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Oda, H.* ; Roth, H.R.* ; Bhatia, K.K.* ; Oda, M.* ; Kitasaka, T.* ; Iwano, S.* ; Homma, H.* ; Takabatake, H.* ; Mori, M.* ; Natori, H.* ; Schnabel, J.A.* ; Mori, K.*

Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images.

Proc. SPIE 10575 (2018)
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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
We propose a novel mediastinal lymph node detection and segmentation method from chest CT volumes based on fully convolutional networks (FCNs). Most lymph node detection methods are based on filters for blob-like structures, which are not specific for lymph nodes. The 3D U-Net is a recent example of the state-of-the-art 3D FCNs. The 3D U-Net can be trained to learn appearances of lymph nodes in order to output lymph node likelihood maps on input CT volumes. However, it is prone to oversegmentation of each lymph node due to the strong data imbalance between lymph nodes and the remaining part of the CT volumes. To moderate the balance of sizes between the target classes, we train the 3D U-Net using not only lymph node annotations but also other anatomical structures (lungs, airways, aortic arches, and pulmonary arteries) that can be extracted robustly in an automated fashion. We applied the proposed method to 45 cases of contrast-enhanced chest CT volumes. Experimental results showed that 95.5% of lymph nodes were detected with 16.3 false positives per CT volume. The segmentation results showed that the proposed method can prevent oversegmentation, achieving an average Dice score of 52.3 ± 23.1%, compared to the baseline method with 49.2 ± 23.8%, respectively.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Computer-aided Diagnosis ; Fully Convolutional Network ; Imbalance Weighting
Sprache englisch
Veröffentlichungsjahr 2018
HGF-Berichtsjahr 2018
ISSN (print) / ISBN 0277-786X
e-ISSN 1996-756X
Zeitschrift Proceedings of SPIE
Quellenangaben Band: 10575 Heft: , Seiten: , Artikelnummer: , Supplement: ,
Verlag SPIE
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
DOI 10.1117/12.2287066
Scopus ID 85046290545
Erfassungsdatum 2022-09-07
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