Method for endobronchial video parsing

Ziv R. Yaniv, Patrick D. Byrnes, William E. Higgins

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

1 Citation (Scopus)

Abstract

Endoscopic examination of the lungs during bronchoscopy produces a considerable amount of endobronchial video. A physician uses the video stream as a guide to navigate the airway tree for various purposes such as general airway examinations, collecting tissue samples, or administering disease treatment. Aside from its intraoperative utility, the recorded video provides high-resolution detail of the airway mucosal surfaces and a record of the endoscopic procedure. Unfortunately, due to a lack of robust automatic video-analysis methods to summarize this immense data source, it is essentially discarded after the procedure. To address this problem, we present a fully-automatic method for parsing endobronchial video for the purpose of summarization. Endoscopic-shot segmentation is first performed to parse the video sequence into structurally similar groups according to a geometric model. Bronchoscope-motion analysis then identifies motion sequences performed during bronchoscopy and extracts relevant information. Finally, representative key frames are selected based on the derived motion information to present a drastically reduced summary of the processed video. The potential of our method is demonstrated on four endobronchial video sequences from both phantom and human data. Preliminary tests show that, on average, our method reduces the number of frames required to represent an input video sequence by approximately 96% and consistently selects salient key frames appropriately distributed throughout the video sequence, enabling quick and accurate post-operative review of the endoscopic examination.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2016
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsRobert J. Webster, Ziv R. Yaniv
PublisherSPIE
ISBN (Electronic)9781510600218
DOIs
StatePublished - Jan 1 2016
EventMedical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling - San Diego, United States
Duration: Feb 28 2016Mar 1 2016

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9786
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling
CountryUnited States
CitySan Diego
Period2/28/163/1/16

Fingerprint

examination
physicians
Bronchoscopy
lungs
shot
Tissue
Bronchoscopes
Information Storage and Retrieval
high resolution
Physicians
Lung
Motion analysis
Therapeutics

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Cite this

Yaniv, Z. R., Byrnes, P. D., & Higgins, W. E. (2016). Method for endobronchial video parsing. In R. J. Webster, & Z. R. Yaniv (Eds.), Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling [97861B] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9786). SPIE. https://doi.org/10.1117/12.2208939
Yaniv, Ziv R. ; Byrnes, Patrick D. ; Higgins, William E. / Method for endobronchial video parsing. Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling. editor / Robert J. Webster ; Ziv R. Yaniv. SPIE, 2016. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
@inproceedings{20d629c2f83e48b9815d13c6dcb779b8,
title = "Method for endobronchial video parsing",
abstract = "Endoscopic examination of the lungs during bronchoscopy produces a considerable amount of endobronchial video. A physician uses the video stream as a guide to navigate the airway tree for various purposes such as general airway examinations, collecting tissue samples, or administering disease treatment. Aside from its intraoperative utility, the recorded video provides high-resolution detail of the airway mucosal surfaces and a record of the endoscopic procedure. Unfortunately, due to a lack of robust automatic video-analysis methods to summarize this immense data source, it is essentially discarded after the procedure. To address this problem, we present a fully-automatic method for parsing endobronchial video for the purpose of summarization. Endoscopic-shot segmentation is first performed to parse the video sequence into structurally similar groups according to a geometric model. Bronchoscope-motion analysis then identifies motion sequences performed during bronchoscopy and extracts relevant information. Finally, representative key frames are selected based on the derived motion information to present a drastically reduced summary of the processed video. The potential of our method is demonstrated on four endobronchial video sequences from both phantom and human data. Preliminary tests show that, on average, our method reduces the number of frames required to represent an input video sequence by approximately 96{\%} and consistently selects salient key frames appropriately distributed throughout the video sequence, enabling quick and accurate post-operative review of the endoscopic examination.",
author = "Yaniv, {Ziv R.} and Byrnes, {Patrick D.} and Higgins, {William E.}",
year = "2016",
month = "1",
day = "1",
doi = "10.1117/12.2208939",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Webster, {Robert J.} and Yaniv, {Ziv R.}",
booktitle = "Medical Imaging 2016",
address = "United States",

