complete the model package

LICENSE

@@ -0,0 +1,201 @@
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README.md

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+---
+tags:
+- monai
+- medical
+library_name: monai
+license: apache-2.0
+---
+# Model Overview
+
+Body CT segmentation models are evolving. Starting from abdominal multi-organ segmentation model [1]. Now the community is developing hundreds of target anatomies. In this bundle, we provide re-trained models for (3D) segmentation of 104 whole-body segments.
+
+This model is trained using the SegResNet [3] network. The model is trained using TotalSegmentator datasets [2].
+
+![structures](https://github.com/wasserth/TotalSegmentator/blob/master/resources/imgs/overview_classes.png)
+
+Figure source from the TotalSegmentator [2].
+
+## MONAI Label Showcase
+
+- We highlight the use of this bundle to use and visualize in MONAI Label + 3D Slicer integration.
+
+![](./imgs/totalsegmentator_monailabel.png) <br>
+
+## Data
+
+The training set is the 104 whole-body structures from the TotalSegmentator released datasets. Users can find more details on the datasets at https://github.com/wasserth/TotalSegmentator. All rights and licenses are reserved to the original authors.
+
+- Target: 104 structures
+- Modality: CT
+- Source: TotalSegmentator
+- Challenge: Large volumes of structures in CT images
+
+### Preprocessing
+
+To use the bundle, users need to download the data and merge all annotated labels into one NIFTI file. Each file contains 0-104 values, each value represents one anatomy class. A sample set is provided with this [link](https://drive.google.com/file/d/1DtDmERVMjks1HooUhggOKAuDm0YIEunG/view?usp=share_link).
+
+## Training Configuration
+
+The segmentation of 104 tissues is formulated as voxel-wise multi-label segmentation. The model is optimized with the gradient descent method minimizing Dice + cross-entropy loss between the predicted mask and ground truth segmentation.
+
+The training was performed with the following:
+
+- GPU: 32 GB of GPU memory
+- Actual Model Input: 96 x 96 x 96
+- AMP: True
+- Optimizer: AdamW
+- Learning Rate: 1e-4
+- Loss: DiceCELoss
+
+### Input
+
+One channel
+- CT image
+
+### Output
+
+105 channels
+- Label 0: Background (everything else)
+- label 1-105: Foreground classes (104)
+
+### High-Resolution and Low-Resolution Models
+
+We retrained two versions of the totalSegmentator models, following the original paper and implementation.
+To meet multiple demands according to computation resources and performance, we provide a 1.5 mm model and a 3.0 mm model, both models are trained with 104 foreground output channels.
+
+In this bundle, we configured a parameter called `highres`, users can set it to `true` when using 1.5 mm model, and set it to `false` to use the 3.0 mm model. The high-resolution model is named `model.pt` by default, the low-resolution model is named `model_lowres.pt`.
+
+In MONAI Label use case, users can set the parameter in 3D Slicer plugin to control which model to infer and train.
+
+- Pretrained Checkpoints
+  - 1.5 mm model: [Download link](https://drive.google.com/file/d/1PHpFWboimEXmMSe2vBra6T8SaCMC2SHT/view?usp=share_link)
+  - 3.0 mm model: [Download link](https://drive.google.com/file/d/1c3osYscnr6710ObqZZS8GkZJQlWlc7rt/view?usp=share_link)
+
+### Resource Requirements and Latency Benchmarks
+
+Latencies and memory performance of using the bundle with MONAI Label:
+
+Tested Image Dimension: **(512, 512, 397)**, the slice thickness is **1.5mm** in this case. After resample to **1.5** isotropic resolution, the dimension is   **(287, 287, 397)**
+
+## 1.5 mm (highres) model (Single Model with 104 foreground classes)
+
+Benchmarking on GPU: Memory: **28.73G**
+
+- `++ Latencies => Total: 6.0277; Pre: 1.6228; Inferer: 4.1153; Invert: 0.0000; Post: 0.0897; Write: 0.1995`
+
+Benchmarking on CPU: Memory: **26G**
+
+- `++ Latencies => Total: 38.3108; Pre: 1.6643; Inferer: 30.3018; Invert: 0.0000; Post: 6.1656; Write: 0.1786`
+
+## 3.0 mm (lowres) model (single model with 104 foreground classes)
+
+GPU: Memory: **5.89G**
+
+ - `++ Latencies => Total: 1.9993; Pre: 1.2363; Inferer: 0.5207; Invert: 0.0000; Post: 0.0358; Write: 0.2060`
+
+CPU: Memory: **2.3G**
+
+ - `++ Latencies => Total: 6.6138; Pre: 1.3192; Inferer: 3.6746; Invert: 0.0000; Post: 1.4431; Write: 0.1760`
+
+## Performance
+
+- 1.5 mm Model Training
+
+  - Training Accuracy
+
+![](./imgs/totalsegmentator_train_accuracy.png) <br>
+
+  - Validation Dice
+
+![](./imgs/totalsegmentator_15mm_validation.png) <br>
+
+## 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 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
+```
+
+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 evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json']" --logging_file configs/logging.conf
+```
+
+#### Override the `train` config and `evaluate` config to execute multi-GPU evaluation
+
+```
+torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json','configs/multi_gpu_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
+```
+#### Execute inference with Data Samples
+
+```
+python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf --datalist "['sampledata/imagesTr/s0037.nii.gz','sampledata/imagesTr/s0038.nii.gz']"
+```
+
+
+# References
+
+[1] Tang, Y., Gao, R., Lee, H.H., Han, S., Chen, Y., Gao, D., Nath, V., Bermudez, C., Savona, M.R., Abramson, R.G. and Bao, S., 2021. High-resolution 3D abdominal segmentation with random patch network fusion. Medical image analysis, 69, p.101894.
+
+[2] Wasserthal, J., Meyer, M., Breit, H.C., Cyriac, J., Yang, S. and Segeroth, M., 2022. TotalSegmentator: robust segmentation of 104 anatomical structures in CT images. arXiv preprint arXiv:2208.05868.
+
+[3] Myronenko, A., Siddiquee, M.M.R., Yang, D., He, Y. and Xu, D., 2022. Automated head and neck tumor segmentation from 3D PET/CT. arXiv preprint arXiv:2209.10809.
+
+
+
+# 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.

