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.gitattributes

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Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres/crossval_results_folds_0_1_2_3_4/plans.json

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Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres/crossval_results_folds_0_1_2_3_4/postprocessing.json

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+}

Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres/crossval_results_folds_0_1_2_3_4/postprocessing.pkl

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Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres/dataset.json

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+{
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+        "myocardium": 1
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+    "numTraining": 2021,
+    "file_ending": ".nii.gz"
+}

Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres/fold_0/checkpoint_best.pth

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Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres/fold_0/debug.json

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+    "configuration_name": "3d_fullres",
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+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader.nnUNetDataLoader object at 0x7fcd678011f0>",
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+    "dataloader_train.num_processes": "12",
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+    "dataloader_val.num_processes": "6",
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+    "dataset_json": "{'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'myocardium': 1}, 'numTraining': 2021, 'file_ending': '.nii.gz'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "enable_deep_supervision": "True",
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+    "local_rank": "0",
+    "log_file": "/root/LVM/nnUNet_work/original/full/results/Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres/fold_0/training_log_2026_5_26_15_37_57.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.MetaLogger object at 0x7fcd9500a900>",
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+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7fcd92ed8830>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset411_NCallin', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 0.38999998569488525, 0.38999998569488525], 'original_median_shape_after_transp': [46, 512, 512], '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': 35, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 512.0], 'spacing': [0.38999998569488525, 0.38999998569488525], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 320, 320], 'median_image_size_in_voxels': [55.0, 512.0, 512.0], 'spacing': [2.5, 0.38999998569488525, 0.38999998569488525], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 21853.0, 'mean': 35.430694580078125, 'median': 41.0, 'min': -5361.0, 'percentile_00_5': -107.0, 'percentile_99_5': 101.0, 'std': 35.935302734375}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'myocardium': 1}, 'numTraining': 2021, 'file_ending': '.nii.gz'}, 'device': device(type='cuda')}",
+    "network": "OptimizedModule",
+    "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": "/root/LVM/nnUNet_work/original/full/results/Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres/fold_0",
+    "output_folder_base": "/root/LVM/nnUNet_work/original/full/results/Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
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+    "preprocessed_dataset_folder": "/root/nnUNet_preprocessed/Dataset411_NCallin/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/root/nnUNet_preprocessed/Dataset411_NCallin",
+    "probabilistic_oversampling": "False",
+    "save_every": "5",
+    "torch_version": "2.5.1+cu121",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

