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1 Add Spleen bundle (#2)
* add spleen bundle Signed-off-by: Yiheng Wang <[email protected]> * add issue link Signed-off-by: Yiheng Wang <[email protected]> * modify bundle_root Signed-off-by: Yiheng Wang <[email protected]> * add toml file Signed-off-by: Yiheng Wang <[email protected]> * update spleen bundle Signed-off-by: Yiheng Wang <[email protected]> * [DLMED] update according to comments Signed-off-by: Nic Ma <[email protected]> * update ts model Signed-off-by: Yiheng Wang <[email protected]> * redefine spleen folder * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * revert false changes for deepedit update metadata * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * revert spleen error changes * revert ts model change * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * reproduce ts module * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * remove model files and add yml * update metadata info Signed-off-by: Yiheng Wang <[email protected]> Co-authored-by: Nic Ma <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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‎models/spleen_ct_segmentation/configs/evaluate.json‎

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{
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"validate#postprocessing": {
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"_target_": "Compose",
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"transforms": [
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{
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"_target_": "Activationsd",
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"keys": "pred",
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"softmax": true
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},
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{
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"_target_": "Invertd",
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"keys": [
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"pred",
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"label"
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],
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"transform": "@validate#preprocessing",
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"orig_keys": "image",
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"meta_key_postfix": "meta_dict",
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"nearest_interp": [
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false,
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true
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],
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"to_tensor": true
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},
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{
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"_target_": "AsDiscreted",
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"keys": [
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"pred",
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"label"
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],
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"argmax": [
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true,
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false
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],
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"to_onehot": 2
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},
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{
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"_target_": "SaveImaged",
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"keys": "pred",
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"meta_keys": "pred_meta_dict",
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"output_dir": "@output_dir",
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"resample": false,
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"squeeze_end_dims": true
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}
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]
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},
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"validate#handlers": [
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{
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"_target_": "CheckpointLoader",
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"load_path": "$@ckpt_dir + '/model.pt'",
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"load_dict": {
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"model": "@network"
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}
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},
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{
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"_target_": "StatsHandler",
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"iteration_log": false
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},
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{
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"_target_": "MetricsSaver",
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"save_dir": "@output_dir",
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"metrics": [
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"val_mean_dice",
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"val_acc"
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],
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"metric_details": [
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"val_mean_dice"
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],
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"batch_transform": "$monai.handlers.from_engine(['image_meta_dict'])",
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"summary_ops": "*"
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}
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],
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"evaluating": [
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"$setattr(torch.backends.cudnn, 'benchmark', True)",
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"$@validate#evaluator.run()"
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]
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}

