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Clara AI Brain Tumor Segmentation Pipeline

This asset requires the Clara Deploy SDK. Follow the instructions on the\nClara Ansible page\nto install the Clara Deploy SDK.

Overview

The Brain Tumor Segmentation pipeline is one of the reference pipelines provided with Clara Deploy SDK.\nA pre-trained model for volumetric (3D) brain tumor segmentation (only tumor core (TC) from T1c images) is used in the pipeline.\nThis pipeline depends on the Clara Deploy DICOM Adapter to receive DICOM images, specifically\nsingle channel 3D MRIs (T1c). Once the DICOM instances are stored, the pipeline is\nstarted to first convert the DICOM instances to a volume image in\nMetaIO format and then process the input\nvolume image with the Brain Tumor Segmentation AI model. The AI model generates the labeled\ntumor cores within the brain as a binary mask on slices of the volume at the same size as\nthe input. The brain tumor core is labeled as 1, and the background is labeled as 0.

In its final steps, the pipeline outputs the binary mask as a new DICOM series using the original\nstudy instance UID; also, it outputs the original and segmented volumes to the Clara Deploy\nRender Server for visualization on the Clara Dashboard.

Pipeline Definition

The Brain Tumor Segmentation pipeline is defined in the Clara Deploy pipeline definition language.\nThis pipeline utilizes built-in reference containers to construct the following set of operators:

The dicom-reader operator converts input DICOM data into volume images in MHD format.
The brain-tumor-segmentation operator performs AI inference against the NVIDIA Triton Inference\nserver to generate brain- and tumor-segmentation volume images.
The dicom-writer converts the segmented volume image into DICOM instances, each with a new series\ninstance UID and the original DICOM-study instance UID.
The register-dicom-output operator registers the DICOM instances with the Clara Deploy DICOM\nAdapter, which in turn stores each instance on external DICOM devices per the configuration.
The register-volume-images-for-rendering operator registers original and segmented volume images\nwith the Clara Deploy Render Server for visualization.

The following are details of the pipeline definition, with comments describing how each operator\nfunctions, as well as their input and output.

api-version: 0.4.0\nname: brain-tumor-pipeline\noperators:\n# dicom reader operator\n# Input: '/input' mapped directly to the input of the pipeline, which is populated by the DICOM Adaptor.\n# Output:'/output' for saving converted volume image in MHD format to file whose name\n#            is the same as the DICOM series instance ID.\n- name: dicom-reader\n  description: Converts DICOM instances into MHD, one file per DICOM series.\n  container:\n    image: clara/dicom-reader\n    tag: latest\n  input:\n  - path: /input\n  output:\n  - path: /output\n# brain-tumor-segmentation operator\n# Input: `/input` containing volume image data, MHD format, with a single volume.\n# Output: `/output` containing segmented volume image, MHD format.\n#         `/publish` containing original and segmented volume images, MHD format,\n#             along with rendering configuration file.\n- name: brain-tumor-segmentation\n  description: Segmentation of brain tumor core inferencing using DL trained model.\n  container:\n    image: clara/ai-braintumor\n    tag: latest\n  requests:\n    memory: 8192\n  input:\n  - from: dicom-reader\n    path: /input\n  output:\n  - path: /output\n    name: segmentation\n  - path: /publish\n    name: rendering\n  models:\n  # change the following line to match the name created for the model\n    - name: segmentation_mri_brain_tumors_br16_t1c2tc_v1\n# dicom writer operator\n# Input1: `/input` containing a volume image file, in MHD format, name matching the DICOM series instance UID.\n# Input2: `/dicom` containing original DICOM instances, i.e. dcm file.\n# Output: `/output` containing the DICOM instances converted from the volume image, with updated attributes\n#         based on original DICOM instances.\n- name: dicom-writer\n  description: Converts MHD into DICOM instance with attributes based on the original instances.\n  container:\n    image: clara/dicom-writer\n    tag: latest\n  input:\n  - from: brain-tumor-segmentation\n    name: segmentation\n    path: /input\n  - path: /dicom\n  output:\n  - path: /output\n    name: dicom\n# register-volume-images-for-rendering operator\n# Input: Published original and segmented volume images, MHD format, along with rendering configuration file\n#        from brain-tumor-segmentation operator.\n# Output: N/A. Input data will be sent to the destination, namely `renderserver` for Render Server DataSet Service.\n- name: register-volume-images-for-rendering\n  description: Register volume images, MHD format, for rendering.\n  container:\n    image: clara/register-results\n    tag: latest\n    command: [\"python\", \"register.py\", \"--agent\", \"renderserver\"]\n  input:\n  - from: brain-tumor-segmentation\n    name: rendering\n    path: /input\n# register-dicom-output operator\n# Input: `/input` containing DICOM instances in the named output, `dicom` from dicom-writer operator.\n# Output: N/A. Input data will be sent to the destinations, namely DICOM devices, by the Clara DICOM SCU agent.\n- name: register-dicom-output\n  description: Register converted DICOM instances with Results Service to be sent to external DICOM devices.\n  container:\n    image: clara/register-results\n    tag: latest\n    command: [\"python\", \"register.py\", \"--agent\", \"ClaraSCU\", \"--data\", \"[\\\"MYPACS\\\"]\"]\n  input:\n  - from: dicom-writer\n    name: dicom\n    path: /input\n\n

