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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/progress.png filter=lfs diff=lfs merge=lfs -text

nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/dataset.json

@@ -0,0 +1,34 @@
+{
+    "name": "Dataset145_Fast_organs",
+    "description": "",
+    "reference": "",
+    "licence": "hands off!",
+    "release": "0.0",
+    "labels": {
+        "background": "0",
+        "adrenal_gland_left": "1",
+        "adrenal_gland_right": "2",
+        "bladder": "3",
+        "brain": "4",
+        "gallbladder": "5",
+        "kidney_left": "6",
+        "kidney_right": "7",
+        "liver": "8",
+        "lung_lower_lobe_left": "9",
+        "lung_lower_lobe_right": "10",
+        "lung_middle_lobe_right": "11",
+        "lung_upper_lobe_left": "12",
+        "lung_upper_lobe_right": "13",
+        "pancreas": "14",
+        "spleen": "15",
+        "stomach": "16",
+        "thyroid_left": "17",
+        "thyroid_right": "18",
+        "trachea": "19"
+    },
+    "numTraining": 1683,
+    "file_ending": ".nii.gz",
+    "channel_names": {
+        "0": "CT"
+    }
+}

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nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/debug.json

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+{
+    "_best_ema": "None",
+    "batch_size": "4",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 4, 'patch_size': [176, 80, 80], 'median_image_size_in_voxels': [162.0, 80.0, 80.0], 'spacing': [6.0, 6.0, 6.0], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[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}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 8902,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7ebf99c63580>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7ebf99c62da0>",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "Compose ( [SpatialTransform( independent_scale_for_each_axis = False, p_rot_per_sample = 0.2, p_scale_per_sample = 0.2, p_el_per_sample = 0, data_key = 'data', label_key = 'seg', patch_size = [176, 80, 80], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-0.5235987755982988, 0.5235987755982988), angle_y = (-0.5235987755982988, 0.5235987755982988), angle_z = (-0.5235987755982988, 0.5235987755982988), do_scale = True, scale = (0.7, 1.4), border_mode_data = 'constant', border_cval_data = 0, order_data = 3, border_mode_seg = 'constant', border_cval_seg = -1, order_seg = 1, random_crop = False, p_rot_per_axis = 1, p_independent_scale_per_axis = 1 ), GaussianNoiseTransform( p_per_sample = 0.1, data_key = 'data', noise_variance = (0, 0.1), p_per_channel = 1, per_channel = False ), GaussianBlurTransform( p_per_sample = 0.2, different_sigma_per_channel = True, p_per_channel = 0.5, data_key = 'data', blur_sigma = (0.5, 1.0), different_sigma_per_axis = False, p_isotropic = 0 ), BrightnessMultiplicativeTransform( p_per_sample = 0.15, data_key = 'data', multiplier_range = (0.75, 1.25), per_channel = True ), ContrastAugmentationTransform( p_per_sample = 0.15, data_key = 'data', contrast_range = (0.75, 1.25), preserve_range = True, per_channel = True, p_per_channel = 1 ), SimulateLowResolutionTransform( order_upsample = 3, order_downsample = 0, channels = None, per_channel = True, p_per_channel = 0.5, p_per_sample = 0.25, data_key = 'data', zoom_range = (0.5, 1), ignore_axes = None ), GammaTransform( p_per_sample = 0.1, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = True ), GammaTransform( p_per_sample = 0.3, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = False ), MirrorTransform( p_per_sample = 1, data_key = 'data', label_key = 'seg', axes = (0, 1, 2) ), RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7ebf99c636a0>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7ebf99c62bc0>",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "Compose ( [RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataset_json": "{'name': 'Dataset145_Fast_organs', 'description': '', 'reference': '', 'licence': 'hands off!', 'release': '0.0', 'labels': {'background': '0', 'adrenal_gland_left': '1', 'adrenal_gland_right': '2', 'bladder': '3', 'brain': '4', 'gallbladder': '5', 'kidney_left': '6', 'kidney_right': '7', 'liver': '8', 'lung_lower_lobe_left': '9', 'lung_lower_lobe_right': '10', 'lung_middle_lobe_right': '11', 'lung_upper_lobe_left': '12', 'lung_upper_lobe_right': '13', 'pancreas': '14', 'spleen': '15', 'stomach': '16', 'thyroid_left': '17', 'thyroid_right': '18', 'trachea': '19'}, 'numTraining': 1683, 'file_ending': '.nii.gz', 'channel_names': {'0': 'CT'}}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "fold": "all",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA A100 80GB PCIe",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7ebfa1d77730>",