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+    "batch_size": "2",
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+    "configuration_name": "3d_fullres",
+    "cudnn_version": 90100,
+    "current_epoch": "0",
+    "dataloader_train": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x7ccdcb5a92b0>",
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+    "dataloader_train.generator.transforms": "ComposeTransforms( transforms = [Convert3DTo2DTransform(  ), SpatialTransform( patch_size = [320, 320], patch_center_dist_from_border = [0, 0], random_crop = False, p_elastic_deform = 0, elastic_deform_scale = (0, 0.2), elastic_deform_magnitude = (0, 0.2), p_rotation = 0.2, rotation = (-3.141592653589793, 3.141592653589793), p_rot_per_axis = 1, p_scaling = 0.2, scaling = (0.7, 1.4), p_synchronize_scaling_across_axes = 1, p_synchronize_def_scale_across_axes = 0, bg_style_seg_sampling = False, mode_seg = 'bilinear', border_mode_seg = 'constant', center_deformation = True, mode_image = 'bilinear', padding_mode_image = 'zeros', padding_value_seg = -1, padding_value_image = 0, align_corners = False, _grid_cache = {} ), Convert2DTo3DTransform(  ), RandomTransform(p=0.1, transform=GaussianNoiseTransform( noise_variance = (0, 0.1), p_per_channel = 1, synchronize_channels = True )), RandomTransform(p=0.2, transform=GaussianBlurTransform( blur_sigma = (0.5, 1.0), benchmark = True, synchronize_channels = False, synchronize_axes = False, p_per_channel = 0.5, benchmark_use_fft = {}, benchmark_num_runs = 9 )), RandomTransform(p=0.15, transform=MultiplicativeBrightnessTransform( multiplier_range = BGContrast(contrast_range=(0.75, 1.25)), synchronize_channels = False, p_per_channel = 1 )), RandomTransform(p=0.15, transform=ContrastTransform( contrast_range = BGContrast(contrast_range=(0.75, 1.25)), preserve_range = True, synchronize_channels = False, p_per_channel = 1.0 )), RandomTransform(p=0.25, transform=SimulateLowResolutionTransform( scale = (0.5, 1), synchronize_channels = False, synchronize_axes = True, ignore_axes = (0,), allowed_channels = None, p_per_channel = 0.5, upmodes = {1: 'linear', 2: 'bilinear', 3: 'trilinear'} )), RandomTransform(p=0.1, transform=GammaTransform( gamma = BGContrast(contrast_range=(0.7, 1.5)), p_invert_image = 1.0, synchronize_channels = False, p_per_channel = 1.0, p_retain_stats = 1.0 )), RandomTransform(p=0.3, transform=GammaTransform( gamma = BGContrast(contrast_range=(0.7, 1.5)), p_invert_image = 0.0, synchronize_channels = False, p_per_channel = 1.0, p_retain_stats = 1.0 )), RemoveLabelTransform( segmentation_channels = None, label_value = -1, set_to = 0 ), DownsampleSegForDSTransform( ds_scales = [[np.float64(1.0), np.float64(1.0), np.float64(1.0)], [np.float64(1.0), np.float64(0.5), np.float64(0.5)], [np.float64(1.0), np.float64(0.25), np.float64(0.25)], [np.float64(0.5), np.float64(0.125), np.float64(0.125)], [np.float64(0.25), np.float64(0.0625), np.float64(0.0625)], [np.float64(0.125), np.float64(0.03125), np.float64(0.03125)]] )] )",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "None",
+    "dataloader_val": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x7ccdcb5a9190>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader.nnUNetDataLoader object at 0x7ccdcb5a9160>",
+    "dataloader_val.generator.transforms": "ComposeTransforms( transforms = [RemoveLabelTransform( segmentation_channels = None, label_value = -1, set_to = 0 ), DownsampleSegForDSTransform( ds_scales = [[np.float64(1.0), np.float64(1.0), np.float64(1.0)], [np.float64(1.0), np.float64(0.5), np.float64(0.5)], [np.float64(1.0), np.float64(0.25), np.float64(0.25)], [np.float64(0.5), np.float64(0.125), np.float64(0.125)], [np.float64(0.25), np.float64(0.0625), np.float64(0.0625)], [np.float64(0.125), np.float64(0.03125), np.float64(0.03125)]] )] )",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "None",
+    "dataset_json": "{'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'myocardium': 1}, 'numTraining': 2021, 'file_ending': '.nii.gz'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "enable_deep_supervision": "True",
+    "fold": "1",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA GeForce RTX 3090",
+    "grad_scaler": "<torch.amp.grad_scaler.GradScaler object at 0x7ccdf9747830>",
+    "hostname": "26f43e628e81",
+    "inference_allowed_mirroring_axes": "None",
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+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7ccdf911a360>",
+    "local_rank": "0",
+    "log_file": "/root/LVM/nnUNet_work/original/full/results/Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres/fold_1/training_log_2026_5_31_14_32_34.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.MetaLogger object at 0x7ccdf8ed7f20>",
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+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7ccdcb58fe90>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset411_NCallin', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 0.38999998569488525, 0.38999998569488525], 'original_median_shape_after_transp': [46, 512, 512], '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': 35, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 512.0], 'spacing': [0.38999998569488525, 0.38999998569488525], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 320, 320], 'median_image_size_in_voxels': [55.0, 512.0, 512.0], 'spacing': [2.5, 0.38999998569488525, 0.38999998569488525], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 21853.0, 'mean': 35.430694580078125, 'median': 41.0, 'min': -5361.0, 'percentile_00_5': -107.0, 'percentile_99_5': 101.0, 'std': 35.935302734375}}}, 'configuration': '3d_fullres', 'fold': 1, 'dataset_json': {'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'myocardium': 1}, 'numTraining': 2021, 'file_ending': '.nii.gz'}, 'device': device(type='cuda')}",
+    "network": "OptimizedModule",
+    "num_epochs": "2000",
+    "num_input_channels": "1",
+    "num_iterations_per_epoch": "250",
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+    "preprocessed_dataset_folder": "/root/nnUNet_preprocessed/Dataset411_NCallin/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/root/nnUNet_preprocessed/Dataset411_NCallin",
+    "probabilistic_oversampling": "False",
+    "save_every": "5",
+    "torch_version": "2.5.1+cu121",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
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+    "configuration_name": "3d_fullres",
+    "cudnn_version": 90100,
+    "current_epoch": "220",
+    "dataloader_train": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x7c4229c613d0>",
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+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "None",
+    "dataloader_val": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x7c4229adfbf0>",
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+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "None",
+    "dataset_json": "{'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'myocardium': 1}, 'numTraining': 2021, 'file_ending': '.nii.gz'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "enable_deep_supervision": "True",
+    "fold": "2",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA GeForce RTX 3090",
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+    "hostname": "26f43e628e81",
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+    "local_rank": "0",
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+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7c4229a57800>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset411_NCallin', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 0.38999998569488525, 0.38999998569488525], 'original_median_shape_after_transp': [46, 512, 512], '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': 35, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 512.0], 'spacing': [0.38999998569488525, 0.38999998569488525], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 320, 320], 'median_image_size_in_voxels': [55.0, 512.0, 512.0], 'spacing': [2.5, 0.38999998569488525, 0.38999998569488525], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 21853.0, 'mean': 35.430694580078125, 'median': 41.0, 'min': -5361.0, 'percentile_00_5': -107.0, 'percentile_99_5': 101.0, 'std': 35.935302734375}}}, 'configuration': '3d_fullres', 'fold': 2, 'dataset_json': {'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'myocardium': 1}, 'numTraining': 2021, 'file_ending': '.nii.gz'}, 'device': device(type='cuda')}",
+    "network": "OptimizedModule",
+    "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: 0.009008874385664096\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
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+    "output_folder_base": "/root/LVM/nnUNet_work/original/full/results/Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
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+    "preprocessed_dataset_folder": "/root/nnUNet_preprocessed/Dataset411_NCallin/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/root/nnUNet_preprocessed/Dataset411_NCallin",
+    "probabilistic_oversampling": "False",
+    "save_every": "5",
+    "torch_version": "2.5.1+cu121",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
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+    "batch_size": "2",
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+    "configuration_name": "3d_fullres",
+    "cudnn_version": 90100,
+    "current_epoch": "0",
+    "dataloader_train": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x7c7d2a5ed4c0>",
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+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "None",
+    "dataloader_val": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x7c7d2a6f0c80>",
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+    "dataloader_val.generator.transforms": "ComposeTransforms( transforms = [RemoveLabelTransform( segmentation_channels = None, label_value = -1, set_to = 0 ), DownsampleSegForDSTransform( ds_scales = [[np.float64(1.0), np.float64(1.0), np.float64(1.0)], [np.float64(1.0), np.float64(0.5), np.float64(0.5)], [np.float64(1.0), np.float64(0.25), np.float64(0.25)], [np.float64(0.5), np.float64(0.125), np.float64(0.125)], [np.float64(0.25), np.float64(0.0625), np.float64(0.0625)], [np.float64(0.125), np.float64(0.03125), np.float64(0.03125)]] )] )",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "None",
+    "dataset_json": "{'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'myocardium': 1}, 'numTraining': 2021, 'file_ending': '.nii.gz'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "enable_deep_supervision": "True",
+    "fold": "3",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA GeForce RTX 3090",
+    "grad_scaler": "<torch.amp.grad_scaler.GradScaler object at 0x7c7d3023e930>",
+    "hostname": "26f43e628e81",
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+    "is_cascaded": "False",
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+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7c7d3311af00>",
+    "local_rank": "0",
+    "log_file": "/root/LVM/nnUNet_work/original/full/results/Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres/fold_3/training_log_2026_6_10_08_54_03.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.MetaLogger object at 0x7c7d2fe2c8c0>",
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+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7c7d2fecb140>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset411_NCallin', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 0.38999998569488525, 0.38999998569488525], 'original_median_shape_after_transp': [46, 512, 512], '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': 35, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 512.0], 'spacing': [0.38999998569488525, 0.38999998569488525], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 320, 320], 'median_image_size_in_voxels': [55.0, 512.0, 512.0], 'spacing': [2.5, 0.38999998569488525, 0.38999998569488525], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 21853.0, 'mean': 35.430694580078125, 'median': 41.0, 'min': -5361.0, 'percentile_00_5': -107.0, 'percentile_99_5': 101.0, 'std': 35.935302734375}}}, 'configuration': '3d_fullres', 'fold': 3, 'dataset_json': {'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'myocardium': 1}, 'numTraining': 2021, 'file_ending': '.nii.gz'}, 'device': device(type='cuda')}",
+    "network": "OptimizedModule",
+    "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)",
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+    "output_folder_base": "/root/LVM/nnUNet_work/original/full/results/Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres",
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+    "preprocessed_dataset_folder": "/root/nnUNet_preprocessed/Dataset411_NCallin/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/root/nnUNet_preprocessed/Dataset411_NCallin",
+    "probabilistic_oversampling": "False",
+    "save_every": "5",
+    "torch_version": "2.5.1+cu121",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

