5 Fold Cross Validation
Model | LIDC | RibFrac | CADA | Kits19 |
---|---|---|---|---|
nnDetection | 0.605 | 0.765 | 0.924 | 0.923 |
nnUNetPlus | 0.439* | 0.700 | 0.955 | 0.935 |
nnUNetBasic | 0.411* | 0.667 | 0.930 | 0.908 |
* results with corrected numerical values in softdice loss and improved multi-class import.
5 Fold Cross Validation
Model | ADAM | ProstateX | Pancreas | Hepatic Vessel | Colon | Liver |
---|---|---|---|---|---|---|
nnDetection | 0.780 | 0.300 | 0.766 | 0.727 | 0.662 | 0.628 |
nnUNetPlus | 0.720 | 0.220* | 0.721 | 0.721 | 0.579 | 0.678 |
nnUNetBasic | 0.657 | 0.202* | 0.691 | 0.699 | 0.509 | 0.567 |
*improved multi-class import
Test Split
Model | ProstateX | Pancreas | Hepatic Vessel | Colon | Liver |
---|---|---|---|---|---|
nnDetection | 0.221 | 0.791 | 0.664 | 0.696 | 0.790 |
nnUNetPlus | 0.123* | 0.704 | 0.684 | 0.731 | 0.760 |
ADAM Results are listed under Benchmarks *improved multi-class import
5 Fold Cross Validation
Model | Abdominal Lymph Nodes | Mediastinal Lymph Nodes |
---|---|---|
nnDetection | 0.493 | 0.440 |
nnUNetPlus | 0.378 | 0.334 |
nnUNetBasic | 0.360 | 0.302 |
Test Split
Model | Abdominal Lymph Nodes | Mediastinal Lymph Nodes |
---|---|---|
nnDetection | 0.470 | 0.500 |
nnUNetPlus | 0.311 | 0.342 |
Luna results are listed under Benchmarks
- S. G. Armato III, G. McLennan, L. Bidaut, M. F. McNitt-Gray, C. R. Meyer, A. P.Reeves, B. Zhao, D. R. Aberle, C. I. Henschke, E. A. Hoffman, et al. The lungimage database consortium (lidc) and image database resource initiative (idri):a completed reference database of lung nodules on ct scans.Medical physics,38(2):915–931, 2011
- L. Jin, J. Yang, K. Kuang, B. Ni, Y. Gao, Y. Sun, P. Gao, W. Ma, M. Tan, H. Kang,J. Chen, and M. Li. Deep-learning-assisted detection and segmentation of ribfractures from CT scans: Development and validation of FracNet. 62. Publisher:Elsevier
- C. Tabea Kossen, L. Kaufhold, M. H ̈ullebrand, J.-M. Kuhnigk, J. Br ̈uhning,J. Schaller, B. Pfahringer, A. Spuler, L. Goubergrits, and A. Hennemuth. Cerebralaneurysm detection and analysis, Mar. 2020
- K. Timmins, E. Bennink, I. van der Schaaf, B. Velthuis, Y. Ruigrok, and H. Kuijf.Intracranial Aneurysm Detection and Segmentation Challenge, Mar. 2020.
- N. Heller, N. Sathianathen, A. Kalapara, E. Walczak, K. Moore, H. Kaluzniak,J. Rosenberg, P. Blake, Z. Rengel, M. Oestreich, et al. The kits19 challenge data:300 kidney tumor cases with clinical context, ct semantic segmentations, and sur-gical outcomes.arXiv preprint arXiv:1904.00445, 2019
- G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer, and H. Huisman. Computer-aided detection of prostate cancer in mri.IEEE TMI, 33(5):1083–1092, 2014
- R. Cuocolo, A. Comelli, A. Stefano, V. Benfante, N. Dahiya, A. Stanzione,A. Castaldo, D. R. D. Lucia, A. Yezzi, and M. Imbriaco. Deep learning whole-gland and zonal prostate segmentation on a public mri dataset.Journal of Mag-netic Resonance Imaging, 2021.
- A. L. Simpson, M. Antonelli, S. Bakas, M. Bilello, K. Farahani, B. Van Ginneken,A. Kopp-Schneider, B. A. Landman, G. Litjens, B. Menze, et al. A large anno-tated medical image dataset for the development and evaluation of segmentationalgorithms.arXiv preprint arXiv:1902.09063, 2019.
- H. R. Roth, L. Lu, A. Seff, K. M. Cherry, J. Hoffman, S. Wang, J. Liu, E. Turkbey,and R. M. Summers. A new 2.5 d representation for lymph node detection usingrandom sets of deep convolutional neural network observations. InMICCAI, pages520–527. Springer, 2014
- A. Seff, L. Lu, A. Barbu, H. Roth, H.-C. Shin, and R. M. Summers. Leveraging mid-level semantic boundary cues for automated lymph node detection. InMICCAI,pages 53–61. Springer, 2015
Disclaimer: This overview reflects the literature upon submission of nnDetection (March 2021). It will not be updated with newer methods and can not replace a thorough literature research of future work.
