Upload multi_organ_segmentation version 0.0.5

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+docs/output_example.png filter=lfs diff=lfs merge=lfs -text
+models/model.ts filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,3 @@
+Private Use, Non-Commercial, Non-Reverse-Engineering License
+
+The Licensee is not allowed to distribute or make the model to any third party, either for free or for a fee. Reverse engineering of the model is not allowed. This includes, but is not limited to, providing the model as part of a commercial offering, sharing the model on a public or private network, or making the model available for download on the Internet.

configs/dataset_0.json

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+            "label": "ACC_g523h/ACC_g523h-label.nii.gz",
+            "image": "ACC_g523h/ACC_g523h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g150h/ACC_g150h-label.nii.gz",
+            "image": "ACC_g150h/ACC_g150h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g564h/ACC_g564h-label.nii.gz",
+            "image": "ACC_g564h/ACC_g564h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g191h/ACC_g191h-label.nii.gz",
+            "image": "ACC_g191h/ACC_g191h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g080h/ACC_g080h-label.nii.gz",
+            "image": "ACC_g080h/ACC_g080h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g587h/ACC_g587h-label.nii.gz",
+            "image": "ACC_g587h/ACC_g587h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g458h/ACC_g458h-label.nii.gz",
+            "image": "ACC_g458h/ACC_g458h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g223h/ACC_g223h-label.nii.gz",
+            "image": "ACC_g223h/ACC_g223h-data.nii.gz"
+        },
+        {
+            "label": "acc_g032/acc_g032-label.nii.gz",
+            "image": "acc_g032/acc_g032-data.nii.gz"
+        },
+        {
+            "label": "ACC_g176h/ACC_g176h-label.nii.gz",
+            "image": "ACC_g176h/ACC_g176h-data.nii.gz"
+        },
+        {
+            "label": "acc_g007/acc_g007-label.nii.gz",
+            "image": "acc_g007/acc_g007-data.nii.gz"
+        },
+        {
+            "label": "ACC_g675h/ACC_g675h-label.nii.gz",
+            "image": "ACC_g675h/ACC_g675h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g669h/ACC_g669h-label.nii.gz",
+            "image": "ACC_g669h/ACC_g669h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g710h/ACC_g710h-label.nii.gz",
+            "image": "ACC_g710h/ACC_g710h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g304h/ACC_g304h-label.nii.gz",
+            "image": "ACC_g304h/ACC_g304h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g423h/ACC_g423h-label.nii.gz",
+            "image": "ACC_g423h/ACC_g423h-data.nii.gz"
+        },
+        {
+            "label": "acc_g048h/acc_g048h-label.nii.gz",
+            "image": "acc_g048h/acc_g048h-data.nii.gz"
+        },
+        {
+            "label": "acc_g076h/acc_g076h-label.nii.gz",
+            "image": "acc_g076h/acc_g076h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g294h/ACC_g294h-label.nii.gz",
+            "image": "ACC_g294h/ACC_g294h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g520h/ACC_g520h-label.nii.gz",
+            "image": "ACC_g520h/ACC_g520h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g372h/ACC_g372h-label.nii.gz",
+            "image": "ACC_g372h/ACC_g372h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g641h/ACC_g641h-label.nii.gz",
+            "image": "ACC_g641h/ACC_g641h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g216h/ACC_g216h-label.nii.gz",
+            "image": "ACC_g216h/ACC_g216h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g573h/ACC_g573h-label.nii.gz",
+            "image": "ACC_g573h/ACC_g573h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g396h/ACC_g396h-label.nii.gz",
+            "image": "ACC_g396h/ACC_g396h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g143h/ACC_g143h-label.nii.gz",
+            "image": "ACC_g143h/ACC_g143h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g229h/ACC_g229h-label.nii.gz",
+            "image": "ACC_g229h/ACC_g229h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g250h/ACC_g250h-label.nii.gz",
+            "image": "ACC_g250h/ACC_g250h-data.nii.gz"
+        },
+        {
+            "label": "acc_g023/acc_g023-label.nii.gz",
+            "image": "acc_g023/acc_g023-data.nii.gz"
+        },
+        {
+            "label": "acc_g074h/acc_g074h-label.nii.gz",
+            "image": "acc_g074h/acc_g074h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g174h/ACC_g174h-label.nii.gz",
+            "image": "ACC_g174h/ACC_g174h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g107h/ACC_g107h-label.nii.gz",
+            "image": "ACC_g107h/ACC_g107h-data.nii.gz"
+        },
+        {
+            "label": "acc_g087h/acc_g087h-label.nii.gz",
+            "image": "acc_g087h/acc_g087h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g117h/ACC_g117h-label.nii.gz",
+            "image": "ACC_g117h/ACC_g117h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g267h/ACC_g267h-label.nii.gz",
+            "image": "ACC_g267h/ACC_g267h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g361h/ACC_g361h-label.nii.gz",
+            "image": "ACC_g361h/ACC_g361h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g683h/ACC_g683h-label.nii.gz",
+            "image": "ACC_g683h/ACC_g683h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g644h/ACC_g644h-label.nii.gz",
+            "image": "ACC_g644h/ACC_g644h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g479h/ACC_g479h-label.nii.gz",
+            "image": "ACC_g479h/ACC_g479h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g156h/ACC_g156h-label.nii.gz",
+            "image": "ACC_g156h/ACC_g156h-data.nii.gz"
+        },
+        {
+            "label": "ACC_g467h/ACC_g467h-label.nii.gz",
+            "image": "ACC_g467h/ACC_g467h-data.nii.gz"
+        }
+    ]
+}