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+    "configuration_name": "3d_fullres",
+    "cudnn_version": 90100,
+    "current_epoch": "1460",
+    "dataloader_train": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x77209a759c70>",
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+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "None",
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+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "None",
+    "dataset_json": "{'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'myocardium': 1}, 'numTraining': 2021, 'file_ending': '.nii.gz'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "enable_deep_supervision": "True",
+    "fold": "4",
+    "folder_with_segs_from_previous_stage": "None",
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+    "hostname": "26f43e628e81",
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+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x771f25f3aab0>",
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+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): OptimizedModule(\n      (_orig_mod): MemoryEfficientSoftDiceLoss()\n    )\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x771f220d4620>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset411_NCallin', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 0.38999998569488525, 0.38999998569488525], 'original_median_shape_after_transp': [46, 512, 512], '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': 35, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 512.0], 'spacing': [0.38999998569488525, 0.38999998569488525], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 320, 320], 'median_image_size_in_voxels': [55.0, 512.0, 512.0], 'spacing': [2.5, 0.38999998569488525, 0.38999998569488525], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 21853.0, 'mean': 35.430694580078125, 'median': 41.0, 'min': -5361.0, 'percentile_00_5': -107.0, 'percentile_99_5': 101.0, 'std': 35.935302734375}}}, 'configuration': '3d_fullres', 'fold': 4, 'dataset_json': {'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'myocardium': 1}, 'numTraining': 2021, 'file_ending': '.nii.gz'}, 'device': device(type='cuda')}",
+    "network": "OptimizedModule",
+    "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: 0.003082836856470182\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "/root/LVM/nnUNet_work/original/full/results/Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres/fold_4",
+    "output_folder_base": "/root/LVM/nnUNet_work/original/full/results/Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset411_NCallin', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 0.38999998569488525, 0.38999998569488525], 'original_median_shape_after_transp': [46, 512, 512], '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': 35, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 512.0], 'spacing': [0.38999998569488525, 0.38999998569488525], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 320, 320], 'median_image_size_in_voxels': [55.0, 512.0, 512.0], 'spacing': [2.5, 0.38999998569488525, 0.38999998569488525], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 320, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 21853.0, 'mean': 35.430694580078125, 'median': 41.0, 'min': -5361.0, 'percentile_00_5': -107.0, 'percentile_99_5': 101.0, 'std': 35.935302734375}}}",
+    "preprocessed_dataset_folder": "/root/nnUNet_preprocessed/Dataset411_NCallin/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/root/nnUNet_preprocessed/Dataset411_NCallin",
+    "probabilistic_oversampling": "False",
+    "save_every": "5",
+    "torch_version": "2.5.1+cu121",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset411_NCallin/nnUNetTrainer_2000epochs_NoMirroring__nnUNetResEncUNetLPlans__3d_fullres/plans.json

