Etkilesimli Ögrenme ile Akciger Tomografi Hacim Taramalarinda Nodül Tespiti

Ilker Cam; F. Boray Tek

doi:10.1109/SIU.2016.7496166

Etkilesimli Ögrenme ile Akciger Tomografi Hacim Taramalarinda Nodül Tespiti

Translated title of the contribution: Interactive learning based nodule detection in ct lung volumes

Ilker Cam
, F. Boray Tek

Isik University

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

1 Citation (Scopus)

Abstract

We present a novel method to automatically detect lung nodules in CT lung scans. Our method is generalized in the sense that it does not assume/depend a particular organ or a particular nodule type. hence it does not require an organ segmentation. We test our method in a challenging set (Anode09) that is comprised of low dose CT scans which include all types of nodules (solid, ground glass opacity, juxta-fissural, juxta-vascular) of less than 10mm in size. Our method produces 8 false positives per scan for true positive rate of 52%, which is comparable to the first 6 results from the contest.

Translated title of the contribution	Interactive learning based nodule detection in ct lung volumes
Original language	Turkish
Title of host publication	2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2021-2024
Number of pages	4
ISBN (Electronic)	9781509016792
DOIs	https://doi.org/10.1109/SIU.2016.7496166
Publication status	Published - 20 Jun 2016
Externally published	Yes
Event	24th Signal Processing and Communication Application Conference, SIU 2016 - Zonguldak, Turkey Duration: 16 May 2016 → 19 May 2016

Publication series

Name	2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings

Conference

Conference	24th Signal Processing and Communication Application Conference, SIU 2016
Country/Territory	Turkey
City	Zonguldak
Period	16/05/16 → 19/05/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Access to Document

10.1109/SIU.2016.7496166

Cite this

Cam, I., & Tek, F. B. (2016). Etkilesimli Ögrenme ile Akciger Tomografi Hacim Taramalarinda Nodül Tespiti. In 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings (pp. 2021-2024). Article 7496166 (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2016.7496166

Cam, Ilker ; Tek, F. Boray. / Etkilesimli Ögrenme ile Akciger Tomografi Hacim Taramalarinda Nodül Tespiti. 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 2021-2024 (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings).

@inproceedings{c2d64c62017a447eaf5a529ac1c0b6b5,

title = "Etkilesimli {\"O}grenme ile Akciger Tomografi Hacim Taramalarinda Nod{\"u}l Tespiti",

abstract = "We present a novel method to automatically detect lung nodules in CT lung scans. Our method is generalized in the sense that it does not assume/depend a particular organ or a particular nodule type. hence it does not require an organ segmentation. We test our method in a challenging set (Anode09) that is comprised of low dose CT scans which include all types of nodules (solid, ground glass opacity, juxta-fissural, juxta-vascular) of less than 10mm in size. Our method produces 8 false positives per scan for true positive rate of 52\%, which is comparable to the first 6 results from the contest.",

keywords = "computed tomography, interactive segmentation, lung, nodule detection",

author = "Ilker Cam and Tek, \{F. Boray\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 24th Signal Processing and Communication Application Conference, SIU 2016 ; Conference date: 16-05-2016 Through 19-05-2016",

year = "2016",

month = jun,

day = "20",

doi = "10.1109/SIU.2016.7496166",

language = "T{\"u}rk{\c c}e",

series = "2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2021--2024",

booktitle = "2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings",

address = "United States",

}

Cam, I & Tek, FB 2016, Etkilesimli Ögrenme ile Akciger Tomografi Hacim Taramalarinda Nodül Tespiti. in 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings., 7496166, 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 2021-2024, 24th Signal Processing and Communication Application Conference, SIU 2016, Zonguldak, Turkey, 16/05/16. https://doi.org/10.1109/SIU.2016.7496166

Etkilesimli Ögrenme ile Akciger Tomografi Hacim Taramalarinda Nodül Tespiti. / Cam, Ilker; Tek, F. Boray.
2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2021-2024 7496166 (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings).

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

TY - GEN

T1 - Etkilesimli Ögrenme ile Akciger Tomografi Hacim Taramalarinda Nodül Tespiti

AU - Cam, Ilker

AU - Tek, F. Boray

PY - 2016/6/20

Y1 - 2016/6/20

N2 - We present a novel method to automatically detect lung nodules in CT lung scans. Our method is generalized in the sense that it does not assume/depend a particular organ or a particular nodule type. hence it does not require an organ segmentation. We test our method in a challenging set (Anode09) that is comprised of low dose CT scans which include all types of nodules (solid, ground glass opacity, juxta-fissural, juxta-vascular) of less than 10mm in size. Our method produces 8 false positives per scan for true positive rate of 52%, which is comparable to the first 6 results from the contest.

AB - We present a novel method to automatically detect lung nodules in CT lung scans. Our method is generalized in the sense that it does not assume/depend a particular organ or a particular nodule type. hence it does not require an organ segmentation. We test our method in a challenging set (Anode09) that is comprised of low dose CT scans which include all types of nodules (solid, ground glass opacity, juxta-fissural, juxta-vascular) of less than 10mm in size. Our method produces 8 false positives per scan for true positive rate of 52%, which is comparable to the first 6 results from the contest.

KW - computed tomography

KW - interactive segmentation

KW - lung

KW - nodule detection

UR - https://www.scopus.com/pages/publications/84982785232

U2 - 10.1109/SIU.2016.7496166

DO - 10.1109/SIU.2016.7496166

M3 - Konferans katkısı

AN - SCOPUS:84982785232

T3 - 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings

SP - 2021

EP - 2024

BT - 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 24th Signal Processing and Communication Application Conference, SIU 2016

Y2 - 16 May 2016 through 19 May 2016

ER -

Cam I, Tek FB. Etkilesimli Ögrenme ile Akciger Tomografi Hacim Taramalarinda Nodül Tespiti. In 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2021-2024. 7496166. (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings). doi: 10.1109/SIU.2016.7496166

Etkilesimli Ögrenme ile Akciger Tomografi Hacim Taramalarinda Nodül Tespiti

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