monaibundle

MONAI Bundle Application

The MONAIBundle App allows you to easily pull any MONAI Bundle from the MONAI Model Zoo and import it into MONAI Label. However, it's important to note that any MONAI Bundle used with MONAI Label must meet the following constraints:

• It must comply with the MONAI Bundle Specification.
• For inference, the bundle must define either an inference.json or inference.yaml file, and it must include the keys described in the bundle.py file located in the monailabel/tasks/infer/ directory.
• For training, the bundle must define either a train.json or train.yaml file, and it must include the keys described in the bundle.py file located in the monailabel/tasks/train/ directory.
• For multi-GPU training, the bundle must define either a multi_gpu_train.json or multi_gpu_train.yaml file.

These constraints ensure that any MONAI Bundle used with MONAI Label is compatible with the platform and can be seamlessly integrated into your workflow.

Table of Contents

• Supported Models
• How To Use the App
• Epistemic Scoring using MONAI Bundles\
• Detection Model

Supported Models

The MONAIBundle App currently supports most labeling models in the Model-Zoo. You can find a table of supported labeling tasks below. Please note that the list of supported tasks is updated based on the latest release from the Model-Zoo.

Bundle	Model	Objects	Modality	Note
spleen_ct_segmentation	UNet	Spleen	CT	A model for (3D) segmentation of the spleen
swin_unetr_btcv_segmentation	SwinUNETR	Multi-Organ	CT	A model for (3D) multi-organ segmentation
prostate_mri_anatomy	UNet	Prostate	MRI	A model for (3D) prostate segmentation from MRI image
pancreas_ct_dints_segmentation	DiNTS	Pancreas/Tumor	CT	An automl method for (3D) pancreas/tumor segmentation
renalStructures_UNEST_segmentation	UNesT	Kidney Substructure	CT	A pre-trained for inference (3D) kidney cortex/medulla/pelvis segmentation
wholeBrainSeg_UNEST_segmentation	UNesT	Whole Brain	MRI T1	A pre-trained for inference (3D) 133 whole brain structures segmentation
spleen_deepedit_annotation	DeepEdit	Spleen	CT	An interactive method for 3D spleen Segmentation
lung_nodule_ct_detection	RetinaNet	Lung Nodule	CT	The detection model for 3D CT images
wholeBody_ct_segmentation	SegResNet	104 body structures	CT	The segmentation model for 104 tissue from 3D CT images (TotalSegmentator Dataset)

How To Use the App

# skip this if you have already downloaded the app or using github repository (dev mode)
monailabel apps --download --name monaibundle --output workspace

# List all available models from zoo
monailabel start_server --app workspace/monaibundle --studies workspace/images

# Pick spleen_ct_segmentation_v0.1.0 model
monailabel start_server --app workspace/monaibundle --studies workspace/images --conf models spleen_ct_segmentation_v0.1.0

# Pick spleen_ct_segmentation_v0.1.0 model and preload
monailabel start_server --app workspace/monaibundle --studies workspace/images --conf models spleen_ct_segmentation_v0.1.0 --conf preload true

# Pick DeepEdit And Segmentation model (multiple models)
monailabel start_server --app workspace/monaibundle --studies workspace/images --conf models "spleen_ct_segmentation_v0.1.0,spleen_deepedit_annotation_v0.1.0"

# Skip Training Tasks or Infer only mode
monailabel start_server --app workspace/monaibundle --studies workspace/images --conf models spleen_ct_segmentation_v0.1.0 --conf skip_trainers true

Specify bundle version (Optional) Above command will download the latest bundles from Model-Zoo by default. If a specific or older bundle version is used, users can add version _v followed by the bundle name. Example:

monailabel start_server --app workspace/monaibundle --studies workspace/images --conf models spleen_ct_segmentation_v0.3.7

Note: bundles in Model-Zoo are continuously updated, old bundles may contain deprecated API, we recommend using the latest realeased MONAI Label and bundle for the best experience.

Epistemic Scoring for monaibundle app

The MONAIBundle App supports epistemic scoring using bundle models. To use epistemic scoring, you can specify a valid scoring bundle model as the epistemic_model configuration parameter when running the app, E.g., --conf epistemic_model <bundlename>. If a valid scoring bundle model is provided, scoring inference will be triggered. The loaded scoring bundle model can be either a model from the Model Zoo or a local bundle, but it must support the dropout argument.

With epistemic scoring, MONAIBundle can provide measures of uncertainty or confidence in the model's predictions, which can be useful in a variety of applications.