![](/MIC-DKFZ/nnDetection/raw/main/docs/results/source/v001/luna.png)
Methods | 1/8 | 1/4 | 1/2 | 1 | 2 | 4 | 8 | CPM |
---|---|---|---|---|---|---|---|---|
Dou et al. (2017a) | 0.692 | 0.745 | 0.819 | 0.865 | 0.906 | 0.933 | 0.946 | 0.839 |
Zhu et al. (2018) | 0.692 | 0.769 | 0.824 | 0.865 | 0.893 | 0.917 | 0.933 | 0.842 |
Wang et al. (2018) | 0.676 | 0.776 | 0.879 | 0.949 | 0.958 | 0.958 | 0.958 | 0.878 |
Ding et al. (2017) | 0.748 | 0.853 | 0.887 | 0.922 | 0.938 | 0.944 | 0.946 | 0.891 |
Khosravan et al. (2018) | 0.709 | 0.836 | 0.921 | 0.953 | 0.953 | 0.953 | 0.953 | 0.897 |
Liu et al. (2019) | 0.848 | 0.876 | 0.905 | 0.933 | 0.943 | 0.957 | 0.970 | 0.919 |
Song et al. (2020) | 0.723 | 0.838 | 0.887 | 0.911 | 0.928 | 0.934 | 0.948 | 0.881 |
nnDetection v0.1 (ours, 2021) | 0.812 | 0.885 | 0.927 | 0.950 | 0.969 | 0.979 | 0.985 | 0.930 |
Methods with FPR* | ||||||||
Cao et al. (2020) + FPR | 0.848 | 0.899 | 0.925 | 0.936 | 0.949 | 0.957 | 0.960 | 0.925 |
Liu et al. (2019) + FPR | 0.904 | 0.914 | 0.933 | 0.957 | 0.971 | 0.971 | 0.971 | 0.952 |
* Some of the other methods also use FPR stages but the methods listed below report results w. and wo. FPR.
- A. A. A. Setio, A. Traverso, T. de Bel, M. S. Berens, C. van den Bogaard, P. Cerello,H. Chen, Q. Dou, M. E. Fantacci, B. Geurts, R. van der Gugten, P. A. Heng,B. Jansen, M. M. de Kaste, V. Kotov, J. Y.-H. Lin, J. T. Manders, A. S ́o ̃nora-Mengana, J. C. Garc ́ıa-Naranjo, E. Papavasileiou, M. Prokop, M. Saletta, C. M.Schaefer-Prokop, E. T. Scholten, L. Scholten, M. M. Snoeren, E. L. Torres, J. Van-demeulebroucke, N. Walasek, G. C. Zuidhof, B. van Ginneken, and C. Jacobs.Validation, comparison, and combination of algorithms for automatic detection ofpulmonary nodules in computed tomography images: The luna16 challenge.Me-dIA, 42:1–13, 2017.
- Z. Gong, D. Li, J. Lin, Y. Zhang and K. -M. Lam, "Towards Accurate Pulmonary Nodule Detection by Representing Nodules as Points With High-Resolution Network," in IEEE Access, vol. 8, pp. 157391-157402, 2020, doi: 10.1109/ACCESS.2020.3019104
- Q. Dou, H. Chen, L. Yu, J. Qin and P. Heng, "Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection," in IEEE Transactions on Biomedical Engineering, vol. 64, no. 7, pp. 1558-1567, July 2017, doi: 10.1109/TBME.2016.2613502.
- Gupta, A., Saar, T., Martens, O. and Moullec, Y.L. (2018), Automatic detection of multisize pulmonary nodules in CT images: Large-scale validation of the false-positive reduction step. Med. Phys., 45: 1135-1149. https://doi.org/10.1002/mp.12746
- J. Ding, A. Li, Z. Hu, and L. Wang. Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. In MICCAI, pages 559–567. Springer, 2017
- Q. Dou, H. Chen, Y. Jin, H. Lin, J. Qin, and P.-A. Heng. Automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid-loss residual learning. In MICCAI, pages 630–638. Springer, 2017
- N. Khosravan and U. Bagci. S4nd: Single-shot single-scale lung nodule detection. In MICCAI, pages 794–802. Springer, 2018.
- B. Wang, G. Qi, S. Tang, L. Zhang, L. Deng, and Y. Zhang. Automated pulmonary nodule detection: High sensitivity with few candidates. In MICCAI, pages 759–767. Springer, 2018
- W. Zhu, C. Liu, W. Fan, and X. Xie. Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification. In WACV, pages 673–681. IEEE, 2018
- J. Liu, L. Cao, O. Akin, and Y. Tian. 3dfpn-hs: 3d feature pyramid network based high sensitivity and specificity pulmonary nodule detection. In MICCAI, pages 513–521. Springer, 2019
- T. Song, J. Chen, X. Luo, Y. Huang, X. Liu, N. Huang, Y. Chen, Z. Ye, H. Sheng, S. Zhang, and G. Wang. CPM-net: A 3d center-points matching network for pulmonary nodule detection in CT scans. In A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, and L. Joskowicz, editors, MICCAI, pages 550–559. Springer International Publishing
- H. Cao, H. Liu, E. Song, G. Ma, X. Xu, R. Jin, T. Liu, and C. C. Hung. A twostage convolutional neural networks for lung nodule detection. IEEE Journal of Biomedical and Health Informatics, 24(7):2006–2015, 2020.
Disclaimer: This overview reflects the literature upon submission of nnDetection (March 2021). It will not be updated with newer methods and can not replace a thorough literature research of future work.