TY - GEN
T1 - Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images
AU - Oda, Hirohisa
AU - Roth, Holger R.
AU - Bhatia, Kanwal K.
AU - Oda, Masahiro
AU - Kitasaka, Takayuki
AU - Iwano, Shingo
AU - Homma, Hirotoshi
AU - Takabatake, Hirotsugu
AU - Mori, Masaki
AU - Natori, Hiroshi
AU - Schnabel, Julia A.
AU - Mori, Kensaku
N1 - Publisher Copyright:
© 2018 SPIE.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Computer-Aided Diagnosis
KW - Fully convolutional network
KW - Imbalance weighting
UR - http://www.scopus.com/inward/record.url?scp=85046290545&partnerID=8YFLogxK
U2 - 10.1117/12.2287066
DO - 10.1117/12.2287066
M3 - Conference contribution
AN - SCOPUS:85046290545
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2018
A2 - Mori, Kensaku
A2 - Petrick, Nicholas
PB - SPIE
T2 - Medical Imaging 2018: Computer-Aided Diagnosis
Y2 - 12 February 2018 through 15 February 2018
ER -