configs/evaluate.json

@@ -0,0 +1,78 @@
+{
+    "validate#postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "Activationsd",
+                "keys": "pred",
+                "softmax": true
+            },
+            {
+                "_target_": "AsDiscreted",
+                "keys": [
+                    "pred",
+                    "label"
+                ],
+                "argmax": [
+                    true,
+                    false
+                ],
+                "to_onehot": 105
+            },
+            {
+                "_target_": "Invertd",
+                "keys": [
+                    "pred",
+                    "label"
+                ],
+                "transform": "@validate#preprocessing",
+                "orig_keys": "image",
+                "meta_key_postfix": "meta_dict",
+                "nearest_interp": [
+                    true,
+                    true
+                ],
+                "to_tensor": true
+            },
+            {
+                "_target_": "SaveImaged",
+                "_disabled_": true,
+                "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

@@ -0,0 +1,159 @@
+{
+    "displayable_configs": {
+        "highres": true,
+        "sw_overlap": 0.25,
+        "sw_batch_size": 1
+    },
+    "imports": [
+        "$import glob",
+        "$import os"
+    ],
+    "bundle_root": ".",
+    "output_dir": "$@bundle_root + '/eval'",
+    "dataset_dir": "sampledata",
+    "datalist": "$list(sorted(glob.glob(@dataset_dir + '/imagesTs/*.nii.gz')))",
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "pixdim": "$[1.5, 1.5, 1.5] if @displayable_configs#highres else [3.0, 3.0, 3.0]",
+    "modelname": "$'model.pt' if @displayable_configs#highres else 'model_lowres.pt'",
+    "network_def": {
+        "_target_": "SegResNet",
+        "spatial_dims": 3,
+        "in_channels": 1,
+        "out_channels": 105,
+        "init_filters": 32,
+        "blocks_down": [
+            1,
+            2,
+            2,
+            4
+        ],
+        "blocks_up": [
+            1,
+            1,
+            1
+        ],
+        "dropout_prob": 0.2
+    },
+    "network": "$@network_def.to(@device)",
+    "preprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "LoadImaged",
+                "keys": "image"
+            },
+            {
+                "_target_": "EnsureTyped",
+                "keys": "image"
+            },
+            {
+                "_target_": "EnsureChannelFirstd",
+                "keys": "image"
+            },
+            {
+                "_target_": "Orientationd",
+                "keys": "image",
+                "axcodes": "RAS"
+            },
+            {
+                "_target_": "Spacingd",
+                "keys": "image",
+                "pixdim": "@pixdim",
+                "mode": "bilinear"
+            },
+            {
+                "_target_": "NormalizeIntensityd",
+                "keys": "image",
+                "nonzero": true
+            },
+            {
+                "_target_": "ScaleIntensityd",
+                "keys": "image",
+                "minv": -1.0,
+                "maxv": 1.0
+            }
+        ]
+    },
+    "dataset": {
+        "_target_": "Dataset",
+        "data": "$[{'image': i} for i in @datalist]",
+        "transform": "@preprocessing"
+    },
+    "dataloader": {
+        "_target_": "DataLoader",
+        "dataset": "@dataset",
+        "batch_size": 1,
+        "shuffle": false,
+        "num_workers": 1
+    },
+    "inferer": {
+        "_target_": "SlidingWindowInferer",
+        "roi_size": [
+            96,
+            96,
+            96
+        ],
+        "sw_batch_size": "@displayable_configs#sw_batch_size",
+        "overlap": "@displayable_configs#sw_overlap",
+        "padding_mode": "replicate",
+        "mode": "gaussian",
+        "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
+    },
+    "postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "Activationsd",
+                "keys": "pred",
+                "softmax": true
+            },
+            {
+                "_target_": "AsDiscreted",
+                "keys": "pred",
+                "argmax": true
+            },
+            {
+                "_target_": "Invertd",
+                "keys": "pred",
+                "transform": "@preprocessing",
+                "orig_keys": "image",
+                "meta_key_postfix": "meta_dict",
+                "nearest_interp": true,
+                "to_tensor": true
+            },
+            {
+                "_target_": "SaveImaged",
+                "keys": "pred",
+                "meta_keys": "pred_meta_dict",
+                "output_dir": "@output_dir"
+            }
+        ]
+    },
+    "handlers": [
+        {
+            "_target_": "CheckpointLoader",
+            "load_path": "$@bundle_root + '/models/' + @modelname",
+            "load_dict": {
+                "model": "@network"
+            }
+        },
+        {
+            "_target_": "StatsHandler",
+            "iteration_log": false
+        }
+    ],
+    "evaluator": {
+        "_target_": "SupervisedEvaluator",
+        "device": "@device",
+        "val_data_loader": "@dataloader",
+        "network": "@network",
+        "inferer": "@inferer",
+        "postprocessing": "@postprocessing",
+        "val_handlers": "@handlers",
+        "amp": true
+    },
+    "evaluating": [
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "[email protected]()"
+    ]
+}

