Estimating Bacterial Load in FCFM Imaging

Sohan Seth; Ahsan Akram; Kevin Dhaliwal; Christopher K I Williams

doi:10.1007/978-3-319-60964-5_79

Estimating Bacterial Load in FCFM Imaging

Sohan Seth, Ahsan Akram, Kevin Dhaliwal, Christopher K I Williams

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

We address the task of detecting bacteria and estimating bacterial load in the human distal lung with fibered confocal fluorescence microscopy (FCFM) and a targeted smartprobe. Bacteria appear as bright dots in the image when exposed to a smartprobe, but they are often diffcult to detect due to the presence of background auto fluorescence inherent to human lungs. In this study, we create a database of annotated image frames where a clinician has labelled bacteria, and use this database for supervised learning to build a suitable bacterial load
estimation software.

Original language	English
Title of host publication	Medical Image Understanding and Analysis. MIUA 2017.
Publisher	Springer
Pages	909-921
Number of pages	12
ISBN (Electronic)	978-3-319-60964-5
ISBN (Print)	978-3-319-60963-8
DOIs	https://doi.org/10.1007/978-3-319-60964-5_79
Publication status	Published - 22 Jun 2017
Event	Medical Image Understanding and Analysis (MIUA 2017) - Edinburgh, United Kingdom Duration: 11 Jul 2017 → 13 Jul 2017

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer, Cham
Volume	723
ISSN (Print)	1865-0929

Conference

Conference	Medical Image Understanding and Analysis (MIUA 2017)
Country/Territory	United Kingdom
City	Edinburgh
Period	11/07/17 → 13/07/17

Access to Document

10.1007/978-3-319-60964-5_79

Seth et al 2017 Estimating Bacterial LoadAccepted author manuscript, 2.62 MB

https://link.springer.com/chapter/10.1007%2F978-3-319-60964-5_79

Cite this

@inproceedings{c35b9add78854ae0aff83c635caedc60,

title = "Estimating Bacterial Load in FCFM Imaging",

abstract = "We address the task of detecting bacteria and estimating bacterial load in the human distal lung with fibered confocal fluorescence microscopy (FCFM) and a targeted smartprobe. Bacteria appear as bright dots in the image when exposed to a smartprobe, but they are often diffcult to detect due to the presence of background auto fluorescence inherent to human lungs. In this study, we create a database of annotated image frames where a clinician has labelled bacteria, and use this database for supervised learning to build a suitable bacterial loadestimation software.",

author = "Sohan Seth and Ahsan Akram and Kevin Dhaliwal and Williams, {Christopher K I}",

year = "2017",

month = jun,

day = "22",

doi = "10.1007/978-3-319-60964-5_79",

language = "English",

isbn = "978-3-319-60963-8",

series = "Communications in Computer and Information Science",

publisher = "Springer",

pages = "909--921",

booktitle = "Medical Image Understanding and Analysis. MIUA 2017.",

note = "Medical Image Understanding and Analysis (MIUA 2017) ; Conference date: 11-07-2017 Through 13-07-2017",

}

Seth, S , Akram, A, Dhaliwal, K & Williams, CKI 2017, Estimating Bacterial Load in FCFM Imaging. in Medical Image Understanding and Analysis. MIUA 2017.. Communications in Computer and Information Science, vol. 723, Springer, pp. 909-921, Medical Image Understanding and Analysis (MIUA 2017), Edinburgh, United Kingdom, 11/07/17. https://doi.org/10.1007/978-3-319-60964-5_79

TY - GEN

T1 - Estimating Bacterial Load in FCFM Imaging

AU - Seth, Sohan

AU - Akram, Ahsan

AU - Dhaliwal, Kevin

AU - Williams, Christopher K I

PY - 2017/6/22

Y1 - 2017/6/22

N2 - We address the task of detecting bacteria and estimating bacterial load in the human distal lung with fibered confocal fluorescence microscopy (FCFM) and a targeted smartprobe. Bacteria appear as bright dots in the image when exposed to a smartprobe, but they are often diffcult to detect due to the presence of background auto fluorescence inherent to human lungs. In this study, we create a database of annotated image frames where a clinician has labelled bacteria, and use this database for supervised learning to build a suitable bacterial loadestimation software.

AB - We address the task of detecting bacteria and estimating bacterial load in the human distal lung with fibered confocal fluorescence microscopy (FCFM) and a targeted smartprobe. Bacteria appear as bright dots in the image when exposed to a smartprobe, but they are often diffcult to detect due to the presence of background auto fluorescence inherent to human lungs. In this study, we create a database of annotated image frames where a clinician has labelled bacteria, and use this database for supervised learning to build a suitable bacterial loadestimation software.

UR - https://miua2017.wordpress.com/

U2 - 10.1007/978-3-319-60964-5_79

DO - 10.1007/978-3-319-60964-5_79

M3 - Conference contribution

SN - 978-3-319-60963-8

T3 - Communications in Computer and Information Science

SP - 909

EP - 921

BT - Medical Image Understanding and Analysis. MIUA 2017.

PB - Springer

T2 - Medical Image Understanding and Analysis (MIUA 2017)

Y2 - 11 July 2017 through 13 July 2017

ER -

Estimating Bacterial Load in FCFM Imaging

Abstract / Description of output

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Multiplexed 'Touch and Tell' Optical Molecular Sensing and Imaging

Cite this

Estimating Bacterial Load in FCFM Imaging

Abstract / Description of output

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Projects

Multiplexed 'Touch and Tell' Optical Molecular Sensing and Imaging

Cite this