}

Yaniv, ZR, Byrnes, PD & Higgins, WE 2016, Method for endobronchial video parsing. in RJ Webster & ZR Yaniv (eds), Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling., 97861B, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 9786, SPIE, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, United States, 2/28/16. https://doi.org/10.1117/12.2208939

Method for endobronchial video parsing. / Yaniv, Ziv R.; Byrnes, Patrick D.; Higgins, William E.

Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling. ed. / Robert J. Webster; Ziv R. Yaniv. SPIE, 2016. 97861B (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9786).

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

TY - GEN

T1 - Method for endobronchial video parsing

AU - Yaniv, Ziv R.

AU - Byrnes, Patrick D.

AU - Higgins, William E.

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Endoscopic examination of the lungs during bronchoscopy produces a considerable amount of endobronchial video. A physician uses the video stream as a guide to navigate the airway tree for various purposes such as general airway examinations, collecting tissue samples, or administering disease treatment. Aside from its intraoperative utility, the recorded video provides high-resolution detail of the airway mucosal surfaces and a record of the endoscopic procedure. Unfortunately, due to a lack of robust automatic video-analysis methods to summarize this immense data source, it is essentially discarded after the procedure. To address this problem, we present a fully-automatic method for parsing endobronchial video for the purpose of summarization. Endoscopic-shot segmentation is first performed to parse the video sequence into structurally similar groups according to a geometric model. Bronchoscope-motion analysis then identifies motion sequences performed during bronchoscopy and extracts relevant information. Finally, representative key frames are selected based on the derived motion information to present a drastically reduced summary of the processed video. The potential of our method is demonstrated on four endobronchial video sequences from both phantom and human data. Preliminary tests show that, on average, our method reduces the number of frames required to represent an input video sequence by approximately 96% and consistently selects salient key frames appropriately distributed throughout the video sequence, enabling quick and accurate post-operative review of the endoscopic examination.

AB - Endoscopic examination of the lungs during bronchoscopy produces a considerable amount of endobronchial video. A physician uses the video stream as a guide to navigate the airway tree for various purposes such as general airway examinations, collecting tissue samples, or administering disease treatment. Aside from its intraoperative utility, the recorded video provides high-resolution detail of the airway mucosal surfaces and a record of the endoscopic procedure. Unfortunately, due to a lack of robust automatic video-analysis methods to summarize this immense data source, it is essentially discarded after the procedure. To address this problem, we present a fully-automatic method for parsing endobronchial video for the purpose of summarization. Endoscopic-shot segmentation is first performed to parse the video sequence into structurally similar groups according to a geometric model. Bronchoscope-motion analysis then identifies motion sequences performed during bronchoscopy and extracts relevant information. Finally, representative key frames are selected based on the derived motion information to present a drastically reduced summary of the processed video. The potential of our method is demonstrated on four endobronchial video sequences from both phantom and human data. Preliminary tests show that, on average, our method reduces the number of frames required to represent an input video sequence by approximately 96% and consistently selects salient key frames appropriately distributed throughout the video sequence, enabling quick and accurate post-operative review of the endoscopic examination.

UR - http://www.scopus.com/inward/record.url?scp=84982144067&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84982144067&partnerID=8YFLogxK

U2 - 10.1117/12.2208939

DO - 10.1117/12.2208939

M3 - Conference contribution

AN - SCOPUS:84982144067

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2016

A2 - Webster, Robert J.

A2 - Yaniv, Ziv R.

PB - SPIE

ER -

Yaniv ZR, Byrnes PD, Higgins WE. Method for endobronchial video parsing. In Webster RJ, Yaniv ZR, editors, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling. SPIE. 2016. 97861B. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2208939