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 +1,183 @@
+{
+    "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": "1.1.0",
+    "pytorch_version": "1.13.0",
+    "numpy_version": "1.21.2",
+    "optional_packages_version": {
+        "nibabel": "4.0.1",
+        "pytorch-ignite": "0.4.9"
+    },
+    "name": "Whole body CT segmentation",
+    "task": "TotalSegmentator Segmentation",
+    "description": "A pre-trained SegResNet model for volumetric (3D) segmentation of the 104 whole body segments",
+    "authors": "MONAI team",
+    "copyright": "Copyright (c) MONAI Consortium",
+    "data_source": "TotalSegmentator",
+    "data_type": "nibabel",
+    "image_classes": "104 foreground channels, 0 channel for the background, intensity scaled to [0, 1]",
+    "label_classes": "0 is the background, others are whole body segments",
+    "pred_classes": "0 is the background, 104 other chanels are whole body segments",
+    "eval_metrics": {
+        "mean_dice": 0.5
+    },
+    "intended_use": "This is an example, not to be used for diagnostic purposes",
+    "references": [
+        "Wasserthal, J., Meyer, M., Breit, H.C., Cyriac, J., Yang, S. and Segeroth, M., 2022. TotalSegmentator: robust segmentation of 104 anatomical structures in CT images. arXiv preprint arXiv:2208.05868.",
+        "Myronenko, A., Siddiquee, M.M.R., Yang, D., He, Y. and Xu, D., 2022. Automated head and neck tumor segmentation from 3D PET/CT. arXiv preprint arXiv:2209.10809.",
+        "Tang, Y., Gao, R., Lee, H.H., Han, S., Chen, Y., Gao, D., Nath, V., Bermudez, C., Savona, M.R., Abramson, R.G. and Bao, S., 2021. High-resolution 3D abdominal segmentation with random patch network fusion. Medical image analysis, 69, p.101894."
+    ],
+    "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": 105,
+                "spatial_shape": [
+                    96,
+                    96,
+                    96
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    104
+                ],
+                "is_patch_data": true,
+                "channel_def": {
+                    "0": "background",
+                    "1": "spleen",
+                    "2": "kidney_right",
+                    "3": "kidney_left",
+                    "4": "gallbladder",
+                    "5": "liver",
+                    "6": "stomach",
+                    "7": "aorta",
+                    "8": "inferior_vena_cava",
+                    "9": "portal_vein_and_splenic_vein",
+                    "10": "pancreas",
+                    "11": "adrenal_gland_right",
+                    "12": "adrenal_gland_left",
+                    "13": "lung_upper_lobe_left",
+                    "14": "lung_lower_lobe_left",
+                    "15": "lung_upper_lobe_right",
+                    "16": "lung_middle_lobe_right",
+                    "17": "lung_lower_lobe_right",
+                    "18": "vertebrae_L5",
+                    "19": "vertebrae_L4",
+                    "20": "vertebrae_L3",
+                    "21": "vertebrae_L2",
+                    "22": "vertebrae_L1",
+                    "23": "vertebrae_T12",
+                    "24": "vertebrae_T11",
+                    "25": "vertebrae_T10",
+                    "26": "vertebrae_T9",
+                    "27": "vertebrae_T8",
+                    "28": "vertebrae_T7",
+                    "29": "vertebrae_T6",
+                    "30": "vertebrae_T5",
+                    "31": "vertebrae_T4",
+                    "32": "vertebrae_T3",
+                    "33": "vertebrae_T2",
+                    "34": "vertebrae_T1",
+                    "35": "vertebrae_C7",
+                    "36": "vertebrae_C6",
+                    "37": "vertebrae_C5",
+                    "38": "vertebrae_C4",
+                    "39": "vertebrae_C3",
+                    "40": "vertebrae_C2",
+                    "41": "vertebrae_C1",
+                    "42": "esophagus",
+                    "43": "trachea",
+                    "44": "heart_myocardium",
+                    "45": "heart_atrium_left",
+                    "46": "heart_ventricle_left",
+                    "47": "heart_atrium_right",
+                    "48": "heart_ventricle_right",
+                    "49": "pulmonary_artery",
+                    "50": "brain",
+                    "51": "iliac_artery_left",
+                    "52": "iliac_artery_right",
+                    "53": "iliac_vena_left",
+                    "54": "iliac_vena_right",
+                    "55": "small_bowel",
+                    "56": "duodenum",
+                    "57": "colon",
+                    "58": "rib_left_1",
+                    "59": "rib_left_2",
+                    "60": "rib_left_3",
+                    "61": "rib_left_4",
+                    "62": "rib_left_5",
+                    "63": "rib_left_6",
+                    "64": "rib_left_7",
+                    "65": "rib_left_8",
+                    "66": "rib_left_9",
+                    "67": "rib_left_10",
+                    "68": "rib_left_11",
+                    "69": "rib_left_12",
+                    "70": "rib_right_1",
+                    "71": "rib_right_2",
+                    "72": "rib_right_3",
+                    "73": "rib_right_4",
+                    "74": "rib_right_5",
+                    "75": "rib_right_6",
+                    "76": "rib_right_7",
+                    "77": "rib_right_8",
+                    "78": "rib_right_9",
+                    "79": "rib_right_10",
+                    "80": "rib_right_11",
+                    "81": "rib_right_12",
+                    "82": "humerus_left",
+                    "83": "humerus_right",
+                    "84": "scapula_left",
+                    "85": "scapula_right",
+                    "86": "clavicula_left",
+                    "87": "clavicula_right",
+                    "88": "femur_left",
+                    "89": "femur_right",
+                    "90": "hip_left",
+                    "91": "hip_right",
+                    "92": "sacrum",
+                    "93": "face",
+                    "94": "gluteus_maximus_left",
+                    "95": "gluteus_maximus_right",
+                    "96": "gluteus_medius_left",
+                    "97": "gluteus_medius_right",
+                    "98": "gluteus_minimus_left",
+                    "99": "gluteus_minimus_right",
+                    "100": "autochthon_left",
+                    "101": "autochthon_right",
+                    "102": "iliopsoas_left",
+                    "103": "iliopsoas_right",
+                    "104": "urinary_bladder"
+                }
+            }
+        }
+    }
+}