@@ -0,0 +1,213 @@
+{
+    "dataset_name": "Dataset411_NCallin",
+    "plans_name": "nnUNetResEncUNetLPlans",
+    "original_median_spacing_after_transp": [
+        3.0,
+        0.38999998569488525,
+        0.38999998569488525
+    ],
+    "original_median_shape_after_transp": [
+        46,
+        512,
+        512
+    ],
+    "image_reader_writer": "SimpleITKIO",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
+        "3d_fullres": {
+            "data_identifier": "nnUNetPlans_3d_fullres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                40,
+                320,
+                320
+            ],
+            "median_image_size_in_voxels": [
+                55.0,
+                512.0,
+                512.0
+            ],
+            "spacing": [
+                2.5,
+                0.38999998569488525,
+                0.38999998569488525
+            ],
+            "normalization_schemes": [
+                "CTNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "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
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+                "arch_kwargs": {
+                    "n_stages": 7,
+                    "features_per_stage": [
+                        32,
+                        64,
+                        128,
+                        256,
+                        320,
+                        320,
+                        320
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv3d",
+                    "kernel_sizes": [
+                        [
+                            1,
+                            3,
+                            3
+                        ],
+                        [
+                            1,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ]
+                    ],
+                    "strides": [
+                        [
+                            1,
+                            1,
+                            1
+                        ],
+                        [
+                            1,
+                            2,
+                            2
+                        ],
+                        [
+                            1,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            1,
+                            2,
+                            2
+                        ]
+                    ],
+                    "n_blocks_per_stage": [
+                        1,
+                        3,
+                        4,
+                        6,
+                        6,
+                        6,
+                        6
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": false
+        }
+    },
+    "experiment_planner_used": "nnUNetPlannerResEncL",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 21853.0,
+            "mean": 35.430694580078125,
+            "median": 41.0,
+            "min": -5361.0,
+            "percentile_00_5": -107.0,
+            "percentile_99_5": 101.0,
+            "std": 35.935302734375
+        }
+    }
+}

