Online result archive of CHAOS

This page contains results of previous submissions. The last update date is 07/08/2019. The new and up-to-date leader board is at Results page.

Task 1: Liver Segmentation (CT-MRI)
Team Name                  Date        SCORE      Dice     Dice_Scr    RAVD    RAVD_scr   ASSD    ASSD_scr    MSSD    MSSD_scr
12Sigma9 03/08/19 76.709 0.95 94.957 2.792 54.498 1.658 88.946 24.041 62.127
FightHCC2D 07/06/19 72.765 0.904 87.569 3.542 50.566 4.996 83.466 31.626 56.094
FightHCC3D 07/06/19 70.557 0.899 86.882 3.279 47.339 4.801 82.737 31.225 55.932
12Sigma 10/06/19 68.026 0.931 91.805 5.34 37.302 2.479 83.473 30.444 51.629
PKDIAv2 07/05/19 50.661 0.854 84.146 6.654 21.657 9.767 75.839 46.561 42.277
MILab2 14/06/19 49.289 0.899 86.832 11.725 12.252 3.927 73.821 80.101 20.29
MILab3 22/06/19 46.303 0.902 88.397 14.682 6.378 4.516 70.505 94.076 14.441
METU_MMLAB_v102 22/04/19 42.542 0.863 75.942 18.008 14.117 8.506 60.364 62.613 24.943
Task 2: Liver Segmentation (CT only)
Team Name                  Date        SCORE      Dice     Dice_Scr    RAVD    RAVD_scr   ASSD    ASSD_scr    MSSD    MSSD_scr
AbdomenNet V2.1 03/07/19 85.749 0.98 97.977 1.436 71.284 0.679 95.471 13.043 78.262
AbdomenNet V2 03/07/19 85.459 0.979 97.923 1.421 71.586 0.697 95.353 13.815 76.974
FightHCC3D 07/06/19 84.644 0.979 97.947 1.547 69.053 0.692 95.387 14.287 76.188
PKDIAv2 07/05/19 82.457 0.978 97.789 1.32 73.597 0.891 94.059 21.892 64.383
FightHCC2D 07/06/19 82.167 0.977 97.667 1.488 70.235 0.957 93.623 19.713 67.144
12Sigma10 08/08/19 81.441 0.965 96.454 1.452 70.951 1.244 91.708 20.01 66.649
DLIPLab 14/06/19 80.963 0.963 96.315 1.611 67.772 1.231 91.796 19.218 67.97
AbdomenNet 23/05/19 80.752 0.964 96.387 1.726 65.578 1.214 91.908 18.52 69.133
DanielZuziak2 29/07/19 80.677 0.972 97.167 1.368 72.636 1.162 92.251 23.609 60.652
12Sigma11 08/08/19 79.307 0.965 96.506 1.943 61.137 1.216 91.892 19.385 67.692
Dawn v3 17/07/19 78.965 0.967 96.656 2.028 59.787 1.25 91.663 19.348 67.754
Dawn v2 26/05/19 77.177 0.965 96.489 2.239 58.537 1.422 90.521 22.103 63.162
Dawn 05/05/19 75.998 0.965 96.526 2.268 54.645 1.387 90.753 23.006 62.069
12Sigma 10/06/19 74.429 0.957 95.719 2.326 61.034 1.718 88.55 28.553 52.412
OncoRadiomics 24/05/19 74.347 0.972 97.234 1.618 67.643 2.143 85.711 42.807 46.8
Segreg2 08/07/19 73.292 0.975 97.529 1.385 72.306 1.263 91.58 59.582 31.753
DLIPLab2 25/06/19 72.995 0.964 96.372 3.08 38.809 1.223 91.85 21.03 64.95
AIExplore 19/06/19 71.555 0.948 94.799 3.569 40.832 1.788 88.082 22.497 62.505
Segreg 05/07/19 71.335 0.975 97.476 1.475 70.498 1.622 89.19 66.332 28.176
MILab7 18/07/19 69.339 0.97 96.973 4.433 24.613 1.076 92.825 25.25 62.945
BITLab10 04/08/19 65.243 0.972 97.169 1.593 68.133 1.926 87.16 110.658 8.508
NEHUSGGSv2 14/05/19 65.18 0.939 93.9 4.597 32.49 2.382 84.117 30.007 50.212
MILab6 12/07/19 64.274 0.968 96.757 4.402 28.857 1.701 88.662 46.162 42.821
BITLab6 04/08/19 63.237 0.971 97.05 2.093 61.193 4.773 83.548 110.049 11.158
BITLab7 04/08/19 63.055 0.972 97.231 1.719 66.17 2.143 85.712 128.833 3.106
MILab4 28/06/19 62.907 0.966 96.587 4.251 30.115 2.21 85.265 53.497 39.66
BITLab3 03/08/19 61.801 0.962 96.23 3.4 66.176 6.525 78.324 131.221 6.472
BITLab8 04/08/19 61.759 0.961 92.278 4.455 58.263 6.037 83.279 105.891 13.216
BITLab5 03/08/19 61.751 0.943 92.222 14.498 66.978 7.612 79.247 122.904 8.555
BITLab9 04/08/19 60.956 0.959 92.295 5.126 61.833 6.058 80.472 130.256 9.223
BITLab2 03/08/19 60.229 0.967 96.726 2.408 58.705 2.637 82.419 127.845 3.066