configs/evaluate.yaml

@@ -0,0 +1,65 @@
+---
+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: 8
+  - _target_: CopyItemsd
+    keys: "pred"
+    times: 1
+    names: "pred_save"
+  - _target_: AsDiscreted
+    keys:
+    - pred_save
+    argmax:
+    - true
+  - _target_: SaveImaged
+    keys: pred_save
+    meta_keys: pred_meta_dict
+    output_dir: "@output_dir"
+    resample: false
+    squeeze_end_dims: true
+validate#dataset:
+    _target_: Dataset
+    data: "@val_datalist"
+    transform: "@validate#preprocessing"
+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: "*"
+initialize:
+- "$setattr(torch.backends.cudnn, 'benchmark', True)"
+run:
+- "$@validate#evaluator.run()"

configs/inference.yaml

@@ -0,0 +1,117 @@
+---
+imports:
+- "$import glob"
+- "$import os"
+input_channels: 1
+output_classes: 8
+arch_ckpt_path: "$@bundle_root + '/models/search_code_18590.pt'"
+arch_ckpt: "$torch.load(@arch_ckpt_path, map_location=torch.device('cuda'))"
+bundle_root: "."
+output_dir: "$@bundle_root + '/eval'"
+dataset_dir: "/data/"
+data_list_file_path: "$@bundle_root + '/configs/dataset_0.json'"
+datalist: "$monai.data.load_decathlon_datalist(@data_list_file_path, data_list_key='testing',
+  base_dir=@dataset_dir)"
+device: "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
+dints_space:
+  _target_: monai.networks.nets.TopologyInstance
+  channel_mul: 1
+  num_blocks: 12
+  num_depths: 4
+  use_downsample: true
+  arch_code:
+  - "$@arch_ckpt['arch_code_a']"
+  - "$@arch_ckpt['arch_code_c']"
+  device: "$torch.device('cuda')"
+network_def:
+  _target_: monai.networks.nets.DiNTS
+  dints_space: "@dints_space"
+  in_channels: "@input_channels"
+  num_classes: "@output_classes"
+  use_downsample: true
+  node_a: "$torch.from_numpy(@arch_ckpt['node_a'])"
+network: "$@network_def.to(@device)"
+preprocessing:
+  _target_: Compose
+  transforms:
+  - _target_: LoadImaged
+    keys: image
+  - _target_: EnsureChannelFirstd
+    keys: image
+  - _target_: Orientationd
+    keys: image
+    axcodes: RAS
+  - _target_: Spacingd
+    keys: image
+    pixdim:
+    - 1
+    - 1
+    - 1
+    mode: bilinear
+  - _target_: ScaleIntensityRanged
+    keys: image
+    a_min: -500
+    a_max: 500
+    b_min: 0
+    b_max: 1
+    clip: true
+  - _target_: EnsureTyped
+    keys: image
+dataset:
+  _target_: Dataset
+  data: "@datalist"
+  transform: "@preprocessing"
+dataloader:
+  _target_: DataLoader
+  dataset: "@dataset"
+  batch_size: 1
+  shuffle: false
+  num_workers: 4
+inferer:
+  _target_: SlidingWindowInferer
+  roi_size:
+  - 96
+  - 96
+  - 96
+  sw_batch_size: 4
+  overlap: 0.625
+postprocessing:
+  _target_: Compose
+  transforms:
+  - _target_: Activationsd
+    keys: pred
+    softmax: true
+  - _target_: Invertd
+    keys: pred
+    transform: "@preprocessing"
+    orig_keys: image
+    meta_key_postfix: meta_dict
+    nearest_interp: false
+    to_tensor: true
+  - _target_: AsDiscreted
+    keys: pred
+    argmax: true
+  - _target_: SaveImaged
+    keys: pred
+    meta_keys: pred_meta_dict
+    output_dir: "@output_dir"
+handlers:
+- _target_: CheckpointLoader
+  load_path: "$@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
+initialize:
+- "$setattr(torch.backends.cudnn, 'benchmark', True)"
+run:
+- "$@evaluator.run()"

