diff --git a/models/swin_unetr_btcv_segmentation/configs/metadata.json b/models/swin_unetr_btcv_segmentation/configs/metadata.json
index 58a14933..1de4e9be 100644
--- a/models/swin_unetr_btcv_segmentation/configs/metadata.json
+++ b/models/swin_unetr_btcv_segmentation/configs/metadata.json
@@ -1,7 +1,8 @@
{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
- "version": "0.4.1",
+ "version": "0.4.2",
"changelog": {
+ "0.4.2": "fix train params of use_checkpoint",
"0.4.1": "update params to supprot torch.jit.trace torchscript conversion",
"0.4.0": "add name tag",
"0.3.9": "use ITKreader to avoid mass logs at image loading",
diff --git a/models/swin_unetr_btcv_segmentation/configs/train.json b/models/swin_unetr_btcv_segmentation/configs/train.json
index 5135eda1..e5694677 100644
--- a/models/swin_unetr_btcv_segmentation/configs/train.json
+++ b/models/swin_unetr_btcv_segmentation/configs/train.json
@@ -19,7 +19,7 @@
"in_channels": 1,
"out_channels": 14,
"feature_size": 48,
- "use_checkpoint": false
+ "use_checkpoint": true
},
"network": "$@network_def.to(@device)",
"loss": {
diff --git a/models/wholeBody_ct_segmentation/LICENSE b/models/wholeBody_ct_segmentation/LICENSE
new file mode 100644
index 00000000..261eeb9e
--- /dev/null
+++ b/models/wholeBody_ct_segmentation/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
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diff --git a/models/wholeBody_ct_segmentation/configs/evaluate.json b/models/wholeBody_ct_segmentation/configs/evaluate.json
new file mode 100644
index 00000000..2860fc2a
--- /dev/null
+++ b/models/wholeBody_ct_segmentation/configs/evaluate.json
@@ -0,0 +1,78 @@
+{
+ "validate#postprocessing": {
+ "_target_": "Compose",
+ "transforms": [
+ {
+ "_target_": "Activationsd",
+ "keys": "pred",
+ "softmax": true
+ },
+ {
+ "_target_": "AsDiscreted",
+ "keys": [
+ "pred",
+ "label"
+ ],
+ "argmax": [
+ true,
+ false
+ ],
+ "to_onehot": 105
+ },
+ {
+ "_target_": "Invertd",
+ "keys": [
+ "pred",
+ "label"
+ ],
+ "transform": "@validate#preprocessing",
+ "orig_keys": "image",
+ "meta_key_postfix": "meta_dict",
+ "nearest_interp": [
+ true,
+ true
+ ],
+ "to_tensor": true
+ },
+ {
+ "_target_": "SaveImaged",
+ "_disabled_": true,
+ "keys": "pred",
+ "meta_keys": "pred_meta_dict",
+ "output_dir": "@output_dir",
+ "resample": false,
+ "squeeze_end_dims": true
+ }
+ ]
+ },
+ "validate#handlers": [
+ {
+ "_target_": "CheckpointLoader",
+ "load_path": "$@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)",
+ "$@validate#evaluator.run()"
+ ]
+}
diff --git a/models/wholeBody_ct_segmentation/configs/inference.json b/models/wholeBody_ct_segmentation/configs/inference.json
new file mode 100644
index 00000000..b17f4ce8
--- /dev/null
+++ b/models/wholeBody_ct_segmentation/configs/inference.json
@@ -0,0 +1,159 @@
+{
+ "displayable_configs": {
+ "highres": true,
+ "sw_overlap": 0.25,
+ "sw_batch_size": 1
+ },
+ "imports": [
+ "$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": [
+ "$setattr(torch.backends.cudnn, 'benchmark', True)",
+ "$@evaluator.run()"
+ ]
+}
diff --git a/models/wholeBody_ct_segmentation/configs/logging.conf b/models/wholeBody_ct_segmentation/configs/logging.conf
new file mode 100644
index 00000000..91c1a21c
--- /dev/null
+++ b/models/wholeBody_ct_segmentation/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
diff --git a/models/wholeBody_ct_segmentation/configs/metadata.json b/models/wholeBody_ct_segmentation/configs/metadata.json
new file mode 100644
index 00000000..f5513a70
--- /dev/null
+++ b/models/wholeBody_ct_segmentation/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"
+ }
+ }
+ }
+ }
+}
diff --git a/models/wholeBody_ct_segmentation/configs/multi_gpu_evaluate.json b/models/wholeBody_ct_segmentation/configs/multi_gpu_evaluate.json
new file mode 100644
index 00000000..f6b6c6dc
--- /dev/null
+++ b/models/wholeBody_ct_segmentation/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()",