Executing the Pipeline

Please refer to the Quick Start Guide on how to register a pipeline, configure the DICOM Adapter,\nand execute the pipeline.

License

An End User License Agreement\nis included with the product. By pulling and using the Clara Deploy asset on NGC, you accept the\nterms and conditions of these licenses.

Suggested Reading

Release Notes, the Getting Started Guide, and the SDK itself are available at the NVIDIA Developer forum.

For answers to any questions you may have about this release, visit the NVIDIA Devtalk forum.

","resourceVersions",[218,219,220,221,222,223,224,225,226],"resourceVersionsTotal",8,"hasMoreVersions","versionMetadata",{"_187":22,"_188":189,"_49":190,"_191":-5,"_192":193,"_194":-5,"_195":196,"_197":16,"_198":31,"_199":-5,"_200":-5,"_201":31,"_202":203,"_204":103,"_205":206,"_207":103,"_208":209,"_210":103,"_211":212,"_213":31,"_214":212,"_215":31,"_216":217},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/clara/resources/clara_ai_brain_tumor_pipeline/-","version","imageReference","registry","targetPath","cliDownloadCommand","ngc registry resource download-version nvidia/clara/clara_ai_brain_tumor_pipeline:0.7.2-2010.1","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/clara/resources/clara_ai_brain_tumor_pipeline/0.7.2-2010.1/version-history","accessCheckResult",{"_186":103},"hasRelatedCollections","showNimMetadataTab","context",{"_182":183,"_184":185},1793189367645,[],[],[175,176,177,178,179,180,181],{"_45":59,"_47":69,"_49":61,"_51":62,"_53":70,"_55":56},{"_45":59,"_47":68,"_49":61,"_51":62,"_53":63,"_55":56},{"_45":59,"_47":66,"_49":61,"_51":62,"_53":67,"_55":56},{"_45":59,"_47":64,"_49":119,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":61,"_51":62,"_53":63,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":54,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},"orgName","nvidia","teamName","clara","allowed","versionId","description","# Clara AI Brain Tumor Segmentation Pipeline\n\nThis asset requires the Clara Deploy SDK. Follow the instructions on the\n[Clara Bootstrap](https://ngc.nvidia.com/catalog/model-scripts/nvidia:clara:clara_bootstrap) page\nto install the Clara Deploy SDK.\n\n## Overview\n\nThe Brain Tumor Segmentation pipeline is one of the reference pipelines provided with Clara Deploy SDK.\nA pre-trained model for volumetric (3D) brain tumor segmentation (only tumor core (TC) from T1c images) is used in the pipeline.\nThis pipeline depends on the Clara Deploy DICOM Adapter to receive DICOM images, specifically\nsingle channel 3D MRIs (T1c). Once the DICOM instances are stored, the pipeline is\nstarted to first convert the DICOM instances to a volume image in\n[MetaIO format](https://itk.org/Wiki/ITK/MetaIO/Documentation) and then process the input\nvolume image with the Brain Tumor Segmentation AI model. The AI model generates the labeled\ntumor cores within the brain as a binary mask on slices of the volume at the same size as\nthe input. The brain tumor core is labeled as 1, and the background is labeled as 0.\n\nIn its final steps, the pipeline outputs the binary mask as a new DICOM series using the original\nstudy instance UID; also, it outputs the original and segmented volumes to the Clara Deploy\nRender Server for visualization on the Clara Dashboard.\n\n## Pipeline Definition\n\nThe Brain Tumor Segmentation pipeline is defined in the Clara Deploy pipeline definition language.\nThis pipeline utilizes built-in reference containers to construct the following set of operators:\n\n- The dicom-reader operator converts input DICOM data into volume images in MHD format.\n- The brain-tumor-segmentation operator performs AI inference against the NVIDIA TensorRT Inference\nserver to generate brain- and tumor-segmentation volume images.\n- The dicom-writer converts the segmented volume image into DICOM instances, each with a new series\ninstance UID and the original DICOM-study instance UID.\n- The register-dicom-output operator registers the DICOM instances with the Clara Deploy DICOM\nAdapter, which in turn stores each instance on external DICOM devices per the configuration.\n- The register-volume-images-for-rendering operator registers original and segmented volume images\nwith the Clara Deploy Render Server for visualization.\n\nThe following are details of the pipeline definition, with comments describing how each operator\nfunctions, as well as their input and output.