configs/inference_trt.yaml

@@ -0,0 +1,8 @@
+---
+imports:
+- "$import glob"
+- "$import os"
+- "$import torch_tensorrt"
+handlers#0#_disabled_: true
+network_def: "$torch.jit.load(@bundle_root + '/models/model.ts')"
+evaluator#amp: false

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 +1,97 @@
+{
+    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
+    "version": "0.0.5",
+    "changelog": {
+        "0.0.5": "update to huggingface hosting",
+        "0.0.4": "Set image_only to False",
+        "0.0.3": "Update for stable MONAI version",
+        "0.0.2": "Retrain with new MONAI",
+        "0.0.1": "initialize the model package structure"
+    },
+    "monai_version": "1.3.0",
+    "pytorch_version": "1.13.1",
+    "numpy_version": "1.22.2",
+    "required_packages_version": {
+        "fire": "0.5.0",
+        "nibabel": "5.1.0",
+        "pytorch-ignite": "0.4.11",
+        "pyyaml": "6.0.2"
+    },
+    "supported_apps": {},
+    "name": "Abdominal multi-organ segmentation",
+    "task": "Multi-organ segmentation in abdominal CT",
+    "description": "DiNTS architectures for volumetric (3D) segmentation of the abdominal from CT image",
+    "authors": "Chen Shen, Holger R. Roth, Kazunari Misawa, Kensaku Mori",
+    "copyright": "",
+    "data_source": "Aichi Cancer Center, Japan",
+    "data_type": "nibabel",
+    "image_classes": "single channel data, intensity scaled to [0, 1]",
+    "label_classes": "eight channels data, 1 is artery, 2 is portal vein, 3 is liver, 4 is spleen, 5 is stomach, 6 is gallbladder, 7 is pancreas, 0 is everything else",
+    "pred_classes": "8 channels OneHot data, 1 is artery, 2 is portal vein, 3 is liver, 4 is spleen, 5 is stomach, 6 is gallbladder, 7 is pancreas, 0 is background",
+    "eval_metrics": {
+        "mean_dice": 0.88
+    },
+    "references": [
+        "He, Y., Yang, D., Roth, H., Zhao, C. and Xu, D., 2021. Dints: Differentiable neural network topology search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5841-5850).",
+        "Roth, H., Shen C, Oda H., Sugino T., Oda M., Hayashi Y., Misawa K., Mori K., 2018. A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation. International conference on medical image computing and computer-assisted intervention",
+        "Shen, C., Roth, H. R., Nath, V., Hayashi, Y., Oda, M., Misawa, K., Mori, K., 2022. Effective hyperparameter optimization with proxy data for multi-organ segmentation. In Medical Imaging 2022: Image Processing (Vol. 12032, pp. 200-206)"
+    ],
+    "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": 8,
+                "spatial_shape": [
+                    96,
+                    96,
+                    96
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1,
+                    2,
+                    3,
+                    4,
+                    5,
+                    6,
+                    7
+                ],
+                "is_patch_data": true,
+                "channel_def": {
+                    "0": "background",
+                    "1": "artery",
+                    "2": "portal vein",
+                    "3": "liver",
+                    "4": "spleen",
+                    "5": "stomach",
+                    "6": "gallbladder",
+                    "7": "pancreas"
+                }
+            }
+        }
+    }
+}

