complete the model package

README.md

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+---
+tags:
+- monai
+- medical
+library_name: monai
+license: unknown
+---
+# Description
+A pre-trained model for volumetric (3D) multi-organ segmentation from CT image.
+
+# Model Overview
+A pre-trained Swin UNETR [1,2] for volumetric (3D) multi-organ segmentation using CT images from Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset [3].
+## Data
+The training data is from the [BTCV dataset](https://www.synapse.org/#!Synapse:syn3193805/wiki/89480/) (Please regist in `Synapse` and download the `Abdomen/RawData.zip`).
+The dataset format needs to be redefined using the following commands:
+
+```
+unzip RawData.zip
+mv RawData/Training/img/ RawData/imagesTr
+mv RawData/Training/label/ RawData/labelsTr
+mv RawData/Testing/img/ RawData/imagesTs
+```
+
+- Target: Multi-organs
+- Task: Segmentation
+- Modality: CT
+- Size: 30 3D volumes (24 Training + 6 Testing)
+
+## Training configuration
+The training was performed with at least 32GB-memory GPUs.
+
+Actual Model Input: 96 x 96 x 96
+
+## Input and output formats
+Input: 1 channel CT image
+
+Output: 14 channels: 0:Background, 1:Spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland
+
+## Performance
+A graph showing the validation mean Dice for 5000 epochs.
+
+![](./val_dice.png) <br>
+
+This model achieves the following Dice score on the validation data (our own split from the training dataset):
+
+Mean Dice = 0.8283
+
+Note that mean dice is computed in the original spacing of the input data.
+## commands example
+Execute training:
+
+```
+python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf
+```
+
+Override the `train` config to execute multi-GPU training:
+
+```
+torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.json','configs/multi_gpu_train.json']" --logging_file configs/logging.conf
+```
+
+Override the `train` config to execute evaluation with the trained model:
+
+```
+python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json']" --logging_file configs/logging.conf
+```
+
+Execute inference:
+
+```
+python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf
+```
+
+Export checkpoint to TorchScript file:
+
+TorchScript conversion is currently not supported.
+
+# Disclaimer
+This is an example, not to be used for diagnostic purposes.
+
+# References
+[1] Hatamizadeh, Ali, et al. "Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images." arXiv preprint arXiv:2201.01266 (2022). https://arxiv.org/abs/2201.01266.
+
+[2] Tang, Yucheng, et al. "Self-supervised pre-training of swin transformers for 3d medical image analysis." arXiv preprint arXiv:2111.14791 (2021). https://arxiv.org/abs/2111.14791.
+
+[3] Landman B, et al. "MICCAI multi-atlas labeling beyond the cranial vault–workshop and challenge." In Proc. of the MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge 2015 Oct (Vol. 5, p. 12).

configs/evaluate.json

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+{
+    "validate#postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "Activationsd",
+                "keys": "pred",
+                "softmax": true
+            },
+            {
+                "_target_": "Invertd",
+                "keys": [
+                    "pred",
+                    "label"
+                ],
+                "transform": "@validate#preprocessing",
+                "orig_keys": "image",
+                "meta_key_postfix": "meta_dict",
+                "nearest_interp": [
+                    false,
+                    true
+                ],
+                "to_tensor": true
+            },
+            {
+                "_target_": "AsDiscreted",
+                "keys": [
+                    "pred",
+                    "label"
+                ],
+                "argmax": [
+                    true,
+                    false
+                ],
+                "to_onehot": 14
+            },
+            {
+                "_target_": "SaveImaged",
+                "keys": "pred",
+                "meta_keys": "pred_meta_dict",
+                "output_dir": "@output_dir",
+                "resample": false,
+                "squeeze_end_dims": true
+            }
+        ]
+    },
+    "validate#handlers": [
+        {
+            "_target_": "CheckpointLoader",
+            "load_path": "$@ckpt_dir + '/model.pt'",
+            "load_dict": {
+                "model": "@network"
+            }
+        },
+        {
+            "_target_": "StatsHandler",
+            "iteration_log": false
+        },
+        {
+            "_target_": "MetricsSaver",
+            "save_dir": "@output_dir",
+            "metrics": [
+                "val_mean_dice",
+                "val_acc"
+            ],
+            "metric_details": [
+                "val_mean_dice"
+            ],
+            "batch_transform": "$monai.handlers.from_engine(['image_meta_dict'])",
+            "summary_ops": "*"
+        }
+    ],
+    "evaluating": [
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@validate#evaluator.run()"
+    ]
+}