configs/multi_gpu_evaluate.json

@@ -0,0 +1,28 @@
+{
+    "device": "$torch.device(f'cuda:{dist.get_rank()}')",
+    "network": {
+        "_target_": "torch.nn.parallel.DistributedDataParallel",
+        "module": "$@network_def.to(@device)",
+        "device_ids": [
+            "@device"
+        ]
+    },
+    "validate#sampler": {
+        "_target_": "DistributedSampler",
+        "dataset": "@validate#dataset",
+        "even_divisible": false,
+        "shuffle": false
+    },
+    "validate#dataloader#sampler": "@validate#sampler",
+    "validate#handlers#1#_disabled_": "$dist.get_rank() > 0",
+    "evaluating": [
+        "$import torch.distributed as dist",
+        "$dist.init_process_group(backend='nccl')",
+        "$torch.cuda.set_device(@device)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$import logging",
+        "$@validate#evaluator.logger.setLevel(logging.WARNING if dist.get_rank() > 0 else logging.INFO)",
+        "$@validate#evaluator.run()",
+        "$dist.destroy_process_group()"
+    ]
+}

configs/multi_gpu_train.json

@@ -0,0 +1,39 @@
+{
+    "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)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$import logging",
+        "$@train#trainer.logger.setLevel(logging.WARNING if dist.get_rank() > 0 else logging.INFO)",
+        "$@validate#evaluator.logger.setLevel(logging.WARNING if dist.get_rank() > 0 else logging.INFO)",
+        "$@train#trainer.run()",
+        "$dist.destroy_process_group()"
+    ]
+}