DanielZuziak 26/07/19 59.527 0.909 90.69 6.898 52.255 6.04 76.458 87.575 18.705
BITLab4 03/08/19 59.326 0.955 91.857 5.995 53.648 6.607 80.842 148.122 10.958
OncoRadiomicsV2 14/06/19 58.374 0.936 93.627 11.59 0 2.256 84.962 27.056 54.907
BITLab 24/05/19 56.82 0.953 91.933 4.561 53.327 11.394 78.268 158.676 3.751
UniSegm 28/05/19 53.76 0.919 91.851 7.825 18.057 3.728 75.148 44.082 29.985
MILab5 08/07/19 53.126 0.961 96.105 6.321 14.486 2.824 82.643 69.772 19.272
NEHUSGGS 09/05/19 52.701 0.935 93.517 4.48 37.187 4.345 71.767 111.218 8.332
MILab3 22/06/19 50.421 0.957 95.727 7.311 9.732 3.946 75.374 84.15 20.851
KCliver 17/05/19 49.548 0.756 75.55 24.22 15.291 148.847 66.084 178.089 41.265
MILab2 14/06/19 48.834 0.935 93.521 6.737 15.366 4.847 67.995 85.815 18.452
MILab 14/06/19 46.352 0.935 93.469 6.843 13.451 5.135 66.294 92.223 12.194
BITLab1 03/08/19 27.864 0.841 77.865 11.048 26.553 26.941 7.04 199.158 0
Liver_AI_Team2 28/04/19 25.304 0.479 33.909 2.809 50.205 30.513 15.447 123.71 1.654
Liver_AI_Team 22/04/19 14.518 0.496 37.849 14.423 0 27.123 18.28 121.411 1.9433
Task 3: Liver Segmentation (MRI only)
Team Name                  Date        SCORE      Dice     Dice_Scr    RAVD    RAVD_scr   ASSD    ASSD_scr    MSSD    MSSD_scr
12Sigma8 29/07/19 71.131 0.938 93.847 3.469 47.062 2.014 86.575 27.632 57.041
PKDIAv2 07/05/19 70.712 0.945 94.474 3.529 41.803 1.563 89.58 26.062 56.992
12SigmaShanghai3 23/07/19 66.273 0.933 93.25 4.112 36.305 2.214 85.241 31.007 50.298
12Sigma5 22/06/19 66.025 0.928 92.802 4.301 35.223 2.397 84.022 30.12 52.052
12SigmaShanghai 02/07/19 65.431 0.934 93.44 4.358 28.673 2.032 86.453 28.594 53.159
FightHCC2D 07/06/19 62.378 0.867 82.725 4.538 39.592 7.06 77.783 38.763 49.411
12SigmaShanghai2 17/07/19 62.178 0.933 93.251 5.269 26.222 2.311 84.591 35.515 44.649
12Sigma3 07/06/19 61.325 0.919 91.88 6.045 22.425 2.771 81.528 32.135 49.466
FightHCC3D 07/06/19 60.837 0.867 82.281 4.771 33.948 6.74 77.238 38.969 49.88
12Sigma6 22/06/19 60.704 0.899 88.396 5.338 34.856 3.482 77.129 36.199 42.436
12Sigma2 07/06/19 59.861 0.89 89.034 8.804 26.866 12.062 77.581 42.306 45.963
IG5 12/07/19 59.347 0.905 86.365 9.26 23.355 3.86 79.66 35.779 48.01
IG6 15/07/19 59.347 0.905 86.365 9.26 23.355 3.86 79.66 35.779 48.01
IG4 04/07/19 59.181 0.909 88.495 7.895 25.453 3.752 80.049 39.662 42.727
Segreg 05/07/19 55.23 0.909 89.728 7.167 30.404 3.903 75.514 59.437 25.274
IG3 04/07/19 54.358 0.892 85.606 9.282 27.146 4.685 74.515 62.255 30.167
MILab4 28/06/19 54.017 0.875 84.067 10.115 21.034 4.029 76.754 50.492 34.215
IG 01/07/19 53.263 0.9 87.629 8.383 26.915 4.462 75.296 67.186 23.212
METU_MMLAB_v102 22/04/19 53.152 0.888 81.057 12.642 10.943 3.476 77.032 35.743 43.574
MILab 14/06/19 50.553 0.863 83.123 10.714 23.806 4.625 72.528 79.723 22.754
MILab5 08/07/19 49.347 0.887 85.089 15.002 4.375 3.5 76.67 53.829 31.253
MILab2 14/06/19 42.185 0.875 84.732 18.368 4.701 4.801 68.071 99.039 11.236
BITLab 24/05/19 31.852 0.704 51.437 459.377 0 9.654 53.127 95.236 22.8432.843
Task 4: Segmentation of abdominal organs (CT+MRI)
Team Name                  Date        SCORE      Dice     Dice_Scr    RAVD    RAVD_scr   ASSD    ASSD_scr    MSSD    MSSD_scr
12Sigma9 03/08/19 75.719 0.936 92.781 5.561 40.361 1.471 90.192 18.65 70.234
FightHCC3D 07/06/19 71.284 0.879 84.726 5.552 37.651 4.654 83.119 25.455 63.97
FightHCC2D 07/06/19 71.216 0.878 84.907 6.55 37.435 5.242 82.484 27.501 62.336
12Sigma4 12/06/19 68.15 0.914 88.839 8.035 29.157 2.163 85.632 23.755 62.045
PKDIAv2 07/05/19 49.634 0.878 85.464 8.428 18.97 6.373 82.087 33.171 56.642642

