Abstract
Computer aided diagnostic (CAD) system is crucial for modern medical imaging. But almost all CAD systems operate on reconstructed images, which were optimized for radiologists. Computer vision can capture features that is subtle to human observers, so it is desirable to design a CAD system operating on the raw data. In this paper, we proposed a deep-neural-network-based detection system for lung nodule detection in computed tomography (CT). A primal-dual-type deep reconstruction network was applied first to convert the raw data to the image space, followed by a 3-dimensional convolutional neural network (3D-CNN) for the nodule detection. For efficient network training, the deep reconstruction network and the CNN detector was trained sequentially first, then followed by one epoch of end-to-end fine tuning. The method was evaluated on the Lung Image Database Consortium image collection (LIDC-IDRI) with simulated forward projections. With 144 multi-slice fanbeam projections, the proposed end-to-end detector could achieve comparable sensitivity with the reference detector, which was trained and applied on the fully-sampled image data. It also demonstrated superior detection performance compared to detectors trained on the reconstructed images. The proposed method is general and could be expanded to most detection tasks in medical imaging.
| Original language | English (US) |
|---|---|
| Title of host publication | Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings |
| Editors | Mingxia Liu, Heung-Il Suk, Yinghuan Shi |
| Publisher | Springer-Verlag |
| Pages | 37-45 |
| Number of pages | 9 |
| ISBN (Print) | 9783030009182 |
| DOIs | |
| State | Published - Jan 1 2018 |
| Externally published | Yes |
| Event | 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: Sep 16 2018 → Sep 16 2018 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11046 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 |
|---|---|
| Country | Spain |
| City | Granada |
| Period | 9/16/18 → 9/16/18 |
Fingerprint
Keywords
- Artificial neural networks
- Computed tomography
- Computer aided diagnosis
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
End-to-end lung nodule detection in computed tomography. / Wu, Dufan; Kim, Kyungsang; Dong, Bin; Fakhri, Georges El; Li, Quanzheng.
Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. ed. / Mingxia Liu; Heung-Il Suk; Yinghuan Shi. Springer-Verlag, 2018. p. 37-45 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11046 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - End-to-end lung nodule detection in computed tomography
AU - Wu, Dufan
AU - Kim, Kyungsang
AU - Dong, Bin
AU - Fakhri, Georges El
AU - Li, Quanzheng
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Computer aided diagnostic (CAD) system is crucial for modern medical imaging. But almost all CAD systems operate on reconstructed images, which were optimized for radiologists. Computer vision can capture features that is subtle to human observers, so it is desirable to design a CAD system operating on the raw data. In this paper, we proposed a deep-neural-network-based detection system for lung nodule detection in computed tomography (CT). A primal-dual-type deep reconstruction network was applied first to convert the raw data to the image space, followed by a 3-dimensional convolutional neural network (3D-CNN) for the nodule detection. For efficient network training, the deep reconstruction network and the CNN detector was trained sequentially first, then followed by one epoch of end-to-end fine tuning. The method was evaluated on the Lung Image Database Consortium image collection (LIDC-IDRI) with simulated forward projections. With 144 multi-slice fanbeam projections, the proposed end-to-end detector could achieve comparable sensitivity with the reference detector, which was trained and applied on the fully-sampled image data. It also demonstrated superior detection performance compared to detectors trained on the reconstructed images. The proposed method is general and could be expanded to most detection tasks in medical imaging.
AB - Computer aided diagnostic (CAD) system is crucial for modern medical imaging. But almost all CAD systems operate on reconstructed images, which were optimized for radiologists. Computer vision can capture features that is subtle to human observers, so it is desirable to design a CAD system operating on the raw data. In this paper, we proposed a deep-neural-network-based detection system for lung nodule detection in computed tomography (CT). A primal-dual-type deep reconstruction network was applied first to convert the raw data to the image space, followed by a 3-dimensional convolutional neural network (3D-CNN) for the nodule detection. For efficient network training, the deep reconstruction network and the CNN detector was trained sequentially first, then followed by one epoch of end-to-end fine tuning. The method was evaluated on the Lung Image Database Consortium image collection (LIDC-IDRI) with simulated forward projections. With 144 multi-slice fanbeam projections, the proposed end-to-end detector could achieve comparable sensitivity with the reference detector, which was trained and applied on the fully-sampled image data. It also demonstrated superior detection performance compared to detectors trained on the reconstructed images. The proposed method is general and could be expanded to most detection tasks in medical imaging.
KW - Artificial neural networks
KW - Computed tomography
KW - Computer aided diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85054499052&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054499052&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00919-9_5
DO - 10.1007/978-3-030-00919-9_5
M3 - Conference contribution
AN - SCOPUS:85054499052
SN - 9783030009182
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 37
EP - 45
BT - Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings
A2 - Liu, Mingxia
A2 - Suk, Heung-Il
A2 - Shi, Yinghuan
PB - Springer-Verlag
ER -