# Use the UNet in spleen_ct_segmentation_v0.2.0 bundle as epistemic scoring model.
# Manual define epistemic scoring parameters
monailabel start_server \
  --app workspace/monaibundle \
  --studies workspace/images \
  --conf models spleen_ct_segmentation_v0.2.0,swin_unetr_btcv_segmentation_v0.2.0 \
  --conf epistemic_model spleen_ct_segmentation_v0.2.0
  --conf epistemic_max_samples 0 \
  --conf epistemic_simulation_size 5
  --conf epistemic_dropout 0.2

Detection Model

MONAI Label now supports detection models. We demonstrate an example of lung nodule detection model from model-zoo.

3D Slicer plugin needs to be updated or use the latest developer mode.

Step 1: Prerequisites

Get latest MONAI Label Slicer plugin (Optional)

If you are using older release of 3D Slicer MONAI Label plugin, you can update to the latest plugin which supports drawing ROI nodes from MONAI Label server prediction.

Or to use the developer mode of MONAI Label plugin by:

• Pull latest MONAI Label repo:

git clone https://github.com/Project-MONAI/MONAILabel.git

• Go to MONAILabel/plugins/slicer/
• Follow the Readme page to use developer mode of Slicer plugin. https://github.com/Project-MONAI/MONAILabel/tree/main/plugins/slicer#install-plugin-in-developer-mode
  • Install Plugin in Developer Mode
    • Open 3D Slicer: Go to Edit -> Application Settings -> Modules -> Additional Module Paths
    • Add New Module Path: <FULL_PATH>/plugins/slicer/MONAILabel
    • Restart 3D Slicer

Step 2: Download monaibundle App

Sample-app with built-in versions are in the downloads, fetch the monaibundle app using monailabel API

  monailabel apps --download --name monaibundle --output apps

Step 3: Prepare Sample Data

The lung nodule detection model are trained and evaluated using LUNA16 dataset.

LUNA16 is a public dataset of CT lung nodule detection. Using raw CT scans, the goal is to identify locations of possible nodules, and to assign a probability for being a nodule to each location.

Disclaimer: We are not the host of the data. Please make sure to read the requirements and usage policies of the data and give credit to the authors of the dataset! We acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study.

• If you are going to use full datset of LUNA16, please refer to the dataset link, download the data, create and preprocess the images following this page.

• In this tutorial, we prepared a sample subset, resampled and ready to use. The subset is only for demonstration. Download here.

Or use these commands to download and extract directly.

mkdir datasets
wget "https://github.com/Project-MONAI/MONAILabel/releases/download/data/detection_dataset.zip" -O datasets/detection_dataset.zip
unzip datasets/detection_dataset.zip -d datasets/detection_dataset

• The detection model also supports MSD Task06_Lung dataset for inference. Note this dataset is created for nodule segmentation task. We do not use the segmentation mask, only the images for inference. Use the following monailabel API to automatically download the dataset.

monailabel datasets --download --name Task06_Lung --output datasets

Step 4: Start MONAI Label server and 3D Slicer to Annotate

All models, datasets, and plugins are ready, start the MONAI Label server and open 3D Slicer to annotate!

# 1: Use LUNA16 sample data
monailabel start_server --app apps/monaibundle --studies datasets/Task06_Lung/imagesTr --conf models lung_nodule_ct_detection
# 2: Use Task06_Lung data for inference demonstration
monailabel start_server --app apps/monaibundle --studies datasets/detection_dataset --conf models lung_nodule_ct_detection

Step 5: Modify Detection Parameters in the Bundle (Optional)

The lung_nodule_ct_detection bundle can set the box prediction probability threshold. In apps/monaibundle/model/lung_nodule_ct_detection/configs/inference.json, it's define here score_thresh=0.02:

    "detector_ops": [
        "[email protected]_target_keys(box_key='box', label_key='label')",
        "[email protected]_box_selector_parameters(score_thresh=0.02,topk_candidates_per_level=1000,nms_thresh=0.22,detections_per_img=300)",
        "[email protected]_sliding_window_inferer(roi_size=@infer_patch_size,overlap=0.25,sw_batch_size=1,mode='constant',device='cpu')"
    ],

This parameter will impact the number of boxes in the final prediction output. Then show all boxes ROIs in 3D Slicer.

If users want to limit the number of predicted boxes showing on 3D Slicer, users can set higher score_thresh, e.g., score_thresh=0.5, score_thresh=0.6, or even higher.

Additional Configs

To set configuration parameters for MONAI Label Server, use the --conf <name> <value> flag followed by the parameter name and value while starting the MONAI Label Server.

Name	Values	Description
zoo_source	string	Default value: github
zoo_repo	string	Default value: Project-MONAI/model-zoo/hosting_storage_v1
preload	true, false	Preload model into GPU
skip_trainers	true, false	Skip adding training tasks (Run in Infer mode only)
epistemic_max_samples	int	Default value: 0 ; Epistemic scoring parameters
epistemic_simulation_size	int	Default value: 5 ; Epistemic simulation size parameters
epistemic_dropout	float	Default value: 0.2 ; Epistemic scoring parameters: Dropout rate for scoring models