configs/multi_gpu_train.yaml

@@ -0,0 +1,54 @@
+---
+device: "$torch.device(f'cuda:{dist.get_rank()}')"
+network:
+  _target_: torch.nn.parallel.DistributedDataParallel
+  module: "$@network_def.to(@device)"
+  find_unused_parameters: true
+  device_ids:
+  - "@device"
+optimizer#lr: "$0.025*dist.get_world_size()"
+lr_scheduler#step_size: "$80*dist.get_world_size()"
+train#handlers:
+  - _target_: LrScheduleHandler
+    lr_scheduler: "@lr_scheduler"
+    print_lr: true
+  - _target_: ValidationHandler
+    validator: "@validate#evaluator"
+    epoch_level: true
+    interval: "$10*dist.get_world_size()"
+  - _target_: StatsHandler
+    tag_name: train_loss
+    output_transform: "$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)"
+train#trainer#max_epochs: "$400*dist.get_world_size()"
+train#trainer#train_handlers: "$@train#handlers[: -2 if dist.get_rank() > 0 else None]"
+validate#evaluator#val_handlers: "$None if dist.get_rank() > 0 else @validate#handlers"
+initialize:
+- "$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)"
+run:
+- "$@train#trainer.run()"
+finalize:
+- $dist.is_initialized() and dist.destroy_process_group()
+train_data_partition: "$monai.data.partition_dataset(data=@train_datalist, num_partitions=dist.get_world_size(),
+  shuffle=True, even_divisible=True,)[dist.get_rank()]"
+train#dataset:
+  _target_: CacheDataset
+  data: "@train_data_partition"
+  transform: "@train#preprocessing"
+  cache_rate: 0.2
+  num_workers: 4
+val_data_partition: "$monai.data.partition_dataset(data=@val_datalist, num_partitions=dist.get_world_size(),
+  shuffle=False, even_divisible=False,)[dist.get_rank()]"
+validate#dataset:
+  _target_: CacheDataset
+  data: "@val_data_partition"
+  transform: "@validate#preprocessing"
+  cache_rate: 0.2
+  num_workers: 4