configs/inference.json

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+{
+    "imports": [
+        "$import glob",
+        "$import os"
+    ],
+    "bundle_root": "/workspace/MONAI_Bundle/swin_unetr_btcv_segmentation/",
+    "output_dir": "$@bundle_root + '/eval'",
+    "dataset_dir": "/dataset/dataset0",
+    "datalist": "$list(sorted(glob.glob(@dataset_dir + '/imagesTs/*.nii.gz')))",
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "network_def": {
+        "_target_": "SwinUNETR",
+        "spatial_dims": 3,
+        "img_size": 96,
+        "in_channels": 1,
+        "out_channels": 14,
+        "feature_size": 48,
+        "use_checkpoint": true
+    },
+    "network": "$@network_def.to(@device)",
+    "preprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "LoadImaged",
+                "keys": "image"
+            },
+            {
+                "_target_": "EnsureChannelFirstd",
+                "keys": "image"
+            },
+            {
+                "_target_": "Orientationd",
+                "keys": "image",
+                "axcodes": "RAS"
+            },
+            {
+                "_target_": "Spacingd",
+                "keys": "image",
+                "pixdim": [
+                    1.5,
+                    1.5,
+                    2.0
+                ],
+                "mode": "bilinear"
+            },
+            {
+                "_target_": "ScaleIntensityRanged",
+                "keys": "image",
+                "a_min": -175,
+                "a_max": 250,
+                "b_min": 0.0,
+                "b_max": 1.0,
+                "clip": true
+            },
+            {
+                "_target_": "EnsureTyped",
+                "keys": "image"
+            }
+        ]
+    },
+    "dataset": {
+        "_target_": "Dataset",
+        "data": "$[{'image': i} for i in @datalist]",
+        "transform": "@preprocessing"
+    },
+    "dataloader": {
+        "_target_": "DataLoader",
+        "dataset": "@dataset",
+        "batch_size": 1,
+        "shuffle": false,
+        "num_workers": 4
+    },
+    "inferer": {
+        "_target_": "SlidingWindowInferer",
+        "roi_size": [
+            96,
+            96,
+            96
+        ],
+        "sw_batch_size": 4,
+        "overlap": 0.5
+    },
+    "postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "Activationsd",
+                "keys": "pred",
+                "softmax": true
+            },
+            {
+                "_target_": "Invertd",
+                "keys": "pred",
+                "transform": "@preprocessing",
+                "orig_keys": "image",
+                "meta_key_postfix": "meta_dict",
+                "nearest_interp": false,
+                "to_tensor": true
+            },
+            {
+                "_target_": "AsDiscreted",
+                "keys": "pred",
+                "argmax": true
+            },
+            {
+                "_target_": "SaveImaged",
+                "keys": "pred",
+                "meta_keys": "pred_meta_dict",
+                "output_dir": "@output_dir"
+            }
+        ]
+    },
+    "handlers": [
+        {
+            "_target_": "CheckpointLoader",
+            "load_path": "$@bundle_root + '/models/model.pt'",
+            "load_dict": {
+                "model": "@network"
+            }
+        },
+        {
+            "_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
+    },
+    "evaluating": [
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@evaluator.run()"
+    ]
+}

configs/logging.conf

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+[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