LICENSE

@@ -0,0 +1,41 @@
+# AI²Lab Source Code License (National Taiwan University)
+
+1. Definitions
+“Licensor” means any person or entity that distributes its Work under this license.
+
+“Work” means (a) the original work of authorship made available under this license, which may include software, documentation, or other files, and (b) any additions to or derivative works thereof that are made available under this license.
+
+The terms “reproduce,” “reproduction,” “derivative works,” and “distribution” have the meaning as defined under U.S. copyright law; however, for the purposes of this license, derivative works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work.
+
+A Work is “made available” under this license when it includes either (a) a copyright notice referencing the applicability of this license to the Work, or (b) a copy of this license.
+
+2. License Grant
+2.1 Copyright Grant
+Subject to the terms and conditions of this license, each Licensor hereby grants you a perpetual, worldwide, non-exclusive, royalty-free copyright license to use, reproduce, prepare derivative works of, publicly display, publicly perform, sublicense, and distribute its Work and any resulting derivative works in any form.
+
+3. Limitations
+3.1 Redistribution
+You may reproduce or distribute the Work only if: (a) you do so under this license; (b) you include a complete copy of this license with your distribution; and (c) you retain without modification all copyright, patent, trademark, and attribution notices that are present in the Work.
+
+3.2 Derivative Works
+You may apply additional or different terms to your derivative works (“Your Terms”) only if: (a) Your Terms state that the use limitation in Section 3.3 also applies to your derivative works; and (b) you clearly identify the specific derivative works that are subject to Your Terms. Notwithstanding Your Terms, this license (including Section 3.1) will continue to apply to the original Work.
+
+3.3 Use Limitation
+The Work and any derivative works thereof may only be used for non-commercial purposes, including academic research and evaluation.
+However, AI²Lab, National Taiwan University, and its authorized collaborators may use the Work and any derivative works for commercial purposes.
+For the purposes of this license, “non-commercial” means not primarily intended for or directed towards commercial advantage or monetary compensation.
+
+3.4 Patent Claims
+If you initiate or threaten to initiate a patent claim against any Licensor (including a cross-claim or counterclaim in a lawsuit) alleging that the Work infringes your patent, then your rights under this license from such Licensor will terminate immediately.
+
+3.5 Trademarks
+This license does not grant any rights to use the names, logos, or trademarks of any Licensor or its affiliates, except as necessary to reproduce the notices described in this license.
+
+3.6 Termination
+If you violate any term of this license, your rights under this license will terminate immediately.
+
+4. Disclaimer of Warranty
+THE WORK IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE, OR NON-INFRINGEMENT. YOU BEAR THE RISK OF USING THE WORK UNDER THIS LICENSE.
+
+5. Limitation of Liability
+TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY (INCLUDING TORT, CONTRACT, OR OTHERWISE) SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THIS LICENSE OR YOUR USE OF THE WORK, EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.

README.md

@@ -0,0 +1,17 @@
+---
+license: other
+tags:
+- medical
+- myocardium
+- 3d-segmentation
+- nnunet
+- cardiology
+- ct
+- non-contrast
+license_name: ai2lab
+license_link: LICENSE
+---
+# ncLVMass-Net
+An AI model for left ventricular (LV) myocardium segmentation and mass quantification from non-contrast cardiac CT (NCCT) images. The model was trained with the [nnU-Net](https://github.com/mic-dkfz/nnunet) framework on 2,021 patients, all from Taiwan. It achieved an overall Dice score of 0.8827. To use it, provide an input image in NIfTI format (`.nii.gz`) and the model will output the myocardium mask, along with estimated LV mass.
+
+For more information, please refer to [GitHub page](https://github.com/AI-Application-and-Integration-Lab/ncLVMass-Net)