Upload spleen_deepedit_annotation version 0.5.7

.gitattributes

@@ -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
+models/model.ts filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,201 @@
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configs/evaluate.json

@@ -0,0 +1,62 @@
+{
+    "validate#dataset#cache_rate": 0,
+    "validate#postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "Activationsd",
+                "keys": "pred",
+                "softmax": true
+            },
+            {
+                "_target_": "AsDiscreted",
+                "keys": [
+                    "pred",
+                    "label"
+                ],
+                "argmax": [
+                    true,
+                    false
+                ],
+                "to_onehot": "$len(@label_names)+1"
+            },
+            {
+                "_target_": "SaveImaged",
+                "_disabled_": true,
+                "keys": "pred",
+                "output_dir": "@output_dir",
+                "resample": false,
+                "squeeze_end_dims": true
+            }
+        ]
+    },
+    "validate#handlers": [
+        {
+            "_target_": "CheckpointLoader",
+            "load_path": "$@ckpt_dir + '/model.pt'",
+            "load_dict": {
+                "model": "@network"
+            }
+        },
+        {
+            "_target_": "StatsHandler",
+            "iteration_log": false
+        },
+        {
+            "_target_": "MetricsSaver",
+            "save_dir": "@output_dir",
+            "metrics": [
+                "val_mean_dice",
+                "val_acc"
+            ],
+            "metric_details": [
+                "val_mean_dice"
+            ],
+            "batch_transform": "$lambda x: [xx['image'].meta for xx in x]",
+            "summary_ops": "*"
+        }
+    ],
+    "run": [
+        "$@validate#evaluator.run()"
+    ]
+}