\n\n```yaml\napi-version: 0.4.0\nname: brain-tumor-pipeline\noperators:\n# dicom reader operator\n# Input: '/input' mapped directly to the input of the pipeline, which is populated by the DICOM Adaptor.\n# Output:'/output' for saving converted volume image in MHD format to file whose name\n# is the same as the DICOM series instance ID.\n- name: dicom-reader\n description: Converts DICOM instances into MHD, one file per DICOM series.\n container:\n image: clara/dicom-reader\n tag: 0.7.2-2010.1\n input:\n - path: /input\n output:\n - path: /output\n# brain-tumor-segmentation operator\n# Input: `/input` containing volume image data, MHD format, with a single volume.\n# Output: `/output` containing segmented volume image, MHD format.\n# `/publish` containing original and segmented volume images, MHD format,\n# along with rendering configuration file.\n- name: brain-tumor-segmentation\n description: Segmentation of brain tumor core inferencing using DL trained model.\n container:\n image: clara/ai-braintumor\n tag: latest\n requests:\n gpu: 1\n input:\n - from: dicom-reader\n path: /input\n output:\n - path: /output\n name: segmentation\n - path: /publish\n name: rendering\n services:\n - name: trtis\n # TensorRT Inference Server, required by this AI application.\n container:\n image: nvcr.io/nvidia/tensorrtserver\n tag: 19.08-py3\n command: [\"trtserver\", \"--model-store=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models\"]\n # services::connections defines how the TRTIS service is expected to\n # be accessed. Clara Platform supports network (\"http\") and\n # volume (\"file\") connections.\n connections:\n http:\n # The name of the connection is used to populate an environment\n # variable inside the operator's container during execution.\n # This AI application inside the container needs to read this variable to\n # know the IP and port of TRTIS in order to connect to the service.\n - name: NVIDIA_CLARA_TRTISURI\n port: 8000\n # Some services need a specialized or minimal set of hardware. In this case\n # NVIDIA Tensor RT Inference Server [TRTIS] requires at least one GPU to function.\n# dicom writer operator\n# Input1: `/input` containing a volume image file, in MHD format, name matching the DICOM series instance UID.\n# Input2: `/dicom` containing original DICOM instances, i.e. dcm file.\n# Output: `/output` containing the DICOM instances converted from the volume image, with updated attributes\n# based on original DICOM instances.\n- name: dicom-writer\n description: Converts MHD into DICOM instance with attributes based on the original instances.\n container:\n image: clara/dicom-writer\n tag: latest\n input:\n - from: brain-tumor-segmentation\n name: segmentation\n path: /input\n - path: /dicom\n output:\n - path: /output\n name: dicom\n# register-volume-images-for-rendering operator\n# Input: Published original and segmented volume images, MHD format, along with rendering configuration file\n# from brain-tumor-segmentation operator.\n# Output: N/A. Input data will be sent to the destination, namely `renderserver` for Render Server DataSet Service.\n- name: register-volume-images-for-rendering\n description: Register volume images, MHD format, for rendering.\n container:\n image: clara/register-results\n tag: latest\n command: [\"python\", \"register.py\", \"--agent\", \"renderserver\"]\n input:\n - from: brain-tumor-segmentation\n name: rendering\n path: /input\n# register-dicom-output operator\n# Input: `/input` containing DICOM instances in the named output, `dicom` from dicom-writer operator.\n# Output: N/A. Input data will be sent to the destinations, namely DICOM devices, by the Clara DICOM SCU agent.\n- name: register-dicom-output\n description: Register converted DICOM instances with Results Service to be sent to external DICOM devices.\n container:\n image: clara/register-results\n tag: latest\n command: [\"python\", \"register.py\", \"--agent\", \"ClaraSCU\", \"--data\", \"[\\\"MYPACS\\\"]\"]\n input:\n - from: dicom-writer\n name: dicom\n path: /input\n\n```\n\n## Executing the Pipeline\n\nPlease refer to the `Quick Start Guide` on how to register a pipeline, configure the DICOM Adapter,\nand execute the pipeline.