+    "hostname": "metazoo",
+    "inference_allowed_mirroring_axes": "(0, 1, 2)",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7ebfa1d77a30>",
+    "local_rank": "0",
+    "log_file": "/media/zooguard/crucial-nvme-3/PROJECTS/Manuel/MOOSE/fast_organs_model/nnunet_results/Dataset145_Fast_organs/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/training_log_2024_8_27_12_36_38.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7ebfa1d778b0>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7ebfa1da26e0>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset145_Fast_organs', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [6.0, 6.0, 6.0], 'original_median_shape_after_transp': [162, 80, 80], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 492, 'patch_size': [80, 80], 'median_image_size_in_voxels': [80.0, 80.0], 'spacing': [6.0, 6.0], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, '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': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 4, 'patch_size': [176, 80, 80], 'median_image_size_in_voxels': [162.0, 80.0, 80.0], 'spacing': [6.0, 6.0, 6.0], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[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}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 2981.83154296875, 'mean': -306.5704650878906, 'median': -7.578986644744873, 'min': -1138.905029296875, 'percentile_00_5': -952.3096923828125, 'percentile_99_5': 193.60693359375, 'std': 407.40484619140625}}}, 'configuration': '3d_fullres', 'fold': 'all', 'dataset_json': {'name': 'Dataset145_Fast_organs', 'description': '', 'reference': '', 'licence': 'hands off!', 'release': '0.0', 'labels': {'background': '0', 'adrenal_gland_left': '1', 'adrenal_gland_right': '2', 'bladder': '3', 'brain': '4', 'gallbladder': '5', 'kidney_left': '6', 'kidney_right': '7', 'liver': '8', 'lung_lower_lobe_left': '9', 'lung_lower_lobe_right': '10', 'lung_middle_lobe_right': '11', 'lung_upper_lobe_left': '12', 'lung_upper_lobe_right': '13', 'pancreas': '14', 'spleen': '15', 'stomach': '16', 'thyroid_left': '17', 'thyroid_right': '18', 'trachea': '19'}, 'numTraining': 1683, 'file_ending': '.nii.gz', 'channel_names': {'0': 'CT'}}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "2000",
+    "num_input_channels": "1",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "/media/zooguard/crucial-nvme-3/PROJECTS/Manuel/MOOSE/fast_organs_model/nnunet_results/Dataset145_Fast_organs/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all",
+    "output_folder_base": "/media/zooguard/crucial-nvme-3/PROJECTS/Manuel/MOOSE/fast_organs_model/nnunet_results/Dataset145_Fast_organs/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset145_Fast_organs', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [6.0, 6.0, 6.0], 'original_median_shape_after_transp': [162, 80, 80], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 492, 'patch_size': [80, 80], 'median_image_size_in_voxels': [80.0, 80.0], 'spacing': [6.0, 6.0], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, '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': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 4, 'patch_size': [176, 80, 80], 'median_image_size_in_voxels': [162.0, 80.0, 80.0], 'spacing': [6.0, 6.0, 6.0], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[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}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 2981.83154296875, 'mean': -306.5704650878906, 'median': -7.578986644744873, 'min': -1138.905029296875, 'percentile_00_5': -952.3096923828125, 'percentile_99_5': 193.60693359375, 'std': 407.40484619140625}}}",
+    "preprocessed_dataset_folder": "/media/zooguard/crucial-nvme-3/PROJECTS/Manuel/MOOSE/fast_organs_model/nnunet_preprocessed/Dataset145_Fast_organs/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/media/zooguard/crucial-nvme-3/PROJECTS/Manuel/MOOSE/fast_organs_model/nnunet_preprocessed/Dataset145_Fast_organs",
+    "save_every": "50",
+    "torch_version": "2.3.1+cu121",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/training_log_2024_8_27_12_35_49.txt