configs/inference.json

@@ -0,0 +1,216 @@
+{
+    "imports": [
+        "$import glob",
+        "$import numpy",
+        "$import os",
+        "$import ignite"
+    ],
+    "bundle_root": ".",
+    "image_key": "image",
+    "output_dir": "$@bundle_root + '/eval'",
+    "output_ext": ".nii.gz",
+    "output_dtype": "$numpy.float32",
+    "output_postfix": "trans",
+    "separate_folder": true,
+    "load_pretrain": true,
+    "dataset_dir": "/workspace/Datasets/MSD_datasets/Task09_Spleen",
+    "datalist": "$list(sorted(glob.glob(@dataset_dir + '/imagesTs/*.nii.gz')))",
+    "label_names": {
+        "spleen": 1,
+        "background": 0
+    },
+    "spatial_size": [
+        128,
+        128,
+        128
+    ],
+    "number_intensity_ch": 1,
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "network_def": {
+        "_target_": "DynUNet",
+        "spatial_dims": 3,
+        "in_channels": "$len(@label_names) + @number_intensity_ch",
+        "out_channels": "$len(@label_names)",
+        "kernel_size": [
+            3,
+            3,
+            3,
+            3,
+            3,
+            3
+        ],
+        "strides": [
+            1,
+            2,
+            2,
+            2,
+            2,
+            [
+                2,
+                2,
+                1
+            ]
+        ],
+        "upsample_kernel_size": [
+            2,
+            2,
+            2,
+            2,
+            [
+                2,
+                2,
+                1
+            ]
+        ],
+        "norm_name": "instance",
+        "deep_supervision": false,
+        "res_block": true
+    },
+    "network": "$@network_def.to(@device)",
+    "preprocessing_transforms": [
+        {
+            "_target_": "LoadImaged",
+            "keys": "@image_key",
+            "reader": "ITKReader"
+        },
+        {
+            "_target_": "EnsureChannelFirstd",
+            "keys": "@image_key"
+        },
+        {
+            "_target_": "Orientationd",
+            "keys": "@image_key",
+            "axcodes": "RAS"
+        },
+        {
+            "_target_": "ScaleIntensityRanged",
+            "keys": "@image_key",
+            "a_min": -175,
+            "a_max": 250,
+            "b_min": 0.0,
+            "b_max": 1.0,
+            "clip": true
+        }
+    ],
+    "deepedit_transforms": [
+        {
+            "_target_": "scripts.transforms.OrientationGuidanceMultipleLabelDeepEditd",
+            "ref_image": "@image_key",
+            "label_names": "@label_names"
+        },
+        {
+            "_target_": "AddGuidanceFromPointsDeepEditd",
+            "ref_image": "@image_key",
+            "guidance": "guidance",
+            "label_names": "@label_names"
+        },
+        {
+            "_target_": "Resized",
+            "keys": "@image_key",
+            "spatial_size": "@spatial_size",
+            "mode": "area"
+        },
+        {
+            "_target_": "ResizeGuidanceMultipleLabelDeepEditd",
+            "guidance": "guidance",
+            "ref_image": "@image_key"
+        },
+        {
+            "_target_": "AddGuidanceSignalDeepEditd",
+            "keys": "@image_key",
+            "guidance": "guidance",
+            "number_intensity_ch": "@number_intensity_ch"
+        }
+    ],
+    "extra_transforms": [
+        {
+            "_target_": "EnsureTyped",
+            "keys": "@image_key"
+        }
+    ],
+    "preprocessing": {
+        "_target_": "Compose",
+        "transforms": "$@preprocessing_transforms + @deepedit_transforms + @extra_transforms"
+    },
+    "dataset": {
+        "_target_": "Dataset",
+        "data": "$[{'image': i} for i in @datalist]",
+        "transform": "@preprocessing"
+    },
+    "dataloader": {
+        "_target_": "DataLoader",
+        "dataset": "@dataset",
+        "batch_size": 1,
+        "shuffle": false,
+        "num_workers": 2
+    },
+    "inferer": {
+        "_target_": "SimpleInferer"
+    },
+    "postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "EnsureTyped",
+                "keys": "pred"
+            },
+            {
+                "_target_": "Activationsd",
+                "keys": "pred",
+                "softmax": true
+            },
+            {
+                "_target_": "Invertd",
+                "keys": "pred",
+                "transform": "@preprocessing",
+                "orig_keys": "@image_key",
+                "nearest_interp": false,
+                "to_tensor": true
+            },
+            {
+                "_target_": "AsDiscreted",
+                "keys": "pred",
+                "argmax": true
+            },
+            {
+                "_target_": "SaveImaged",
+                "keys": "pred",
+                "output_dir": "@output_dir",
+                "output_ext": "@output_ext",
+                "output_dtype": "@output_dtype",
+                "output_postfix": "@output_postfix",
+                "separate_folder": "@separate_folder"
+            }
+        ]
+    },
+    "handlers": [
+        {
+            "_target_": "StatsHandler",
+            "iteration_log": false
+        }
+    ],
+    "evaluator": {
+        "_target_": "SupervisedEvaluator",
+        "device": "@device",
+        "val_data_loader": "@dataloader",
+        "network": "@network",
+        "inferer": "@inferer",
+        "postprocessing": "@postprocessing",
+        "val_handlers": "@handlers",
+        "amp": true
+    },
+    "checkpointloader": {
+        "_target_": "CheckpointLoader",
+        "load_path": "$@bundle_root + '/models/model.pt'",
+        "load_dict": {
+            "model": "@network"
+        }
+    },
+    "initialize": [
+        "$monai.utils.set_determinism(seed=123)",
+        "$@checkpointloader(@evaluator) if @load_pretrain else None"
+    ],
+    "run": [
+        "$@evaluator.run()"
+    ]
+}

configs/inference_trt.json

@@ -0,0 +1,13 @@
+{
+    "imports": [
+        "$import glob",
+        "$import os",
+        "$import ignite",
+        "$import torch_tensorrt"
+    ],
+    "network_def": "$torch.jit.load(@bundle_root + '/models/model_trt.ts')",
+    "evaluator#amp": false,
+    "initialize": [
+        "$monai.utils.set_determinism(seed=123)"
+    ]
+}

configs/logging.conf

@@ -0,0 +1,21 @@
+[loggers]
+keys=root
+
+[handlers]
+keys=consoleHandler
+
+[formatters]
+keys=fullFormatter
+
+[logger_root]
+level=INFO
+handlers=consoleHandler
+
+[handler_consoleHandler]
+class=StreamHandler
+level=INFO
+formatter=fullFormatter
+args=(sys.stdout,)
+
+[formatter_fullFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(message)s