configs/train.json

@@ -0,0 +1,422 @@
+{
+    "displayable_configs": {
+        "highres": true,
+        "init_LR": 0.0001
+    },
+    "imports": [
+        "$import glob",
+        "$import os",
+        "$import ignite"
+    ],
+    "bundle_root": ".",
+    "ckpt_dir": "$@bundle_root + '/models'",
+    "output_dir": "$@bundle_root + '/eval'",
+    "dataset_dir": "sampledata",
+    "images": "$list(sorted(glob.glob(@dataset_dir + '/imagesTr/*.nii.gz')))",
+    "labels": "$list(sorted(glob.glob(@dataset_dir + '/labelsTr/*.nii.gz')))",
+    "highres": true,
+    "val_interval": 20,
+    "init_LR": 0.0001,
+    "batch_size": 4,
+    "pixdim": "$[1.5, 1.5, 1.5] if @displayable_configs#highres else [3.0, 3.0, 3.0]",
+    "modelname": "$'model.pt' if @displayable_configs#highres else 'model_lowres.pt'",
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "network_def": {
+        "_target_": "SegResNet",
+        "spatial_dims": 3,
+        "in_channels": 1,
+        "out_channels": 105,
+        "init_filters": 32,
+        "blocks_down": [
+            1,
+            2,
+            2,
+            4
+        ],
+        "blocks_up": [
+            1,
+            1,
+            1
+        ],
+        "dropout_prob": 0.2
+    },
+    "network": "$@network_def.to(@device)",
+    "loss": {
+        "_target_": "DiceCELoss",
+        "to_onehot_y": true,
+        "softmax": true
+    },
+    "optimizer": {
+        "_target_": "torch.optim.AdamW",
+        "params": "[email protected]()",
+        "lr": "@displayable_configs#init_LR",
+        "weight_decay": 1e-05
+    },
+    "train": {
+        "deterministic_transforms": [
+            {
+                "_target_": "LoadImaged",
+                "keys": [
+                    "image",
+                    "label"
+                ]
+            },
+            {
+                "_target_": "EnsureChannelFirstd",
+                "keys": [
+                    "image",
+                    "label"
+                ]
+            },
+            {
+                "_target_": "EnsureTyped",
+                "keys": [
+                    "image",
+                    "label"
+                ]
+            },
+            {
+                "_target_": "Orientationd",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "axcodes": "RAS"
+            },
+            {
+                "_target_": "Spacingd",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "pixdim": "@pixdim",
+                "mode": [
+                    "bilinear",
+                    "nearest"
+                ]
+            },
+            {
+                "_target_": "NormalizeIntensityd",
+                "keys": "image",
+                "nonzero": true
+            },
+            {
+                "_target_": "CropForegroundd",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "source_key": "image",
+                "margin": 10,
+                "k_divisible": [
+                    96,
+                    96,
+                    96
+                ]
+            },
+            {
+                "_target_": "GaussianSmoothd",
+                "keys": [
+                    "image"
+                ],
+                "sigma": 0.4
+            },
+            {
+                "_target_": "ScaleIntensityd",
+                "keys": "image",
+                "minv": -1.0,
+                "maxv": 1.0
+            },
+            {
+                "_target_": "EnsureTyped",
+                "keys": [
+                    "image",
+                    "label"
+                ]
+            }
+        ],
+        "random_transforms": [
+            {
+                "_target_": "RandSpatialCropd",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "roi_size": [
+                    96,
+                    96,
+                    96
+                ],
+                "random_size": false
+            }
+        ],
+        "preprocessing": {
+            "_target_": "Compose",
+            "transforms": "$@train#deterministic_transforms + @train#random_transforms"
+        },
+        "dataset": {
+            "_target_": "CacheDataset",
+            "data": "$[{'image': i, 'label': l} for i, l in zip(@images[:-10], @labels[:-10])]",