Task 5: Segmentation of abdominal organs (MRI only)
Team Name                  Date        SCORE      Dice     Dice_Scr    RAVD    RAVD_scr   ASSD    ASSD_scr    MSSD    MSSD_scr
12Sigma8 29/07/19 69.961 0.926 92.595 6.492 33.072 1.606 89.291 18.947 69.969
12SigmaShanghai 02/07/19 68.818 0.928 92.773 6.528 26.854 1.58 89.465 20.53 66.192
12Sigma7 17/07/19 68.1 0.921 91.104 6.517 33.164 1.701 88.657 19.558 68.604
PKDIAv2 07/05/19 66.463 0.93 92.972 6.914 28.652 1.434 90.441 20.101 66.713
12SigmaShanghai2 17/07/19 65.273 0.924 89.454 7.959 22.178 1.804 87.975 24.497 61.092
12Sigma5 22/06/19 64.63 0.912 89.205 7.219 29.74 1.976 86.828 21.488 65.155
12Sigma4 12/06/19 63.522 0.907 87.853 8.348 23.847 2.107 85.955 22.982 63.114
FightHCC3D 07/06/19 63.437 0.857 81.941 6.192 30.821 5.592 80.596 28.413 62.244
FightHCC2D 07/06/19 61.6 0.849 79.542 7.95 29.58 5.798 79.366 30.109 59.24
AbdomenNet 23/05/19 61.467 0.897 84.389 12.17 18.43 2.592 83.908 24.682 61.87
12Sigma6 22/06/19 60.528 0.885 81.67 10.068 21.508 2.853 81.017 26.032 58.504
METU_MMLAB_v102 22/04/19 56.012 0.886 80.218 12.442 15.626 3.206 79.192 32.7 49.295
xmu_med 21/06/19 32.785 0.55 53.132 44.863 10.515 196.827 52.548 236.514 18.802
Dice=Sørensen–Dice coefficient, Dice_scr=Score of Dice metric
RAVD=Relative absolute volume difference, RAVD_scr=Score of RAVD metric
ASSD=Average symmetric surface distance (millimeter), ASSD_scr=Score of ASSD metric
MSSD=Maximum symmetric surface distance (millimeter), MSSD_scr=Score of MSSD metric

Team Information

PKDIA:
-Pierre-Henri Conze : IMT Atlantique | UMR 1101, Inserm, Brest, France
-Emilie Cornec-Le Gall : UMR 1078, Inserm | Department of Nephrology, University Hospital | UBO, Brest, France
-François Rousseau : IMT Atlantique | UMR 1101, Inserm, Brest, France
-Yannick Le Meur : Department of Nephrology, University Hospital, Brest, France