configs/search.yaml

@@ -0,0 +1,276 @@
+---
+imports:
+  - "$from scipy import ndimage"
+arch_ckpt_path: models
+amp: true
+data_file_base_dir: /workspace/data/msd/Task07_Pancreas
+data_list_file_path: configs/dataset_0.json
+determ: true
+input_channels: 1
+learning_rate: 0.025
+learning_rate_arch: 0.001
+learning_rate_milestones:
+- 0.4
+- 0.8
+num_images_per_batch: 1
+num_epochs: 1430
+num_epochs_per_validation: 100
+num_epochs_warmup: 715
+num_patches_per_image: 1
+num_sw_batch_size: 6
+output_classes: 3
+overlap_ratio: 0.625
+patch_size:
+- 96
+- 96
+- 96
+patch_size_valid:
+- 96
+- 96
+- 96
+ram_cost_factor: 0.8
+image_key: image
+label_key: label
+transform_train:
+  _target_: Compose
+  transforms:
+  - _target_: LoadImaged
+    keys:
+    - "@image_key"
+    - "@label_key"
+    image_only: false
+  - _target_: EnsureChannelFirstd
+    keys:
+    - "@image_key"
+    - "@label_key"
+  - _target_: Orientationd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    axcodes: RAS
+  - _target_: Spacingd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    pixdim:
+    - 1
+    - 1
+    - 1
+    mode:
+    - bilinear
+    - nearest
+    align_corners:
+    - true
+    - true
+  - _target_: CastToTyped
+    keys: "@image_key"
+    dtype: "$torch.float32"
+  - _target_: ScaleIntensityRanged
+    keys: "@image_key"
+    a_min: -87
+    a_max: 199
+    b_min: 0
+    b_max: 1
+    clip: true
+  - _target_: CastToTyped
+    keys:
+    - "@image_key"
+    - "@label_key"
+    dtype:
+    - "$np.float16"
+    - "$np.uint8"
+  - _target_: CopyItemsd
+    keys: "@label_key"
+    times: 1
+    names:
+    - label4crop
+  - _target_: Lambdad
+    keys: label4crop
+    func: "$lambda x, s=@output_classes: np.concatenate(tuple([ndimage.binary_dilation((x==_k).astype(x.dtype), iterations=48).astype(float) for _k in range(s)]), axis=0)"
+    overwrite: true
+  - _target_: EnsureTyped
+    keys:
+    - "@image_key"
+    - "@label_key"
+  - _target_: CastToTyped
+    keys: "@image_key"
+    dtype: "$torch.float32"
+  - _target_: SpatialPadd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    - label4crop
+    spatial_size: "@patch_size"
+    mode:
+    - reflect
+    - constant
+    - constant
+  - _target_: RandCropByLabelClassesd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    label_key: label4crop
+    num_classes: "@output_classes"
+    ratios: "$[1,] * @output_classes"
+    spatial_size: "@patch_size"
+    num_samples: "@num_patches_per_image"
+  - _target_: Lambdad
+    keys: label4crop
+    func: "$lambda x: 0"
+  - _target_: RandRotated
+    keys:
+    - "@image_key"
+    - "@label_key"
+    range_x: 0.3
+    range_y: 0.3
+    range_z: 0.3
+    mode:
+    - bilinear
+    - nearest
+    prob: 0.2
+  - _target_: RandZoomd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    min_zoom: 0.8
+    max_zoom: 1.2
+    mode:
+    - trilinear
+    - nearest
+    align_corners:
+    - null
+    - null
+    prob: 0.16
+  - _target_: RandGaussianSmoothd
+    keys: "@image_key"
+    sigma_x:
+    - 0.5
+    - 1.15
+    sigma_y:
+    - 0.5
+    - 1.15
+    sigma_z:
+    - 0.5
+    - 1.15
+    prob: 0.15
+  - _target_: RandScaleIntensityd
+    keys: "@image_key"
+    factors: 0.3
+    prob: 0.5
+  - _target_: RandShiftIntensityd
+    keys: "@image_key"
+    offsets: 0.1
+    prob: 0.5
+  - _target_: RandGaussianNoised
+    keys: "@image_key"
+    std: 0.01
+    prob: 0.15
+  - _target_: RandFlipd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    spatial_axis: 0
+    prob: 0.5
+  - _target_: RandFlipd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    spatial_axis: 1
+    prob: 0.5
+  - _target_: RandFlipd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    spatial_axis: 2
+    prob: 0.5
+  - _target_: CastToTyped
+    keys:
+    - "@image_key"
+    - "@label_key"
+    dtype:
+    - "$torch.float32"
+    - "$torch.uint8"
+  - _target_: ToTensord
+    keys:
+    - "@image_key"
+    - "@label_key"
+transform_validation:
+  _target_: Compose
+  transforms:
+  - _target_: LoadImaged
+    keys:
+    - "@image_key"
+    - "@label_key"
+  - _target_: EnsureChannelFirstd
+    keys:
+    - "@image_key"
+    - "@label_key"
+  - _target_: Orientationd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    axcodes: RAS
+  - _target_: Spacingd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    pixdim:
+    - 1
+    - 1
+    - 1
+    mode:
+    - bilinear
+    - nearest
+    align_corners:
+    - true
+    - true
+  - _target_: CastToTyped
+    keys: "@image_key"
+    dtype: "$torch.float32"
+  - _target_: ScaleIntensityRanged
+    keys: "@image_key"
+    a_min: -87
+    a_max: 199
+    b_min: 0
+    b_max: 1
+    clip: true
+  - _target_: CastToTyped
+    keys:
+    - "@image_key"
+    - "@label_key"
+    dtype:
+    - "$np.float16"
+    - "$np.uint8"
+  - _target_: CastToTyped
+    keys:
+    - "@image_key"
+    - "@label_key"
+    dtype:
+    - "$torch.float32"
+    - "$torch.uint8"
+  - _target_: ToTensord
+    keys:
+    - "@image_key"
+    - "@label_key"
+loss:
+  _target_: DiceCELoss
+  include_background: false
+  to_onehot_y: true
+  softmax: true
+  squared_pred: true
+  batch: true
+  smooth_nr: 0.00001
+  smooth_dr: 0.00001
+dints_space:
+  _target_: monai.networks.nets.TopologySearch
+  channel_mul: 0.5
+  num_blocks: 12
+  num_depths: 4
+  use_downsample: true
+  device: "$torch.device('cuda')"
+network:
+  _target_: monai.networks.nets.DiNTS
+  dints_space: "@dints_space"
+  in_channels: "@input_channels"
+  num_classes: "@output_classes"
+  use_downsample: true

