  • Confidence
  • Deformable registration
  • Statistical shape models

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Sinha, A., Liu, X., Ishii, M., Hager, G. D., & Taylor, R. H. (2019). Recovering Physiological Changes in Nasal Anatomy with Confidence Estimates. In H. Greenspan, R. Tanno, M. Erdt, T. Arbel, C. Baumgartner, A. Dalca, C. H. Sudre, W. M. Wells, K. Drechsler, M. Erdt, M. G. Linguraru, R. Shekhar, C. Oyarzun Laura, S. Wesarg, & M. Á. González Ballester (Eds.), 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 (pp. 115-124). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11840 LNCS). Springer. https://doi.org/10.1007/978-3-030-32689-0_12