configs/metadata.json

@@ -0,0 +1,112 @@
+{
+    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
+    "version": "0.5.7",
+    "changelog": {
+        "0.5.7": "update to huggingface hosting",
+        "0.5.6": "use monai 1.4 and update large files",
+        "0.5.5": "update to use monai 1.3.1",
+        "0.5.4": "add load_pretrain flag for infer",
+        "0.5.3": "update to use monai 1.3.0",
+        "0.5.2": "update the checkpoint loader logic for inference",
+        "0.5.1": "add option to validate at training start, and I/O param entries",
+        "0.5.0": "enable finetune and early stop",
+        "0.4.9": "fix orientation issue on clicks",
+        "0.4.8": "Add infer transforms to manage clicks from viewer",
+        "0.4.7": "fix the wrong GPU index issue of multi-node",
+        "0.4.6": "update to use rc7 which solves dynunet issue",
+        "0.4.5": "remove error dollar symbol in readme",
+        "0.4.4": "add RAM comsumption with Cachedataset",
+        "0.4.3": "update ONNX-TensorRT descriptions",
+        "0.4.2": "deterministic retrain benchmark, update fig links",
+        "0.4.1": "add the ONNX-TensorRT way of model conversion",
+        "0.4.0": "fix mgpu finalize issue",
+        "0.3.9": "enable deterministic training",
+        "0.3.8": "adapt to BundleWorkflow interface",
+        "0.3.7": "add name tag",
+        "0.3.6": "restructure readme to match updated template",
+        "0.3.5": "update metric in metadata",
+        "0.3.4": "add validate.json file and dice score in readme",
+        "0.3.3": "update to use monai 1.0.1",
+        "0.3.2": "enhance readme on commands example",
+        "0.3.1": "fix license Copyright error",
+        "0.3.0": "update license files",
+        "0.2.0": "unify naming",
+        "0.1.0": "complete the model package",
+        "0.0.1": "initialize the model package structure"
+    },
+    "monai_version": "1.4.0",
+    "pytorch_version": "2.4.0",
+    "numpy_version": "1.24.4",
+    "required_packages_version": {
+        "itk": "5.4.0",
+        "pytorch-ignite": "0.4.11",
+        "scikit-image": "0.23.2",
+        "einops": "0.7.0",
+        "tensorboard": "2.17.0",
+        "nibabel": "5.2.1"
+    },
+    "supported_apps": {},
+    "name": "Spleen DeepEdit annotation",
+    "task": "Decathlon spleen segmentation",
+    "description": "This is a pre-trained model for 3D segmentation of the spleen organ from CT images using DeepEdit.",
+    "authors": "MONAI team",
+    "copyright": "Copyright (c) MONAI Consortium",
+    "data_source": "Task09_Spleen.tar from http://medicaldecathlon.com/",
+    "data_type": "nibabel",
+    "image_classes": "single channel data, intensity scaled to [0, 1]",
+    "label_classes": "single channel data, 1 is spleen, 0 is background",
+    "pred_classes": "2 channels OneHot data, channel 1 is spleen, channel 0 is background",
+    "eval_metrics": {
+        "mean_dice": 0.97
+    },
+    "intended_use": "This is an example, not to be used for diagnostic purposes",
+    "references": [
+        "Sakinis, Tomas, et al. 'Interactive segmentation of medical images through fully convolutional neural networks.' arXiv preprint arXiv:1903.08205 (2019)"
+    ],
+    "network_data_format": {
+        "inputs": {
+            "image": {
+                "type": "image",
+                "format": "hounsfield",
+                "modality": "CT",
+                "num_channels": 3,
+                "spatial_shape": [
+                    128,
+                    128,
+                    128
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "image"
+                }
+            }
+        },
+        "outputs": {
+            "pred": {
+                "type": "image",
+                "format": "segmentation",
+                "num_channels": 2,
+                "spatial_shape": [
+                    128,
+                    128,
+                    128
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "background",
+                    "1": "spleen"
+                }
+            }
+        }
+    }
+}