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+{
+    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
+    "version": "0.1.0",
+    "changelog": {
+        "0.1.0": "complete the model package",
+        "0.0.1": "initialize the model package structure"
+    },
+    "monai_version": "0.9.0",
+    "pytorch_version": "1.10.0",
+    "numpy_version": "1.21.2",
+    "optional_packages_version": {
+        "nibabel": "3.2.1",
+        "pytorch-ignite": "0.4.8",
+        "einops": "0.4.1"
+    },
+    "task": "BTCV multi-organ segmentation",
+    "description": "A pre-trained model for volumetric (3D) multi-organ segmentation from CT image",
+    "authors": "MONAI team",
+    "copyright": "Copyright (c) MONAI Consortium",
+    "data_source": "RawData.zip from https://www.synapse.org/#!Synapse:syn3193805/wiki/217752/",
+    "data_type": "nibabel",
+    "image_classes": "single channel data, intensity scaled to [0, 1]",
+    "label_classes": "multi-channel data,0:background,1:spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland",
+    "pred_classes": "14 channels OneHot data, 0:background,1:spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland",
+    "eval_metrics": {
+        "mean_dice": 0.8283
+    },
+    "intended_use": "This is an example, not to be used for diagnostic purposes",
+    "references": [
+        "Hatamizadeh, Ali, et al. 'Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images. arXiv preprint arXiv:2201.01266 (2022). https://arxiv.org/abs/2201.01266.",
+        "Tang, Yucheng, et al. 'Self-supervised pre-training of swin transformers for 3d medical image analysis. arXiv preprint arXiv:2111.14791 (2021). https://arxiv.org/abs/2111.14791."
+    ],
+    "network_data_format": {
+        "inputs": {
+            "image": {
+                "type": "image",
+                "format": "hounsfield",
+                "modality": "CT",
+                "num_channels": 1,
+                "spatial_shape": [
+                    96,
+                    96,
+                    96
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": true,
+                "channel_def": {
+                    "0": "image"
+                }
+            }
+        },
+        "outputs": {
+            "pred": {
+                "type": "image",
+                "format": "segmentation",
+                "num_channels": 14,
+                "spatial_shape": [
+                    96,
+                    96,
+                    96
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": true,
+                "channel_def": {
+                    "0": "background",
+                    "1": "spleen",
+                    "2": "Right Kidney",
+                    "3": "Left Kideny",
+                    "4": "Gallbladder",
+                    "5": "Esophagus",
+                    "6": "Liver",
+                    "7": "Stomach",
+                    "8": "Aorta",
+                    "9": "IVC",
+                    "10": "Portal and Splenic Veins",
+                    "11": "Pancreas",
+                    "12": "Right adrenal gland",
+                    "13": "Left adrenal gland"
+                }
+            }
+        }
+    }
+}

configs/multi_gpu_train.json

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+{
+    "device": "$torch.device(f'cuda:{dist.get_rank()}')",
+    "network": {
+        "_target_": "torch.nn.parallel.DistributedDataParallel",
+        "module": "$@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": "$@train#handlers[: -2 if dist.get_rank() > 0 else None]",
+    "validate#sampler": {
+        "_target_": "DistributedSampler",
+        "dataset": "@validate#dataset",
+        "even_divisible": false,
+        "shuffle": false
+    },
+    "validate#dataloader#sampler": "@validate#sampler",
+    "validate#evaluator#val_handlers": "$None if dist.get_rank() > 0 else @validate#handlers",
+    "training": [
+        "$import torch.distributed as dist",
+        "$dist.init_process_group(backend='nccl')",
+        "$torch.cuda.set_device(@device)",
+        "$monai.utils.set_determinism(seed=123)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@train#trainer.run()",
+        "$dist.destroy_process_group()"
+    ]
+}