configs/train.yaml

@@ -0,0 +1,356 @@
+---
+imports:
+- "$import glob"
+- "$import json"
+- "$import os"
+- "$import ignite"
+- "$from scipy import ndimage"
+input_channels: 1
+output_classes: 8
+arch_ckpt_path: "$@bundle_root + '/models/search_code_18590.pt'"
+arch_ckpt: "$torch.load(@arch_ckpt_path, map_location=torch.device('cuda'))"
+bundle_root: "."
+ckpt_dir: "$@bundle_root + '/models'"
+output_dir: "$@bundle_root + '/eval'"
+dataset_dir: "/data/"
+data_list_file_path: "$@bundle_root + '/configs/dataset_0.json'"
+train_datalist: "$monai.data.load_decathlon_datalist(@data_list_file_path, data_list_key='training',
+  base_dir=@dataset_dir)"
+val_datalist: "$monai.data.load_decathlon_datalist(@data_list_file_path, data_list_key='validation',
+  base_dir=@dataset_dir)"
+device: "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
+dints_space:
+  _target_: monai.networks.nets.TopologyInstance
+  channel_mul: 1
+  num_blocks: 12
+  num_depths: 4
+  use_downsample: true
+  arch_code:
+  - "$@arch_ckpt['arch_code_a']"
+  - "$@arch_ckpt['arch_code_c']"
+  device: "$torch.device('cuda')"
+network_def:
+  _target_: monai.networks.nets.DiNTS
+  dints_space: "@dints_space"
+  in_channels: "@input_channels"
+  num_classes: "@output_classes"
+  use_downsample: true
+  node_a: "$@arch_ckpt['node_a']"
+network: "$@network_def.to(@device)"
+loss:
+  _target_: DiceCELoss
+  include_background: false
+  to_onehot_y: true
+  softmax: true
+  squared_pred: true
+  batch: true
+  smooth_nr: 1.0e-05
+  smooth_dr: 1.0e-05
+optimizer:
+  _target_: torch.optim.SGD
+  params: "$@network.parameters()"
+  momentum: 0.9
+  weight_decay: 4.0e-05
+  lr: 0.025
+lr_scheduler:
+  _target_: torch.optim.lr_scheduler.StepLR
+  optimizer: "@optimizer"
+  step_size: 80
+  gamma: 0.5
+image_key: image
+label_key: label
+val_interval: 10
+train:
+  deterministic_transforms:
+  - _target_: LoadImaged
+    keys:
+    - "@image_key"
+    - "@label_key"
+    image_only: false
+  - _target_: EnsureChannelFirstd
+    keys:
+    - "@image_key"
+    - "@label_key"
+  - _target_: Orientationd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    axcodes: RAS
+  - _target_: Spacingd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    pixdim:
+    - 1
+    - 1
+    - 1
+    mode:
+    - bilinear
+    - nearest
+    align_corners:
+    - true
+    - true
+  - _target_: CastToTyped
+    keys: "@image_key"
+    dtype: "$torch.float32"
+  - _target_: ScaleIntensityRanged
+    keys: "@image_key"
+    a_min: -500
+    a_max: 500
+    b_min: 0
+    b_max: 1
+    clip: true
+  - _target_: CastToTyped
+    keys:
+    - "@image_key"
+    - "@label_key"
+    dtype:
+    - "$np.float16"
+    - "$np.uint8"
+  - _target_: CopyItemsd
+    keys: "@label_key"
+    times: 1
+    names:
+    - label4crop
+  - _target_: Lambdad
+    keys: label4crop
+    func: "$lambda x, s=@output_classes: np.concatenate(tuple([ndimage.binary_dilation((x==_k).astype(x.dtype),
+      iterations=48).astype(float) for _k in range(s)]), axis=0)"
+    overwrite: true
+  - _target_: EnsureTyped
+    keys:
+    - "@image_key"
+    - "@label_key"
+  - _target_: CastToTyped
+    keys: "@image_key"
+    dtype: "$torch.float32"
+  - _target_: SpatialPadd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    - label4crop
+    spatial_size:
+    - 96
+    - 96