configs/multi_gpu_train.json

@@ -0,0 +1,40 @@
+{
+    "device": "$torch.device('cuda:' + os.environ['LOCAL_RANK'])",
+    "network": {
+        "_target_": "torch.nn.parallel.DistributedDataParallel",
+        "module": "$@network_def.to(@device)",
+        "device_ids": [
+            "@device"
+        ]
+    },
+    "train#sampler": {
+        "_target_": "DistributedSampler",
+        "dataset": "@train#dataset",
+        "even_divisible": true,
+        "shuffle": true
+    },
+    "train#dataloader#sampler": "@train#sampler",
+    "train#dataloader#shuffle": false,
+    "train#trainer#train_handlers": "$@train#handlers[: -2 if dist.get_rank() > 0 else None]",
+    "validate#sampler": {
+        "_target_": "DistributedSampler",
+        "dataset": "@validate#dataset",
+        "even_divisible": false,
+        "shuffle": false
+    },
+    "validate#dataloader#sampler": "@validate#sampler",
+    "validate#evaluator#val_handlers": "$@validate#handlers[: -3 if dist.get_rank() > 0 else None]",
+    "initialize": [
+        "$import torch.distributed as dist",
+        "$dist.is_initialized() or dist.init_process_group(backend='nccl')",
+        "$torch.cuda.set_device(@device)",
+        "$monai.utils.set_determinism(seed=123)"
+    ],
+    "run": [
+        "$@validate#handlers#0.set_trainer(trainer=@train#trainer) if @early_stop else None",
+        "$@train#trainer.run()"
+    ],
+    "finalize": [
+        "$dist.is_initialized() and dist.destroy_process_group()"
+    ]
+}