METU_MMLAB: Bora Baydar, Savaş Özkan, Gözde Bozdağı Akar, Dept. of Electrical and Electronics Eng., Middle East Technical University, Ankara, Turkey

NEHUSGGS: Gajendra Kumar Mourya et al. - Department of Biomedical Engineering, SOT, North Eastern Hill University, Shillong, Meghalaya, India

Dawn: South China University of Technology

Liver_AI_Team: D.Sabarinathan(Couger Inc, Japan), Dr. Parisa Beham, Dr. Priya Kansal (Sethu Institute of Technology, India)

KCliver: Chen Kun, Fudan University Shanghai, China

AbdomenNet: Mandel Chen et al. IFLYTEK South China AI Research Institute, China

DeepMedic [Applied by organizers]: https://biomedia.doc.ic.ac.uk/software/deepmedic/ Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK

  1. Konstantinos Kamnitsas, Christian Ledig, Virginia F.J. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K. Menon, Daniel Rueckert, and Ben Glocker, “Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation”, Medical Image Analysis, 2016.
  2. Konstantinos Kamnitsas, Liang Chen, Christian Ledig, Daniel Rueckert, and Ben Glocker, “Multi-Scale 3D CNNs for segmentation of brain Lesions in multi-modal MRI”, in proceeding of ISLES challenge, MICCAI 2015.

Dense V-Networks [Applied by organizers via NiftyNet]:

  1. E. Gibson et al., "Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks," in IEEE Transactions on Medical Imaging, vol. 37, no. 8, pp. 1822-1834, Aug. 2018. https://doi.org/10.1109/TMI.2018.2806309
  2. E. Gibson, W. Li, C. Sudre, L. Fidon, D. Shakir, G. Wang, Z. Eaton-Rosen, R. Gray, T. Doel, Y. Hu, T. Whyntie, P. Nachev, M. Modat, D. C. Barratt, S. Ourselin, M. J. Cardoso and T. Vercauteren (2018) NiftyNet: a deep-learning platform for medical imaging, Computer Methods and Programs in Biomedicine. https://doi.org/10.1016/j.cmpb.2018.01.025

V-Net [Applied by organizers via NiftyNet]:

  1. F. Milletari, N. Navab and S. Ahmadi, "V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation," 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, 2016, pp. 565-571. https://doi.org/10.1109/3DV.2016.79
  2. E. Gibson, W. Li, C. Sudre, L. Fidon, D. Shakir, G. Wang, Z. Eaton-Rosen, R. Gray, T. Doel, Y. Hu, T. Whyntie, P. Nachev, M. Modat, D. C. Barratt, S. Ourselin, M. J. Cardoso and T. Vercauteren (2018) NiftyNet: a deep-learning platform for medical imaging, Computer Methods and Programs in Biomedicine. https://doi.org/10.1016/j.cmpb.2018.01.025

OncoRadiomics:
-Akshayaa Vaidyanathan, Artificial Intelligence Scientist, OncoRadiomics, Liège, Belgium
-Dr. Sean Walsh, Chief Science Officer, OncoRadiomics, Liège, Belgium
-Prof. Dr. Philippe Lambin, Chief Scientific Advisor, OncoRadiomics, Liège, Belgium

BITLab: Lei Chen, Beijing Institute of Technology, Beijing , China.

FightHCC: Jun Ma, University of Toronto, Nanjing University of Science and Technology.
UniSegm: Catalina Gómez et al. Biomedical Computer Vision group at Universidad de los Andes, Colombia

12Sigma: Jie Cai, University of South Carolina U.S. Shizhong Han and Yunqiang Chen, 12Sigma Technologies

12SigmaShanghai: Yuxiang Ye, Yinan chen, Yajing Zhu, 12Sigma Technologies

AIExplore: Smart Healthcare, Manufacturing, and City, Taiwan.

MILab:
SiChuan University, China.

DLIPLab:
Minyoung Chung, Computer Graphics & Image Processing Laboratory, Department of Computer Science & Engineering, Seoul National University, Korea.

IG: Jiejian Luo, Lixin Wang, Jin Qin, Mingkun Yang from Sinovation Ventures

Segreg:  Panlong Xu, SJTU(Shanghai Jiao Tong University), China.

DanielZuziak: AGH University of Science and Technology, Kraków, Poland