configs/train.json

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+{
+    "imports": [
+        "$import glob",
+        "$import os",
+        "$import ignite"
+    ],
+    "bundle_root": "/workspace/MONAI_Bundle/swin_unetr_btcv_segmentation/",
+    "ckpt_dir": "$@bundle_root + '/models'",
+    "output_dir": "$@bundle_root + '/eval'",
+    "dataset_dir": "/dataset/dataset0",
+    "images": "$list(sorted(glob.glob(@dataset_dir + '/imagesTr/*.nii.gz')))",
+    "labels": "$list(sorted(glob.glob(@dataset_dir + '/labelsTr/*.nii.gz')))",
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "network_def": {
+        "_target_": "SwinUNETR",
+        "spatial_dims": 3,
+        "img_size": 96,
+        "in_channels": 1,
+        "out_channels": 14,
+        "feature_size": 48,
+        "use_checkpoint": true
+    },
+    "network": "$@network_def.to(@device)",
+    "loss": {
+        "_target_": "DiceCELoss",
+        "to_onehot_y": true,
+        "softmax": true,
+        "squared_pred": true,
+        "batch": true
+    },
+    "optimizer": {
+        "_target_": "torch.optim.Adam",
+        "params": "$@network.parameters()",
+        "lr": 0.0002
+    },
+    "train": {
+        "deterministic_transforms": [
+            {
+                "_target_": "LoadImaged",
+                "keys": [
+                    "image",
+                    "label"
+                ]
+            },
+            {
+                "_target_": "EnsureChannelFirstd",
+                "keys": [
+                    "image",
+                    "label"
+                ]
+            },
+            {
+                "_target_": "Orientationd",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "axcodes": "RAS"
+            },
+            {
+                "_target_": "Spacingd",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "pixdim": [
+                    1.5,
+                    1.5,
+                    2.0
+                ],
+                "mode": [
+                    "bilinear",
+                    "nearest"
+                ]
+            },
+            {
+                "_target_": "ScaleIntensityRanged",
+                "keys": "image",
+                "a_min": -175,
+                "a_max": 250,
+                "b_min": 0.0,
+                "b_max": 1.0,
+                "clip": true
+            },
+            {
+                "_target_": "EnsureTyped",
+                "keys": [
+                    "image",
+                    "label"
+                ]
+            }
+        ],
+        "random_transforms": [
+            {
+                "_target_": "RandCropByPosNegLabeld",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "label_key": "label",
+                "spatial_size": [
+                    96,
+                    96,
+                    96
+                ],
+                "pos": 1,
+                "neg": 1,
+                "num_samples": 4,
+                "image_key": "image",
+                "image_threshold": 0
+            },
+            {
+                "_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"
+                ],
+                "max_k": 3,
+                "prob": 0.1
+            },
+            {
+                "_target_": "RandShiftIntensityd",
+                "keys": "image",
+                "offsets": 0.1,
+                "prob": 0.5
+            }
+        ],
+        "preprocessing": {
+            "_target_": "Compose",
+            "transforms": "$@train#deterministic_transforms + @train#random_transforms"
+        },
+        "dataset": {
+            "_target_": "CacheDataset",
+            "data": "$[{'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": 2,
+            "shuffle": true,
+            "num_workers": 4
+        },
+        "inferer": {
+            "_target_": "SimpleInferer"
+        },
+        "postprocessing": {
+            "_target_": "Compose",
+            "transforms": [
+                {
+                    "_target_": "Activationsd",
+                    "keys": "pred",
+                    "softmax": true
+                },
+                {
+                    "_target_": "AsDiscreted",
+                    "keys": [
+                        "pred",
+                        "label"
+                    ],
+                    "argmax": [
+                        true,
+                        false
+                    ],
+                    "to_onehot": 14
+                }
+            ]
+        },
+        "handlers": [
+            {
+                "_target_": "ValidationHandler",
+                "validator": "@validate#evaluator",
+                "epoch_level": true,
+                "interval": 5
+            },
+            {
+                "_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_accuracy": {
+                "_target_": "ignite.metrics.Accuracy",
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
+            }
+        },
+        "trainer": {
+            "_target_": "SupervisedTrainer",
+            "max_epochs": 500,
+            "device": "@device",
+            "train_data_loader": "@train#dataloader",
+            "network": "@network",
+            "loss_function": "@loss",
+            "optimizer": "@optimizer",
+            "inferer": "@train#inferer",
+            "postprocessing": "@train#postprocessing",
+            "key_train_metric": "@train#key_metric",
+            "train_handlers": "@train#handlers",
+            "amp": true
+        }
+    },
+    "validate": {
+        "preprocessing": {
+            "_target_": "Compose",
+            "transforms": "%train#deterministic_transforms"
+        },
+        "dataset": {
+            "_target_": "CacheDataset",
+            "data": "$[{'image': i, 'label': l} for i, l in zip(@images[-9:], @labels[-9:])]",
+            "transform": "@validate#preprocessing",
+            "cache_rate": 1.0
+        },
+        "dataloader": {
+            "_target_": "DataLoader",
+            "dataset": "@validate#dataset",
+            "batch_size": 1,
+            "shuffle": false,
+            "num_workers": 4
+        },
+        "inferer": {
+            "_target_": "SlidingWindowInferer",
+            "roi_size": [
+                96,
+                96,
+                96
+            ],
+            "sw_batch_size": 4,
+            "overlap": 0.5
+        },
+        "postprocessing": "%train#postprocessing",
+        "handlers": [
+            {
+                "_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",
+                "include_background": false,
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
+            }
+        },
+        "additional_metrics": {
+            "val_accuracy": {
+                "_target_": "ignite.metrics.Accuracy",
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
+            }
+        },
+        "evaluator": {
+            "_target_": "SupervisedEvaluator",
+            "device": "@device",
+            "val_data_loader": "@validate#dataloader",
+            "network": "@network",
+            "inferer": "@validate#inferer",
+            "postprocessing": "@validate#postprocessing",
+            "key_val_metric": "@validate#key_metric",
+            "additional_metrics": "@validate#additional_metrics",
+            "val_handlers": "@validate#handlers",
+            "amp": true
+        }
+    },
+    "training": [
+        "$monai.utils.set_determinism(seed=123)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@train#trainer.run()"
+    ]
+}