configs/train.json

@@ -0,0 +1,458 @@
+{
+    "imports": [
+        "$import glob",
+        "$import os",
+        "$import ignite",
+        "$import scripts"
+    ],
+    "bundle_root": ".",
+    "ckpt_dir": "$@bundle_root + '/models'",
+    "output_dir": "$@bundle_root + '/eval'",
+    "dataset_dir": "/workspace/Datasets/MSD_datasets/Task09_Spleen",
+    "images": "$list(sorted(glob.glob(@dataset_dir + '/imagesTr/*.nii.gz')))",
+    "labels": "$list(sorted(glob.glob(@dataset_dir + '/labelsTr/*.nii.gz')))",
+    "label_names": {
+        "spleen": 1,
+        "background": 0
+    },
+    "finetune": false,
+    "finetune_model_path": "$@bundle_root + '/models/model.pt'",
+    "early_stop": false,
+    "epochs": 500,
+    "spatial_size": [
+        128,
+        128,
+        128
+    ],
+    "number_intensity_ch": 1,
+    "deepgrow_probability_train": 0.4,
+    "deepgrow_probability_val": 1.0,
+    "val_interval": 1,
+    "val_at_start": false,
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "network_def": {
+        "_target_": "DynUNet",
+        "spatial_dims": 3,
+        "in_channels": "$len(@label_names) + @number_intensity_ch",
+        "out_channels": "$len(@label_names)",
+        "kernel_size": [
+            3,
+            3,
+            3,
+            3,
+            3,
+            3
+        ],
+        "strides": [
+            1,
+            2,
+            2,
+            2,
+            2,
+            [
+                2,
+                2,
+                1
+            ]
+        ],
+        "upsample_kernel_size": [
+            2,
+            2,
+            2,
+            2,
+            [
+                2,
+                2,
+                1
+            ]
+        ],
+        "norm_name": "instance",
+        "deep_supervision": false,
+        "res_block": true
+    },
+    "network": "$@network_def.to(@device)",
+    "loss": {
+        "_target_": "DiceCELoss",
+        "to_onehot_y": true,
+        "softmax": true
+    },
+    "optimizer": {
+        "_target_": "torch.optim.Adam",
+        "params": "$@network.parameters()",
+        "lr": 0.0001
+    },
+    "lr_scheduler": {
+        "_target_": "torch.optim.lr_scheduler.StepLR",
+        "optimizer": "@optimizer",
+        "step_size": 1000,
+        "gamma": 0.1
+    },
+    "train": {
+        "preprocessing_transforms": [
+            {
+                "_target_": "LoadImaged",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "reader": "ITKReader"
+            },
+            {
+                "_target_": "NormalizeLabelsInDatasetd",
+                "keys": "label",
+                "label_names": "@label_names"
+            },
+            {
+                "_target_": "EnsureChannelFirstd",
+                "keys": [
+                    "image",
+                    "label"
+                ]
+            },
+            {
+                "_target_": "Orientationd",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "axcodes": "RAS"
+            },
+            {
+                "_target_": "ScaleIntensityRanged",
+                "keys": "image",
+                "a_min": -175,
+                "a_max": 250,
+                "b_min": 0.0,
+                "b_max": 1.0,
+                "clip": true
+            }
+        ],
+        "random_transforms": [
+            {
+                "_target_": "RandFlipd",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "spatial_axis": [
+                    0
+                ],
+                "prob": 0.1
+            },
+            {
+                "_target_": "RandFlipd",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "spatial_axis": [
+                    1
+                ],
+                "prob": 0.1
+            },
+            {
+                "_target_": "RandFlipd",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "spatial_axis": [
+                    2
+                ],
+                "prob": 0.1
+            },
+            {
+                "_target_": "RandRotate90d",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "prob": 0.1,
+                "max_k": 3
+            },
+            {
+                "_target_": "RandShiftIntensityd",
+                "keys": "image",
+                "offsets": 0.1,
+                "prob": 0.5
+            }
+        ],
+        "deepedit_transforms": [
+            {
+                "_target_": "Resized",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "spatial_size": "@spatial_size",
+                "mode": [
+                    "area",
+                    "nearest"
+                ]
+            },
+            {
+                "_target_": "FindAllValidSlicesMissingLabelsd",
+                "keys": "label",
+                "sids": "sids"
+            },
+            {
+                "_target_": "AddInitialSeedPointMissingLabelsd",
+                "keys": "label",
+                "guidance": "guidance",
+                "sids": "sids"
+            },
+            {
+                "_target_": "AddGuidanceSignalDeepEditd",
+                "keys": "image",
+                "guidance": "guidance",
+                "number_intensity_ch": "@number_intensity_ch"
+            },
+            {
+                "_target_": "ToTensord",
+                "keys": [
+                    "image",
+                    "label"
+                ]
+            }
+        ],
+        "preprocessing": {
+            "_target_": "Compose",
+            "transforms": "$@train#preprocessing_transforms + @train#random_transforms + @train#deepedit_transforms"
+        },
+        "click_transforms": {
+            "_target_": "Compose",
+            "transforms": [
+                {
+                    "_target_": "Activationsd",
+                    "keys": "pred",
+                    "softmax": true
+                },
+                {
+                    "_target_": "AsDiscreted",
+                    "keys": "pred",
+                    "argmax": true
+                },
+                {
+                    "_target_": "ToNumpyd",
+                    "keys": [
+                        "image",
+                        "label",
+                        "pred"
+                    ]
+                },
+                {
+                    "_target_": "FindDiscrepancyRegionsDeepEditd",
+                    "keys": "label",
+                    "pred": "pred",
+                    "discrepancy": "discrepancy"
+                },
+                {
+                    "_target_": "AddRandomGuidanceDeepEditd",
+                    "keys": "NA",
+                    "guidance": "guidance",
+                    "discrepancy": "discrepancy",
+                    "probability": "probability"
+                },
+                {
+                    "_target_": "AddGuidanceSignalDeepEditd",
+                    "keys": "image",