‎models/spleen_ct_segmentation/configs/inference.json‎

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{
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"imports": [
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"$import glob",
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"$import os"
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],
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"bundle_root": "/workspace/data/tutorials/modules/bundle/spleen_segmentation",
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"output_dir": "$@bundle_root + '/eval'",
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"dataset_dir": "/workspace/data/Task09_Spleen",
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"datalist": "$list(sorted(glob.glob(@dataset_dir + '/imagesTs/*.nii.gz')))",
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"device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
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"network_def": {
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"_target_": "UNet",
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"spatial_dims": 3,
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"in_channels": 1,
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"out_channels": 2,
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"channels": [
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16,
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32,
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64,
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128,
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256
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],
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"strides": [
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2,
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2,
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2,
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2
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],
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"num_res_units": 2,
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"norm": "batch"
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},
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"network": "$@network_def.to(@device)",
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"preprocessing": {
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"_target_": "Compose",
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"transforms": [
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{
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"_target_": "LoadImaged",
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"keys": "image"
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},
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{
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"_target_": "EnsureChannelFirstd",
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"keys": "image"
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},
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{
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"_target_": "Orientationd",
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"keys": "image",
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"axcodes": "RAS"
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},
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{
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"_target_": "Spacingd",
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"keys": "image",
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"pixdim": [
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1.5,
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1.5,
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2.0
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],
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"mode": "bilinear"
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},
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{
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"_target_": "ScaleIntensityRanged",
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"keys": "image",
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"a_min": -57,
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"a_max": 164,
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"b_min": 0,
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"b_max": 1,
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"clip": true
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},
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{
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"_target_": "EnsureTyped",
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"keys": "image"
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}
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]
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},
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"dataset": {
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"_target_": "Dataset",
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"data": "$[{'image': i} for i in @datalist]",
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"transform": "@preprocessing"
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},
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"dataloader": {
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"_target_": "DataLoader",
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"dataset": "@dataset",
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"batch_size": 1,
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"shuffle": false,
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"num_workers": 4
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},
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"inferer": {
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"_target_": "SlidingWindowInferer",
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"roi_size": [
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96,
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96
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],
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"sw_batch_size": 4,
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"overlap": 0.5
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},
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"postprocessing": {
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"_target_": "Compose",
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"transforms": [
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{
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"_target_": "Activationsd",
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"keys": "pred",
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"softmax": true
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},
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{
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"_target_": "Invertd",
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"keys": "pred",
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"transform": "@preprocessing",
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"orig_keys": "image",
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"meta_key_postfix": "meta_dict",
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"nearest_interp": false,
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"to_tensor": true
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},
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{
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"_target_": "AsDiscreted",
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"keys": "pred",
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"argmax": true
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},
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{
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"_target_": "SaveImaged",
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"keys": "pred",
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"meta_keys": "pred_meta_dict",
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"output_dir": "@output_dir"
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}
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]
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},
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"handlers": [
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{
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"_target_": "CheckpointLoader",
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"load_path": "$@bundle_root + '/models/model.pt'",
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"load_dict": {
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"model": "@network"
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}
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},
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{
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"_target_": "StatsHandler",
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"iteration_log": false
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}
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],
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"evaluator": {
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"_target_": "SupervisedEvaluator",
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"device": "@device",
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"val_data_loader": "@dataloader",
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"network": "@network",
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"inferer": "@inferer",
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"postprocessing": "@postprocessing",
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"val_handlers": "@handlers",
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"amp": true
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},
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"evaluating": [
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"$setattr(torch.backends.cudnn, 'benchmark', True)",
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"[email protected]()"
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]
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}

‎models/spleen_ct_segmentation/configs/logging.conf‎

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

‎models/spleen_ct_segmentation/configs/metadata.json‎

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{
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"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
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"version": "0.1.0",
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"changelog": {
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"0.1.0": "complete the model package",
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"0.0.1": "initialize the model package structure"
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},
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"monai_version": "0.9.0",
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"pytorch_version": "1.10.0",
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"numpy_version": "1.21.2",
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"optional_packages_version": {
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"nibabel": "3.2.1"
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},
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"task": "Decathlon spleen segmentation",
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"description": "A pre-trained model for volumetric (3D) segmentation of the spleen from CT image",
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"authors": "MONAI team",
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"copyright": "Copyright (c) MONAI Consortium",
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"data_source": "Task09_Spleen.tar from http://medicaldecathlon.com/",
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"data_type": "nibabel",
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"image_classes": "single channel data, intensity scaled to [0, 1]",
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"label_classes": "single channel data, 1 is spleen, 0 is everything else",
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"pred_classes": "2 channels OneHot data, channel 1 is spleen, channel 0 is background",
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"eval_metrics": {
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"mean_dice": 0.96
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},
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"intended_use": "This is an example, not to be used for diagnostic purposes",
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"references": [
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"Xia, Yingda, et al. '3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training. arXiv preprint arXiv:1811.12506 (2018). https://arxiv.org/abs/1811.12506.",
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"Kerfoot E., Clough J., Oksuz I., Lee J., King A.P., Schnabel J.A. (2019) Left-Ventricle Quantification Using Residual U-Net. In: Pop M. et al. (eds) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science, vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_40"
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],
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"network_data_format": {
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"inputs": {
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"image": {
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"type": "image",
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"format": "hounsfield",
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"modality": "CT",
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"num_channels": 1,
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"spatial_shape": [
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96,
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96,
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96
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],
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"dtype": "float32",
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"value_range": [
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0,
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1
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],
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"is_patch_data": true,
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"channel_def": {
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"0": "image"
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}
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}
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},
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"outputs": {
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"pred": {
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"type": "image",
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"format": "segmentation",
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"num_channels": 2,
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"spatial_shape": [
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96,
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96,
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96
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],
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"dtype": "float32",
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"value_range": [
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0,
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1
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],
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"is_patch_data": true,
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"channel_def": {
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"0": "background",
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"1": "spleen"
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}
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}
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}
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}
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}

‎models/spleen_ct_segmentation/configs/multi_gpu_train.json‎

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

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