+            "transform": "@train#preprocessing",
+            "cache_rate": 0.4,
+            "num_workers": 4
+        },
+        "dataloader": {
+            "_target_": "DataLoader",
+            "dataset": "@train#dataset",
+            "batch_size": "@batch_size",
+            "shuffle": true,
+            "num_workers": 4
+        },
+        "inferer": {
+            "_target_": "SimpleInferer"
+        },
+        "postprocessing": {
+            "_target_": "Compose",
+            "transforms": [
+                {
+                    "_target_": "Activationsd",
+                    "keys": "pred",
+                    "softmax": true
+                },
+                {
+                    "_target_": "AsDiscreted",
+                    "keys": [
+                        "pred",
+                        "label"
+                    ],
+                    "argmax": [
+                        true,
+                        false
+                    ],
+                    "to_onehot": 105
+                }
+            ]
+        },
+        "handlers": [
+            {
+                "_target_": "ValidationHandler",
+                "validator": "@validate#evaluator",
+                "epoch_level": true,
+                "interval": "@val_interval"
+            },
+            {
+                "_target_": "StatsHandler",
+                "tag_name": "train_loss",
+                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
+            },
+            {
+                "_target_": "TensorBoardStatsHandler",
+                "log_dir": "@output_dir",
+                "tag_name": "train_loss",
+                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
+            }
+        ],
+        "key_metric": {
+            "train_accuracy": {
+                "_target_": "ignite.metrics.Accuracy",
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
+            }
+        },
+        "trainer": {
+            "_target_": "SupervisedTrainer",
+            "max_epochs": 4000,
+            "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": [
+                {
+                    "_target_": "LoadImaged",
+                    "keys": [
+                        "image",
+                        "label"
+                    ]
+                },
+                {
+                    "_target_": "EnsureChannelFirstd",
+                    "keys": [
+                        "image",
+                        "label"
+                    ]
+                },
+                {
+                    "_target_": "EnsureTyped",
+                    "keys": [
+                        "image",
+                        "label"
+                    ]
+                },
+                {
+                    "_target_": "Orientationd",
+                    "keys": [
+                        "image",
+                        "label"
+                    ],
+                    "axcodes": "RAS"
+                },
+                {
+                    "_target_": "Spacingd",
+                    "keys": [
+                        "image",
+                        "label"
+                    ],
+                    "pixdim": "@pixdim",
+                    "mode": [
+                        "bilinear",
+                        "nearest"
+                    ]
+                },
+                {
+                    "_target_": "NormalizeIntensityd",
+                    "keys": "image",
+                    "nonzero": true
+                },
+                {
+                    "_target_": "CropForegroundd",
+                    "keys": [
+                        "image",
+                        "label"
+                    ],
+                    "source_key": "image",
+                    "margin": 10,
+                    "k_divisible": [
+                        96,
+                        96,
+                        96
+                    ]
+                },
+                {
+                    "_target_": "GaussianSmoothd",
+                    "keys": [
+                        "image"
+                    ],
+                    "sigma": 0.4
+                },
+                {
+                    "_target_": "ScaleIntensityd",
+                    "keys": "image",