+    - 96
+    mode:
+    - reflect
+    - constant
+    - constant
+  random_transforms:
+  - _target_: RandCropByLabelClassesd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    label_key: label4crop
+    num_classes: "@output_classes"
+    ratios: "$[1,] * @output_classes"
+    spatial_size:
+    - 96
+    - 96
+    - 96
+    num_samples: 1
+  - _target_: Lambdad
+    keys: label4crop
+    func: "$lambda x: 0"
+  - _target_: RandRotated
+    keys:
+    - "@image_key"
+    - "@label_key"
+    range_x: 0.3
+    range_y: 0.3
+    range_z: 0.3
+    mode:
+    - bilinear
+    - nearest
+    prob: 0.2
+  - _target_: RandZoomd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    min_zoom: 0.8
+    max_zoom: 1.2
+    mode:
+    - trilinear
+    - nearest
+    align_corners:
+    - true
+    -
+    prob: 0.16
+  - _target_: RandGaussianSmoothd
+    keys: "@image_key"
+    sigma_x:
+    - 0.5
+    - 1.15
+    sigma_y:
+    - 0.5
+    - 1.15
+    sigma_z:
+    - 0.5
+    - 1.15
+    prob: 0.15
+  - _target_: RandScaleIntensityd
+    keys: "@image_key"
+    factors: 0.3
+    prob: 0.5
+  - _target_: RandShiftIntensityd
+    keys: "@image_key"
+    offsets: 0.1
+    prob: 0.5
+  - _target_: RandGaussianNoised
+    keys: "@image_key"
+    std: 0.01
+    prob: 0.15
+  - _target_: RandFlipd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    spatial_axis: 0
+    prob: 0.5
+  - _target_: RandFlipd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    spatial_axis: 1
+    prob: 0.5
+  - _target_: RandFlipd
+    keys:
+    - "@image_key"
+    - "@label_key"
+    spatial_axis: 2
+    prob: 0.5
+  - _target_: CastToTyped
+    keys:
+    - "@image_key"
+    - "@label_key"
+    dtype:
+    - "$torch.float32"
+    - "$torch.uint8"
+  - _target_: ToTensord
+    keys:
+    - "@image_key"
+    - "@label_key"
+  preprocessing:
+    _target_: Compose
+    transforms: "$@train#deterministic_transforms + @train#random_transforms"
+  dataset:
+    _target_: CacheDataset
+    data: "@train_datalist"
+    transform: "@train#preprocessing"
+    cache_rate: 0.125
+    num_workers: 4
+  dataloader:
+    _target_: DataLoader
+    dataset: "@train#dataset"
+    batch_size: 2
+    shuffle: true
+    num_workers: 4
+  inferer:
+    _target_: SimpleInferer
+  postprocessing:
+    _target_: Compose
+    transforms:
+    - _target_: Activationsd
+      keys: pred
+      softmax: true
+    - _target_: AsDiscreted
+      keys:
+      - pred
+      - label
+      argmax:
+      - true
+      - false
+      to_onehot: "@output_classes"
+  handlers:
+  - _target_: LrScheduleHandler
+    lr_scheduler: "@lr_scheduler"
+    print_lr: true
+  - _target_: ValidationHandler
+    validator: "@validate#evaluator"
+    epoch_level: true
+    interval: "@val_interval"
+  - _target_: StatsHandler
+    tag_name: train_loss
+    output_transform: "$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: 400
+    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: "@val_datalist"
+    transform: "@validate#preprocessing"
+    cache_rate: 0.125
+  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: 6
+    overlap: 0.625
+  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
+initialize:
+- "$monai.utils.set_determinism(seed=123)"
+- "$setattr(torch.backends.cudnn, 'benchmark', True)"
+run:
+- "$@train#trainer.run()"

