Datasets:
pyushkevich
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crashs_template_package

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Please cite the following paper when using nnU-Net:
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
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This is the configuration used by this training:
Configuration name: 3d_fullres
{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 128, 128], 'median_image_size_in_voxels': [64.0, 113.0, 113.0], 'spacing': [0.9765625, 0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}
These are the global plan.json settings:
{'dataset_name': 'Dataset303_3TT1WMSegASHSGT', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [0.9765625, 0.5, 0.48828125], 'original_median_shape_after_transp': [64, 113, 113], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [1, 0, 2], 'transpose_backward': [1, 0, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 685.0, 'mean': 356.69915771484375, 'median': 396.0, 'min': 117.0, 'percentile_00_5': 149.0, 'percentile_99_5': 570.0, 'std': 120.57451629638672}}}
2023-08-02 21:58:34.149126: unpacking dataset...
2023-08-02 21:58:34.473478: unpacking done...
2023-08-02 21:58:34.474119: do_dummy_2d_data_aug: False
2023-08-02 21:58:34.474694: Using splits from existing split file: /data/liyue7/Data/M1_nnUNet/nnUNet_preprocessed/Dataset303_3TT1WMSegASHSGT/splits_final.json
2023-08-02 21:58:34.474854: The split file contains 5 splits.
2023-08-02 21:58:34.474900: Desired fold for training: 0
2023-08-02 21:58:34.474939: This split has 46 training and 12 validation cases.
2023-08-02 21:58:34.480075: Unable to plot network architecture:
2023-08-02 21:58:34.480146: No module named 'hiddenlayer'
2023-08-02 21:58:34.519761:
2023-08-02 21:58:34.519815: Epoch 0
2023-08-02 21:58:34.519891: Current learning rate: 0.01
2023-08-02 22:00:03.004448: train_loss -0.1602
2023-08-02 22:00:03.004621: val_loss -0.6431
2023-08-02 22:00:03.004667: Pseudo dice [0.7123]
2023-08-02 22:00:03.004718: Epoch time: 88.49 s
2023-08-02 22:00:03.004757: Yayy! New best EMA pseudo Dice: 0.7123
2023-08-02 22:00:04.079823:
2023-08-02 22:00:04.079919: Epoch 1
2023-08-02 22:00:04.080002: Current learning rate: 0.00998
2023-08-02 22:01:06.331137: train_loss -0.6851
2023-08-02 22:01:06.331275: val_loss -0.7601
2023-08-02 22:01:06.331314: Pseudo dice [0.8097]
2023-08-02 22:01:06.331359: Epoch time: 62.25 s
2023-08-02 22:01:06.331396: Yayy! New best EMA pseudo Dice: 0.7221
2023-08-02 22:01:08.636403:
2023-08-02 22:01:08.636506: Epoch 2
2023-08-02 22:01:08.636586: Current learning rate: 0.00995
2023-08-02 22:02:11.011815: train_loss -0.7477
2023-08-02 22:02:11.011961: val_loss -0.8005
2023-08-02 22:02:11.012002: Pseudo dice [0.8433]
2023-08-02 22:02:11.012047: Epoch time: 62.38 s
2023-08-02 22:02:11.012084: Yayy! New best EMA pseudo Dice: 0.7342
2023-08-02 22:02:13.056767:
2023-08-02 22:02:13.056870: Epoch 3
2023-08-02 22:02:13.056954: Current learning rate: 0.00993
2023-08-02 22:03:15.491260: train_loss -0.7857
2023-08-02 22:03:15.491742: val_loss -0.8215
2023-08-02 22:03:15.491786: Pseudo dice [0.8605]
2023-08-02 22:03:15.491831: Epoch time: 62.44 s
2023-08-02 22:03:15.491870: Yayy! New best EMA pseudo Dice: 0.7468
2023-08-02 22:03:17.505135:
2023-08-02 22:03:17.505237: Epoch 4
2023-08-02 22:03:17.505322: Current learning rate: 0.00991
2023-08-02 22:04:19.921209: train_loss -0.8016
2023-08-02 22:04:19.921346: val_loss -0.8273
2023-08-02 22:04:19.921388: Pseudo dice [0.8615]
2023-08-02 22:04:19.921433: Epoch time: 62.42 s
2023-08-02 22:04:19.921470: Yayy! New best EMA pseudo Dice: 0.7583
2023-08-02 22:04:21.908744:
2023-08-02 22:04:21.908846: Epoch 5
2023-08-02 22:04:21.908923: Current learning rate: 0.00989
2023-08-02 22:05:24.340911: train_loss -0.8115
2023-08-02 22:05:24.341050: val_loss -0.8428
2023-08-02 22:05:24.341090: Pseudo dice [0.8746]
2023-08-02 22:05:24.341135: Epoch time: 62.43 s
2023-08-02 22:05:24.341172: Yayy! New best EMA pseudo Dice: 0.7699
2023-08-02 22:05:26.416294:
2023-08-02 22:05:26.416399: Epoch 6
2023-08-02 22:05:26.416478: Current learning rate: 0.00986
2023-08-02 22:06:28.831770: train_loss -0.8274
2023-08-02 22:06:28.831909: val_loss -0.8475
2023-08-02 22:06:28.831949: Pseudo dice [0.8801]
2023-08-02 22:06:28.831995: Epoch time: 62.42 s
2023-08-02 22:06:28.832032: Yayy! New best EMA pseudo Dice: 0.7809
2023-08-02 22:06:30.999928:
2023-08-02 22:06:31.000031: Epoch 7
2023-08-02 22:06:31.000112: Current learning rate: 0.00984
2023-08-02 22:07:33.432782: train_loss -0.8394
2023-08-02 22:07:33.432915: val_loss -0.8555
2023-08-02 22:07:33.432956: Pseudo dice [0.8845]
2023-08-02 22:07:33.433001: Epoch time: 62.43 s
2023-08-02 22:07:33.433038: Yayy! New best EMA pseudo Dice: 0.7913
2023-08-02 22:07:35.622762:
2023-08-02 22:07:35.622863: Epoch 8
2023-08-02 22:07:35.622944: Current learning rate: 0.00982
2023-08-02 22:08:38.060140: train_loss -0.838
2023-08-02 22:08:38.060285: val_loss -0.8542
2023-08-02 22:08:38.060333: Pseudo dice [0.883]
2023-08-02 22:08:38.060412: Epoch time: 62.44 s
2023-08-02 22:08:38.060493: Yayy! New best EMA pseudo Dice: 0.8005
2023-08-02 22:08:40.178575:
2023-08-02 22:08:40.178674: Epoch 9
2023-08-02 22:08:40.178754: Current learning rate: 0.0098
2023-08-02 22:09:42.615384: train_loss -0.8516
2023-08-02 22:09:42.615526: val_loss -0.8548