+                    "minv": -1.0,
+                    "maxv": 1.0
+                },
+                {
+                    "_target_": "CenterSpatialCropd",
+                    "keys": [
+                        "image",
+                        "label"
+                    ],
+                    "roi_size": [
+                        160,
+                        160,
+                        160
+                    ]
+                }
+            ]
+        },
+        "postprocessing": {
+            "_target_": "Compose",
+            "transforms": [
+                {
+                    "_target_": "Activationsd",
+                    "keys": "pred",
+                    "softmax": true
+                },
+                {
+                    "_target_": "AsDiscreted",
+                    "keys": [
+                        "pred",
+                        "label"
+                    ],
+                    "argmax": [
+                        true,
+                        false
+                    ],
+                    "to_onehot": 105
+                }
+            ]
+        },
+        "dataset": {
+            "_target_": "Dataset",
+            "data": "$[{'image': i, 'label': l} for i, l in zip(@images[-10:], @labels[-10:])]",
+            "transform": "@validate#preprocessing"
+        },
+        "dataloader": {
+            "_target_": "DataLoader",
+            "dataset": "@validate#dataset",
+            "batch_size": 1,
+            "shuffle": false,
+            "num_workers": 4
+        },
+        "inferer": {
+            "_target_": "SlidingWindowInferer",
+            "roi_size": [
+                96,
+                96,
+                96
+            ],
+            "sw_batch_size": 1,
+            "overlap": 0.25
+        },
+        "handlers": [
+            {
+                "_target_": "StatsHandler",
+                "iteration_log": false
+            },
+            {
+                "_target_": "TensorBoardStatsHandler",
+                "log_dir": "@output_dir",
+                "iteration_log": false
+            },
+            {
+                "_target_": "CheckpointSaver",
+                "save_dir": "@ckpt_dir",
+                "save_dict": {
+                    "model": "@network"
+                },
+                "save_key_metric": true,
+                "key_metric_filename": "@modelname"
+            }
+        ],
+        "key_metric": {
+            "val_mean_dice": {
+                "_target_": "MeanDice",
+                "include_background": false,
+                "output_transform": "$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 +1,172 @@
+# Model Overview
+
+Body CT segmentation models are evolving. Starting from abdominal multi-organ segmentation model [1]. Now the community is developing hundreds of target anatomies. In this bundle, we provide re-trained models for (3D) segmentation of 104 whole-body segments.
+
+This model is trained using the SegResNet [3] network. The model is trained using TotalSegmentator datasets [2].
+
+![structures](https://github.com/wasserth/TotalSegmentator/blob/master/resources/imgs/overview_classes.png)
+
+Figure source from the TotalSegmentator [2].
+
+## MONAI Label Showcase
+
+- We highlight the use of this bundle to use and visualize in MONAI Label + 3D Slicer integration.
+
+![](./imgs/totalsegmentator_monailabel.png) <br>
+
+## Data
+
+The training set is the 104 whole-body structures from the TotalSegmentator released datasets. Users can find more details on the datasets at https://github.com/wasserth/TotalSegmentator. All rights and licenses are reserved to the original authors.
+
+- Target: 104 structures
+- Modality: CT
+- Source: TotalSegmentator
+- Challenge: Large volumes of structures in CT images
+
+### Preprocessing
+
+To use the bundle, users need to download the data and merge all annotated labels into one NIFTI file. Each file contains 0-104 values, each value represents one anatomy class. A sample set is provided with this [link](https://drive.google.com/file/d/1DtDmERVMjks1HooUhggOKAuDm0YIEunG/view?usp=share_link).
+
+## Training Configuration
+