docs/README.md

@@ -0,0 +1,125 @@
+# Multi-organ segmentation in abdominal CT
+
+### **Authors**
+
+Chen Shen<sup>1</sup>, Holger R. Roth<sup>2</sup>, Kazunari Misawa<sup>3</sup>, Kensaku Mori<sup>1</sup>
+
+1. Nagoya University, Japan
+
+2. NVIDIA Corporation, USA
+
+3. Aichi Cancer Center, Japan
+
+### **Tags**
+
+Segmentation, Multi-organ, Abdominal
+
+## **Model Description**
+
+This model uses the DiNTS model architecture searched on [Medical Segmentation Decathlon](http://medicaldecathlon.com/) Pancreas [1] and re-trained for multi-organ segmentation from abdominal CT images [2,3].
+
+## **Data**
+
+This model was trained on an abdominal CT dataset in portal venous phase collected from Aichi Cancer Center in Japan. Since this is a private dataset, similar models can be trained using other public multi-organ datasets like [BTCV](https://www.synapse.org/#!Synapse:syn3193805/wiki/89480).
+
+For this bundle, we split the 420 cases into training, validation and testing with 300, 60 and 60 cases, respectively.
+
+## **Output**
+8 channels
+
+- 0: Background
+- 1: Artery
+- 2: Portal vein
+- 3: Liver
+- 4: Spleen
+- 5: Stomach
+- 6: Gallbladder
+- 7: Pancreas
+
+Here is an example of output.
+
+![alt用テキスト](output_example.png)
+
+## **Scores**
+
+This model achieves the following Dice score on the validation data (our own split from the whole dataset):
+
+Mean Dice = 88.6%
+
+## 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 model searching:
+
+```
+python -m scripts.search run --config_file configs/search.yaml
+```
+
+#### Execute multi-GPU model searching (recommended):
+
+```
+torchrun --nnodes=1 --nproc_per_node=8 -m scripts.search run --config_file configs/search.yaml
+```
+
+#### Execute training:
+
+```
+python -m monai.bundle run --config_file configs/train.yaml
+```
+
+Please note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using `--dataset_dir`:
+
+```
+python -m monai.bundle run --config_file configs/train.yaml
+```
+
+#### Override the `train` config to execute multi-GPU training:
+
+```
+torchrun --nnodes=1 --nproc_per_node=8 \
+     -m scripts.search run \
+     --config_file configs/search.yaml
+```
+
+#### Override the `train` config to execute evaluation with the trained model:
+
+```
+python -m monai.bundle run \
+    --config_file "['configs/train.yaml','configs/evaluate.yaml']"
+```
+
+#### Execute inference:
+```
+python -m monai.bundle run --config_file configs/inference.yaml
+```
+
+
+#### Export checkpoint for TorchScript:
+
+```
+python -m monai.bundle ckpt_export network_def --filepath models/model.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.yaml
+```
+
+#### Execute inference with the TensorRT model:
+
+```
+python -m monai.bundle run --config_file "['configs/inference.yaml', 'configs/inference_trt.yaml']"
+```
+
+
+## **References**
+
+[1] He, Y., Yang, D., Roth, H., Zhao, C. and Xu, D., 2021. Dints: Differentiable neural network topology search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5841-5850).
+
+​
+[2] Roth, Holger R., et al. "A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation." International conference on medical image computing and computer-assisted intervention. Springer, Cham, 2018.
+​
+
+[3] Shen, Chen, et al. "Effective hyperparameter optimization with proxy data for multi-organ segmentation." Medical Imaging 2022: Image Processing. Vol. 12032. SPIE, 2022.
+
+
+
+## **License**
+The Licensee is not allowed to distribute or make the model to any third party, either for free or for a fee. Reverse engineering of the model is not allowed. This includes, but is not limited to, providing the model as part of a commercial offering, sharing the model on a public or private network, or making the model available for download on the Internet.

models/model.pt

@@ -0,0 +1,3 @@
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+size 616156645

models/model.ts

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3da668b2484e263ff01c148ed4210df4421f8bbd7683da34b7930d965011a9e5
+size 616352855

models/search_code_18590.pt

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+size 4335