output_images/plans.json

{
    "dataset_name": "Dataset060_Merged_Def",
    "plans_name": "nnUNetPlans",
    "original_median_spacing_after_transp": [
        1.5,
        1.5,
        1.5
    ],
    "original_median_shape_after_transp": [
        237,
        259,
        319
    ],
    "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": 195,
            "patch_size": [
                224,
                288
            ],
            "median_image_size_in_voxels": [
                206.0,
                267.0
            ],
            "spacing": [
                1.5,
                1.5
            ],
            "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,
                2
            ],
            "n_conv_per_stage_decoder": [
                2,
                2,
                2,
                2,
                2
            ],
            "num_pool_per_axis": [
                5,
                5
            ],
            "pool_op_kernel_sizes": [
                [
                    1,
                    1
                ],
                [
                    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
                ]
            ],
            "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": {
                "is_seg": false,
                "order": 1,
                "order_z": 0,
                "force_separate_z": null
            },
            "batch_dice": true
        },
        "3d_fullres": {
            "data_identifier": "nnUNetPlans_3d_fullres",
            "preprocessor_name": "DefaultPreprocessor",
            "batch_size": 2,
            "patch_size": [
                256,
                160,
                224
            ],
            "median_image_size_in_voxels": [
                291.0,
                206.0,
                267.0
            ],
            "spacing": [
                1.5,
                1.5,
                1.5
            ],
            "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,
                2
            ],
            "n_conv_per_stage_decoder": [
                2,
                2,
                2,
                2,
                2
            ],
            "num_pool_per_axis": [
                5,
                5,
                5
            ],
            "pool_op_kernel_sizes": [
                [
                    1,
                    1,
                    1
                ],
                [
                    2,
                    2,
                    2
                ],
                [
                    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
                ],
                [
                    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": 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": {
        "0": {
            "max": 5805.0,
            "mean": 64.62580871582031,
            "median": 76.0,
            "min": -2048.0,
            "percentile_00_5": -981.9942016601562,
            "percentile_99_5": 1093.9758911132812,
            "std": 140.14614868164062
        }
    }
}