+                    "guidance": "guidance",
+                    "number_intensity_ch": "@number_intensity_ch"
+                },
+                {
+                    "_target_": "ToTensord",
+                    "keys": [
+                        "image",
+                        "label"
+                    ]
+                }
+            ]
+        },
+        "dataset": {
+            "_target_": "CacheDataset",
+            "data": "$[{'image': i, 'label': l} for i, l in zip(@images[:-9], @labels[:-9])]",
+            "transform": "@train#preprocessing",
+            "cache_rate": 1.0,
+            "num_workers": 4
+        },
+        "dataloader": {
+            "_target_": "DataLoader",
+            "dataset": "@train#dataset",
+            "batch_size": 1,
+            "shuffle": true,
+            "num_workers": 0
+        },
+        "inferer": {
+            "_target_": "SimpleInferer"
+        },
+        "postprocessing": {
+            "_target_": "Compose",
+            "transforms": [
+                {
+                    "_target_": "Activationsd",
+                    "keys": "pred",
+                    "softmax": true
+                },
+                {
+                    "_target_": "AsDiscreted",
+                    "keys": [
+                        "pred",
+                        "label"
+                    ],
+                    "argmax": [
+                        true,
+                        false
+                    ],
+                    "to_onehot": "$len(@label_names)+1"
+                }
+            ]
+        },
+        "handlers": [
+            {
+                "_target_": "CheckpointLoader",
+                "_disabled_": "$not @finetune",
+                "load_path": "@finetune_model_path",
+                "load_dict": {
+                    "model": "@network"
+                }
+            },
+            {
+                "_target_": "LrScheduleHandler",
+                "lr_scheduler": "@lr_scheduler",
+                "print_lr": true
+            },
+            {
+                "_target_": "ValidationHandler",
+                "validator": "@validate#evaluator",
+                "epoch_level": true,
+                "exec_at_start": "@val_at_start",
+                "interval": "@val_interval"
+            },
+            {
+                "_target_": "StatsHandler",
+                "tag_name": "train_loss",
+                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
+            },
+            {
+                "_target_": "TensorBoardStatsHandler",
+                "log_dir": "@output_dir",
+                "tag_name": "train_loss",
+                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
+            }
+        ],
+        "key_metric": {
+            "train_dice": {
+                "_target_": "MeanDice",
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
+            }
+        },
+        "train_iteration_update": {
+            "_target_": "Interaction",
+            "deepgrow_probability": "@deepgrow_probability_train",
+            "transforms": "@train#click_transforms",
+            "click_probability_key": "probability",
+            "train": true,
+            "label_names": "@label_names"
+        },
+        "trainer": {
+            "_target_": "SupervisedTrainer",
+            "device": "@device",
+            "max_epochs": "@epochs",
+            "train_data_loader": "@train#dataloader",
+            "network": "@network",
+            "optimizer": "@optimizer",
+            "loss_function": "@loss",
+            "inferer": "@train#inferer",
+            "amp": true,
+            "postprocessing": "@train#postprocessing",
+            "key_train_metric": "@train#key_metric",
+            "train_handlers": "@train#handlers",
+            "iteration_update": "@train#train_iteration_update"
+        }
+    },
+    "validate": {
+        "preprocessing": {
+            "_target_": "Compose",
+            "transforms": "$@train#preprocessing_transforms + @train#deepedit_transforms"
+        },
+        "dataset": {
+            "_target_": "CacheDataset",
+            "data": "$[{'image': i, 'label': l} for i, l in zip(@images[-9:], @labels[-9:])]",
+            "transform": "@validate#preprocessing",
+            "cache_rate": 1.0,
+            "num_workers": 4
+        },
+        "dataloader": {
+            "_target_": "DataLoader",
+            "dataset": "@validate#dataset",
+            "batch_size": 1,
+            "shuffle": false,
+            "num_workers": 0
+        },
+        "inferer": {
+            "_target_": "SimpleInferer"
+        },
+        "postprocessing": "%train#postprocessing",
+        "handlers": [
+            {
+                "_target_": "EarlyStopHandler",
+                "_disabled_": "$not @early_stop",
+                "trainer": null,
+                "patience": 1,
+                "score_function": "$scripts.score_function",
+                "min_delta": 0.01
+            },
+            {
+                "_target_": "StatsHandler",
+                "iteration_log": false
+            },
+            {
+                "_target_": "TensorBoardStatsHandler",
+                "log_dir": "@output_dir",
+                "iteration_log": false
+            },
+            {
+                "_target_": "CheckpointSaver",
+                "save_dir": "@ckpt_dir",
+                "save_dict": {
+                    "model": "@network"
+                },
+                "save_key_metric": true,
+                "key_metric_filename": "model.pt"
+            }
+        ],
+        "key_metric": {
+            "val_mean_dice": {
+                "_target_": "MeanDice",
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
+            }
+        },
+        "val_iteration_update": {
+            "_target_": "Interaction",
+            "deepgrow_probability": "@deepgrow_probability_val",
+            "transforms": "@train#click_transforms",
+            "click_probability_key": "probability",
+            "train": false,
+            "label_names": "@label_names"
+        },
+        "evaluator": {
+            "_target_": "SupervisedEvaluator",
+            "device": "@device",
+            "val_data_loader": "@validate#dataloader",
+            "network": "@network",
+            "inferer": "@validate#inferer",
+            "postprocessing": "@validate#postprocessing",
+            "key_val_metric": "@validate#key_metric",
+            "val_handlers": "@validate#handlers",
+            "iteration_update": "@validate#val_iteration_update",
+            "amp": true
+        }
+    },
+    "initialize": [
+        "$monai.utils.set_determinism(seed=123)"
+    ],
+    "run": [
+        "$@validate#handlers#0.set_trainer(trainer=@train#trainer) if @early_stop else None",
+        "$@train#trainer.run()"
+    ]
+}