@@ -0,0 +1,22 @@
+
+#######################################################################
+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.
+#######################################################################
+ 
+
+This is the configuration used by this training:
+Configuration name: 3d_fullres
+ {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 4, 'patch_size': [176, 80, 80], 'median_image_size_in_voxels': [162.0, 80.0, 80.0], 'spacing': [6.0, 6.0, 6.0], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[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': 'Dataset145_Fast_organs', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [6.0, 6.0, 6.0], 'original_median_shape_after_transp': [162, 80, 80], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 2981.83154296875, 'mean': -306.5704650878906, 'median': -7.578986644744873, 'min': -1138.905029296875, 'percentile_00_5': -952.3096923828125, 'percentile_99_5': 193.60693359375, 'std': 407.40484619140625}}} 
+ 
+2024-08-27 12:35:52.178022: unpacking dataset... 
+2024-08-27 12:36:03.383381: unpacking done... 
+2024-08-27 12:36:03.384249: do_dummy_2d_data_aug: False 
+2024-08-27 12:36:03.404946: Unable to plot network architecture: 
+2024-08-27 12:36:03.405035: No module named 'hiddenlayer' 
+2024-08-27 12:36:03.410572:  
+2024-08-27 12:36:03.410657: Epoch 0 
+2024-08-27 12:36:03.410768: Current learning rate: 0.01 

nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/plans.json

@@ -0,0 +1,272 @@
+{
+    "dataset_name": "Dataset145_Fast_organs",
+    "plans_name": "nnUNetPlans",
+    "original_median_spacing_after_transp": [
+        6.0,
+        6.0,
+        6.0
+    ],
+    "original_median_shape_after_transp": [
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+        80,
+        80
+    ],
+    "image_reader_writer": "SimpleITKIO",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
+        "2d": {
+            "data_identifier": "nnUNetPlans_2d",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 492,
+            "patch_size": [
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+                80
+            ],
+            "median_image_size_in_voxels": [
+                80.0,
+                80.0
+            ],
+            "spacing": [
+                6.0,
+                6.0
+            ],
+            "normalization_schemes": [
+                "CTNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                4,
+                4
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
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+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
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+                [
+                    3,
+                    3
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+            ],
+            "unet_max_num_features": 512,
+            "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": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
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+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "batch_dice": true
+        },
+        "3d_fullres": {
+            "data_identifier": "nnUNetPlans_3d_fullres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 4,
+            "patch_size": [
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+                80,
+                80
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+            "median_image_size_in_voxels": [
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+                6.0,
+                6.0
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+            "normalization_schemes": [
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+            ],
+            "use_mask_for_norm": [
+                false
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+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
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+                2,
+                2,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
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+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                4,
+                4,
+                4
+            ],
+            "pool_op_kernel_sizes": [
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+                    1,
+                    1
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+                    2,
+                    2
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+                    2,
+                    2
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+                    2,
+                    2,
+                    2
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+                    2,
+                    2
+                ]
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+            "conv_kernel_sizes": [
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+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
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+                [
+                    3,
+                    3,
+                    3
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+                [
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+                    3,
+                    3
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+            "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": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "batch_dice": false
+        }
+    },
+    "experiment_planner_used": "ExperimentPlanner",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
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+            "max": 2981.83154296875,
+            "mean": -306.5704650878906,
+            "median": -7.578986644744873,
+            "min": -1138.905029296875,
+            "percentile_00_5": -952.3096923828125,
+            "percentile_99_5": 193.60693359375,
+            "std": 407.40484619140625
+        }
+    }
+}