+ "$dist.destroy_process_group()"
+ ]
+}
diff --git a/models/wholeBody_ct_segmentation/configs/multi_gpu_train.json b/models/wholeBody_ct_segmentation/configs/multi_gpu_train.json
new file mode 100644
index 00000000..4161e527
--- /dev/null
+++ b/models/wholeBody_ct_segmentation/configs/multi_gpu_train.json
@@ -0,0 +1,39 @@
+{
+ "device": "$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()",
+ "$dist.destroy_process_group()"
+ ]
+}
diff --git a/models/wholeBody_ct_segmentation/configs/train.json b/models/wholeBody_ct_segmentation/configs/train.json
new file mode 100644
index 00000000..9e79afd6
--- /dev/null
+++ b/models/wholeBody_ct_segmentation/configs/train.json
@@ -0,0 +1,422 @@
+{
+ "displayable_configs": {
+ "highres": true,
+ "init_LR": 0.0001
+ },
+ "imports": [
+ "$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": "$@network.parameters()",
+ "lr": "@displayable_configs#init_LR",
+ "weight_decay": 1e-05
+ },
+ "train": {
+ "deterministic_transforms": [
+ {
+ "_target_": "LoadImaged",
+ "keys": [
+ "image",
+ "label"
+ ]
+ },
+ {
+ "_target_": "EnsureChannelFirstd",
+ "keys": [
+ "image",
+ "label"
+ ]
+ },
+ {
+ "_target_": "EnsureTyped",
+ "keys": [
+ "image",
+ "label"
+ ]
+ },
+ {
+ "_target_": "Orientationd",
+ "keys": [
+ "image",
+ "label"
+ ],
+ "axcodes": "RAS"
+ },
+ {
+ "_target_": "Spacingd",
+ "keys": [
+ "image",
+ "label"
+ ],
+ "pixdim": "@pixdim",
+ "mode": [
+ "bilinear",
+ "nearest"
+ ]
+ },
+ {
+ "_target_": "NormalizeIntensityd",
+ "keys": "image",
+ "nonzero": true
+ },
+ {
+ "_target_": "CropForegroundd",
+ "keys": [
+ "image",
+ "label"
+ ],
+ "source_key": "image",
+ "margin": 10,
+ "k_divisible": [
+ 96,
+ 96,
+ 96
+ ]
+ },
+ {
+ "_target_": "GaussianSmoothd",
+ "keys": [
+ "image"
+ ],
+ "sigma": 0.4
+ },
+ {
+ "_target_": "ScaleIntensityd",
+ "keys": "image",
+ "minv": -1.0,
+ "maxv": 1.0
+ },
+ {
+ "_target_": "EnsureTyped",
+ "keys": [
+ "image",
+ "label"
+ ]
+ }
+ ],
+ "random_transforms": [
+ {
+ "_target_": "RandSpatialCropd",
+ "keys": [
+ "image",
+ "label"
+ ],
+ "roi_size": [
+ 96,
+ 96,
+ 96
+ ],
+ "random_size": false
+ }
+ ],
+ "preprocessing": {
+ "_target_": "Compose",
+ "transforms": "$@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()"
+ ]
+}
diff --git a/models/wholeBody_ct_segmentation/docs/README.md b/models/wholeBody_ct_segmentation/docs/README.md
new file mode 100644
index 00000000..9a2b7fad
--- /dev/null
+++ b/models/wholeBody_ct_segmentation/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].
+
+
+
+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.
+
+
+
+## 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
+
+
+
+ - Validation Dice
+
+
+
+## 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.
diff --git a/models/wholeBody_ct_segmentation/docs/data_license.txt b/models/wholeBody_ct_segmentation/docs/data_license.txt
new file mode 100644
index 00000000..0f702f0b
--- /dev/null
+++ b/models/wholeBody_ct_segmentation/docs/data_license.txt
@@ -0,0 +1,6 @@
+Third Party Licenses
+-----------------------------------------------------------------------
+
+/*********************************************************************/
+i. TotalSegmentator
+ https://zenodo.org/record/6802614#.Y9iTydLMJ6I
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diff --git a/models/wholeBody_ct_segmentation/large_files.yml b/models/wholeBody_ct_segmentation/large_files.yml
new file mode 100644
index 00000000..3420abf2
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+++ b/models/wholeBody_ct_segmentation/large_files.yml
@@ -0,0 +1,9 @@
+large_files:
+ - path: "models/model.pt"
+ url: "https://drive.google.com/file/d/1PHpFWboimEXmMSe2vBra6T8SaCMC2SHT/view?usp=share_link"
+ hash_val: ""
+ hash_type: ""
+ - path: "models/model_lowres.pt"
+ url: "https://drive.google.com/file/d/1c3osYscnr6710ObqZZS8GkZJQlWlc7rt/view?usp=share_link"
+ hash_val: ""
+ hash_type: ""