docs/README.md

@@ -0,0 +1,79 @@
+# Description
+A pre-trained model for volumetric (3D) multi-organ segmentation from CT image.
+
+# Model Overview
+A pre-trained Swin UNETR [1,2] for volumetric (3D) multi-organ segmentation using CT images from Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset [3].
+## Data
+The training data is from the [BTCV dataset](https://www.synapse.org/#!Synapse:syn3193805/wiki/89480/) (Please regist in `Synapse` and download the `Abdomen/RawData.zip`).
+The dataset format needs to be redefined using the following commands:
+
+```
+unzip RawData.zip
+mv RawData/Training/img/ RawData/imagesTr
+mv RawData/Training/label/ RawData/labelsTr
+mv RawData/Testing/img/ RawData/imagesTs
+```
+
+- Target: Multi-organs
+- Task: Segmentation
+- Modality: CT
+- Size: 30 3D volumes (24 Training + 6 Testing)
+
+## Training configuration
+The training was performed with at least 32GB-memory GPUs.
+
+Actual Model Input: 96 x 96 x 96
+
+## Input and output formats
+Input: 1 channel CT image
+
+Output: 14 channels: 0:Background, 1:Spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland
+
+## Performance
+A graph showing the validation mean Dice for 5000 epochs.
+
+![](./val_dice.png) <br>
+
+This model achieves the following Dice score on the validation data (our own split from the training dataset):
+
+Mean Dice = 0.8283
+
+Note that mean dice is computed in the original spacing of the input data.
+## commands example
+Execute training:
+
+```
+python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf
+```
+
+Override the `train` config to execute multi-GPU training:
+
+```
+torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.json','configs/multi_gpu_train.json']" --logging_file configs/logging.conf
+```
+
+Override the `train` config to execute evaluation with the trained model:
+
+```
+python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json']" --logging_file configs/logging.conf
+```
+
+Execute inference:
+
+```
+python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf
+```
+
+Export checkpoint to TorchScript file:
+
+TorchScript conversion is currently not supported.
+
+# Disclaimer
+This is an example, not to be used for diagnostic purposes.
+
+# References
+[1] Hatamizadeh, Ali, et al. "Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images." arXiv preprint arXiv:2201.01266 (2022). https://arxiv.org/abs/2201.01266.
+
+[2] Tang, Yucheng, et al. "Self-supervised pre-training of swin transformers for 3d medical image analysis." arXiv preprint arXiv:2111.14791 (2021). https://arxiv.org/abs/2111.14791.
+
+[3] Landman B, et al. "MICCAI multi-atlas labeling beyond the cranial vault–workshop and challenge." In Proc. of the MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge 2015 Oct (Vol. 5, p. 12).

docs/license.txt

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

models/model.pt

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