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nnDetection v0.1

Pool v0.1

 

Train Pool [AP @ IoU 0.1]

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.

 

Validation Pool [AP @ IoU 0.1]

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

 

Test Pool [AP @ IoU 0.1]

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

 

References

  • 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

Benchmarks

Luna

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.

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.  

References (no particular oder)

  • 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.

ADAM Live Leaderboard

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.

Model Sens FP
nnDetection 0.64 0.3