docs/README.md

@@ -0,0 +1,167 @@
+# Model Overview
+A pre-trained model for 3D segmentation of the spleen organ from CT images using DeepEdit.
+
+DeepEdit is an algorithm that combines the power of two models in one single architecture. It allows the user to perform inference as a standard segmentation method (i.e., UNet) and interactively segment part of an image using clicks [2]. DeepEdit aims to facilitate the user experience and, at the same time, develop new active learning techniques.
+
+The model was trained on 32 images and validated on 9 images.
+
+## Data
+The training dataset is the Spleen Task from the Medical Segmentation Decathalon. Users can find more details on the datasets at http://medicaldecathlon.com/.
+
+- Target: Spleen
+- Modality: CT
+- Size: 61 3D volumes (41 Training + 20 Testing)
+- Source: Memorial Sloan Kettering Cancer Center
+- Challenge: Large-ranging foreground size
+
+## Training configuration
+The training as performed with the following:
+- GPU: at least 12GB of GPU memory
+- Actual Model Input: 128 x 128 x 128
+- AMP: True
+- Optimizer: Adam
+- Learning Rate: 1e-4
+- Loss: DiceCELoss
+
+### Input
+Three channels
+- CT image
+- Spleen Segment
+- Background Segment
+
+### Output
+Two channels
+- Label 1: spleen
+- Label 0: everything else
+
+## Performance
+
+Dice score is used for evaluating the performance of the model. This model achieves a dice score of 0.97, depending on the number of simulated clicks.
+
+#### Training Dice
+![A graph showing the train dice over 90 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_spleen_deepedit_annotation_train_dice_v2.png)
+
+#### Training Loss
+![A graph showing the training loss over 90 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_spleen_deepedit_annotation_train_loss_v2.png)
+
+#### Validation Dice
+![A graph showing the validation dice over 90 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_spleen_deepedit_annotation_val_dice_v2.png)
+
+#### TensorRT speedup
+The `spleen_deepedit_annotation` bundle supports acceleration with TensorRT through the ONNX-TensorRT method. The table below displays the speedup ratios observed on an A100 80G GPU.
+
+| method | torch_fp32(ms) | torch_amp(ms) | trt_fp32(ms) | trt_fp16(ms) | speedup amp | speedup fp32 | speedup fp16 | amp vs fp16|
+| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| model computation | 147.52 | 40.32 | 28.87 | 11.94 | 3.66 | 5.11 | 12.36 | 3.38 |
+| end2end |1292.39 | 1204.62 | 1168.09 | 1149.88 | 1.07 | 1.11 | 1.12 | 1.05 |
+
+Where:
+- `model computation` means the speedup ratio of model's inference with a random input without preprocessing and postprocessing
+- `end2end` means run the bundle end-to-end with the TensorRT based model.
+- `torch_fp32` and `torch_amp` are for the PyTorch models with or without `amp` mode.
+- `trt_fp32` and `trt_fp16` are for the TensorRT based models converted in corresponding precision.
+- `speedup amp`, `speedup fp32` and `speedup fp16` are the speedup ratios of corresponding models versus the PyTorch float32 model
+- `amp vs fp16` is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.
+
+Currently, the only available method to accelerate this model is through ONNX-TensorRT. However, the Torch-TensorRT method is under development and will be available in the near future.
+
+This result is benchmarked under:
+ - TensorRT: 8.5.3+cuda11.8
+ - Torch-TensorRT Version: 1.4.0
+ - CPU Architecture: x86-64
+ - OS: ubuntu 20.04
+ - Python version:3.8.10
+ - CUDA version: 12.0
+ - GPU models and configuration: A100 80G
+
+### Memory Consumption
+
+- Dataset Manager: CacheDataset
+- Data Size: 61 3D Volumes
+- Cache Rate: 1.0
+- Single GPU - System RAM Usage: 8.2G
+
+### Memory Consumption Warning
+
+If you face memory issues with CacheDataset, you can either switch to a regular Dataset class or lower the caching rate `cache_rate` in the configurations within range [0, 1] to minimize the System RAM requirements.
+
+## MONAI Bundle Commands
+In addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.
+
+For more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html).
+
+#### Execute training:
+
+```
+python -m monai.bundle run --config_file configs/train.json
+```
+
+Please note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using `--dataset_dir`:
+
+```
+python -m monai.bundle run --config_file configs/train.json --dataset_dir <actual dataset path>
+```
+
+#### Override the `train` config to execute multi-GPU training:
+
+```
+torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file "['configs/train.json','configs/multi_gpu_train.json']"
+```
+
+Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
+
+####  Override the `train` config to execute evaluation with the trained model:
+
+```
+python -m monai.bundle run --config_file "['configs/train.json','configs/evaluate.json']"
+```
+
+####  Execute inference:
+
+```
+python -m monai.bundle run --config_file configs/inference.json
+```
+
+Optionally, clicks can be added to the data dictionary that is passed to the preprocessing transforms. The add keys are defined in `label_names` in `configs/inference.json`, and the corresponding values are the point coordinates. The following is an example of a data dictionary:
+
+```
+{"image": "example.nii.gz", "background": [], "spleen": [[I1, J1, K1], [I2, J2, K2]]}
+```
+where **[I1,J1,K1]** and **[I2,J2,K2]** are the point coordinates.
+
+#### Export checkpoint to TensorRT based models with fp32 or fp16 precision:
+
+```bash
+python -m monai.bundle trt_export --net_id network_def \
+--filepath models/model_trt.ts --ckpt_file models/model.pt \
+--meta_file configs/metadata.json --config_file configs/inference.json \
+--precision <fp32/fp16>  --use_onnx "True" --use_trace "True"
+```
+
+#### Execute inference with the TensorRT model:
+
+```
+python -m monai.bundle run --config_file "['configs/inference.json', 'configs/inference_trt.json']"
+```
+
+# References
+[1] Diaz-Pinto, Andres, et al. DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images. MICCAI Workshop on Data Augmentation, Labelling, and Imperfections. MICCAI 2022.
+
+[2] Diaz-Pinto, Andres, et al. "MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images." arXiv preprint arXiv:2203.12362 (2022).
+
+[3] Sakinis, Tomas, et al. "Interactive segmentation of medical images through fully convolutional neural networks." arXiv preprint arXiv:1903.08205 (2019).
+
+# License
+Copyright (c) MONAI Consortium
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