+The segmentation of 104 tissues is formulated as voxel-wise multi-label segmentation. The model is optimized with the gradient descent method minimizing Dice + cross-entropy loss between the predicted mask and ground truth segmentation.
+
+The training was performed with the following:
+
+- GPU: 32 GB of GPU memory
+- Actual Model Input: 96 x 96 x 96
+- AMP: True
+- Optimizer: AdamW
+- Learning Rate: 1e-4
+- Loss: DiceCELoss
+
+### Input
+
+One channel
+- CT image
+
+### Output
+
+105 channels
+- Label 0: Background (everything else)
+- label 1-105: Foreground classes (104)
+
+### High-Resolution and Low-Resolution Models
+
+We retrained two versions of the totalSegmentator models, following the original paper and implementation.
+To meet multiple demands according to computation resources and performance, we provide a 1.5 mm model and a 3.0 mm model, both models are trained with 104 foreground output channels.
+
+In this bundle, we configured a parameter called `highres`, users can set it to `true` when using 1.5 mm model, and set it to `false` to use the 3.0 mm model. The high-resolution model is named `model.pt` by default, the low-resolution model is named `model_lowres.pt`.
+
+In MONAI Label use case, users can set the parameter in 3D Slicer plugin to control which model to infer and train.
+
+- Pretrained Checkpoints
+  - 1.5 mm model: [Download link](https://drive.google.com/file/d/1PHpFWboimEXmMSe2vBra6T8SaCMC2SHT/view?usp=share_link)
+  - 3.0 mm model: [Download link](https://drive.google.com/file/d/1c3osYscnr6710ObqZZS8GkZJQlWlc7rt/view?usp=share_link)
+
+### Resource Requirements and Latency Benchmarks
+
+Latencies and memory performance of using the bundle with MONAI Label:
+
+Tested Image Dimension: **(512, 512, 397)**, the slice thickness is **1.5mm** in this case. After resample to **1.5** isotropic resolution, the dimension is   **(287, 287, 397)**
+
+## 1.5 mm (highres) model (Single Model with 104 foreground classes)
+
+Benchmarking on GPU: Memory: **28.73G**
+
+- `++ Latencies => Total: 6.0277; Pre: 1.6228; Inferer: 4.1153; Invert: 0.0000; Post: 0.0897; Write: 0.1995`
+
+Benchmarking on CPU: Memory: **26G**
+
+- `++ Latencies => Total: 38.3108; Pre: 1.6643; Inferer: 30.3018; Invert: 0.0000; Post: 6.1656; Write: 0.1786`
+
+## 3.0 mm (lowres) model (single model with 104 foreground classes)
+
+GPU: Memory: **5.89G**
+
+ - `++ Latencies => Total: 1.9993; Pre: 1.2363; Inferer: 0.5207; Invert: 0.0000; Post: 0.0358; Write: 0.2060`
+
+CPU: Memory: **2.3G**
+
+ - `++ Latencies => Total: 6.6138; Pre: 1.3192; Inferer: 3.6746; Invert: 0.0000; Post: 1.4431; Write: 0.1760`
+
+## Performance
+
+- 1.5 mm Model Training
+
+  - Training Accuracy
+
+![](./imgs/totalsegmentator_train_accuracy.png) <br>
+
+  - Validation Dice
+
+![](./imgs/totalsegmentator_15mm_validation.png) <br>
+
+## 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 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
+```
+
+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 evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json']" --logging_file configs/logging.conf
+```
+
+#### Override the `train` config and `evaluate` config to execute multi-GPU evaluation
+
+```
+torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json','configs/multi_gpu_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
+```
+#### Execute inference with Data Samples
+
+```
+python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf --datalist "['sampledata/imagesTr/s0037.nii.gz','sampledata/imagesTr/s0038.nii.gz']"
+```
+
+
+# References
+
+[1] Tang, Y., Gao, R., Lee, H.H., Han, S., Chen, Y., Gao, D., Nath, V., Bermudez, C., Savona, M.R., Abramson, R.G. and Bao, S., 2021. High-resolution 3D abdominal segmentation with random patch network fusion. Medical image analysis, 69, p.101894.
+
+[2] Wasserthal, J., Meyer, M., Breit, H.C., Cyriac, J., Yang, S. and Segeroth, M., 2022. TotalSegmentator: robust segmentation of 104 anatomical structures in CT images. arXiv preprint arXiv:2208.05868.
+
+[3] Myronenko, A., Siddiquee, M.M.R., Yang, D., He, Y. and Xu, D., 2022. Automated head and neck tumor segmentation from 3D PET/CT. arXiv preprint arXiv:2209.10809.
+
+
+
+# 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. TotalSegmentator
+   https://zenodo.org/record/6802614#.Y9iTydLMJ6I

models/model.pt

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