docs/data_license.txt

@@ -0,0 +1,6 @@
+Third Party Licenses
+-----------------------------------------------------------------------
+
+/*********************************************************************/
+i. Medical Segmentation Decathlon
+   http://medicaldecathlon.com/

models/model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2b4f139fd4f94b1b2c616d6fc423cd3fef3291ca5e4c7262fd8ae292792c0a7b
+size 124036018

models/model.ts

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e31509a99ab6f04bbe063e7a7b2acf4570c912a4f0619ab7020bd8b7aa5ef9d5
+size 124167408

scripts/__init__.py

@@ -0,0 +1 @@
+from .early_stop_score_function import score_function

scripts/early_stop_score_function.py

@@ -0,0 +1,15 @@
+import os
+
+import torch
+import torch.distributed as dist
+
+
+def score_function(engine):
+    val_metric = engine.state.metrics["val_mean_dice"]
+    if dist.is_initialized():
+        device = torch.device("cuda:" + os.environ["LOCAL_RANK"])
+        val_metric = torch.tensor([val_metric]).to(device)
+        dist.all_reduce(val_metric, op=dist.ReduceOp.SUM)
+        val_metric /= dist.get_world_size()
+        return val_metric.item()
+    return val_metric

scripts/transforms.py

@@ -0,0 +1,38 @@
+from typing import Dict
+
+import numpy as np
+from einops import rearrange
+from monai.transforms.transform import Transform
+
+
+class OrientationGuidanceMultipleLabelDeepEditd(Transform):
+    def __init__(self, ref_image="image", label_names=None):
+        """
+        Convert the guidance to the RAS orientation
+        """
+        self.ref_image = ref_image
+        self.label_names = label_names
+
+    def transform_points(self, point, affine):
+        """transform point to the coordinates of the transformed image
+        point: numpy array [bs, N, 3]
+        """
+        bs, n = point.shape[:2]
+        point = np.concatenate((point, np.ones((bs, n, 1))), axis=-1)
+        point = rearrange(point, "b n d -> d (b n)")
+        point = affine @ point
+        point = rearrange(point, "d (b n)-> b n d", b=bs)[:, :, :3]
+        return point
+
+    def __call__(self, data):
+        d: Dict = dict(data)
+        for key_label in self.label_names.keys():
+            points = d.get(key_label, [])
+            if len(points) < 1:
+                continue
+            reoriented_points = self.transform_points(
+                np.array(points)[None],
+                np.linalg.inv(d[self.ref_image].meta["affine"].numpy()) @ d[self.ref_image].meta["original_affine"],
+            )
+            d[key_label] = reoriented_points[0]
+        return d