\n\n## License\n\nAn [End User License Agreement](https://developer.nvidia.com/nvidia-clara-sdk-license)\nis included with the product. By pulling and using the Clara Deploy asset on NGC, you accept the\nterms and conditions of these licenses.\n\n## Suggested Reading\n\nRelease Notes, the Getting Started Guide, and the SDK itself are available at the [NVIDIA Developer forum](https://developer.nvidia.com/clara).\n\nFor answers to any questions you may have about this release, visit the [NVIDIA Devtalk forum](https://devtalk.nvidia.com/default/board/362/clara-sdk/).","UPLOAD_COMPLETE","releaseType","createdDate",["D",1604686164666],"updatedDate","totalSizeInBytes",22023723,"totalFileCount","isSigned","digest","malwareScanStatus","isScanned","releaseNotes","# 0.7.2-2010.1\n\nInitial release.","hasReleaseNotes","setup","1. CD into a directory where pipelines will be stored, as shown in the following example:\n\n```bash\nmkdir -p ~/.clara/pipelines\ncd ~/.clara/pipelines\n```\n\n2. Use `clara pull pipeline` to download the pipeline definition:\n\n```bash\nclara pull pipeline clara_ai_brain_tumor_pipeline\n```\n\n3. Unzip the test data:\n\n```bash\nunzip app_braintumor-input_v1.zip\n```\n\n4. Unzip and install the model:\n\n```bash\nsudo unzip app_braintumor-model_v1.zip -d /clara/common/models/\n```\n5. Install the pipeline:\n```bash\nclara create pipeline -p brain-tumor-pipeline.yaml\n```\n\n6. Verify that the pipeline is correctly stored:\n```bash\nclara list pipelines\n```\n\n7. Verify that the stored pipeline is correct by examining the output of the following command:\n```bash\nclara describe pipeline -p \n```","hasSetup","quickStartGuide","# How to Run a Reference Pipeline\n\nClara Deploy SDK comes bundled with a set of reference pipelines. This section provides general\nguidelines for executing them on your local development machine. For information specific to a\nreference pipeline, please view the documentation for that pipeline.\n\n## Download the Reference Pipeline\n\nRefer to `Setup` section for downloading the pipeline.\n\n## Run Reference Pipelines by Sending DICOM Instances Over the Network\n\nMany reference pipelines are triggered by the Clara DICOM Adaptor after it has received DICOM\ninstances from an external sender to one of its mapped AE Titles.\n\nThis section explains the process with an example Brain Tumor Segmentation Pipeline.\n\n### External DICOM Sender and DICOM Receiver\n\nYou need an external DICOM Service Class User (SCU) application to send images to\nthe Clara DICOM Adapter (acting as a DICOM SCP). Similarly, when your pipeline\nfinishes executing, you may want to send the output to an external DICOM receiver.\n\nFor this example, we will use the open-source DICOM toolkit\n[DCMTK](https://dicom.offis.de/dcmtk.php.en) to provide the *external* DICOM sender and\nDICOM receiver functionality.\n\n#### Install DCMTK\n\nInstall DCMTK utilities with the following command:\n\n```bash\nsudo apt-get install dcmtk\n```\n\n#### Setup an External DICOM Receiver\n\nYou can use the storescp utility as an external DICOM receiver. Open a terminal\nand run the following commands to set up a DICOM Receiver. This will create a directory on\nyour machine where DICOM images produced by Clara Deploy SDK will be stored. We\nwill refer to that directory as the \"dicom-destination-directory\".\n\n```bash\nmkdir dicom-destination-directory\ncd dicom-destination-directory\nsudo storescp -v --fork -aet MYPACS 1004\n```\n\n- `MYPACS`: The Application Entity (AE) title of the external DICOM Receiver\n\n- `1004`: The port number where `storescp` will listen for any incoming DICOM traffic. Note that\nthe IP address will be the same as that of the local system.\n\n#### Setup an External DICOM Sender\n\nYou can use the storescu utility from DCMTK as a DICOM sender.\n\n### Customize the Pipeline\n\nBefore publishing a pipeline, you can modify the default settings in the pipeline definition YAML\nto configure the resource request and orchestration mode. We will use the\n`brain-tumor-pipeline.yaml` to show the steps.\n\n#### Customize GPU request\n\nIf multiple GPUs are available, the number of GPUs requested for an operator and/or pipeline\ndepedent service (e.g. TensorRT Inference Server) can be customized.\n\nThe GPU request for TensorRT Inference Server is set to 1 by default, as shown below:\n```YAML\n- name: trtis\n # TensorRT Inference Server, required by this AI application.\n container:\n image: nvcr.io/nvidia/tensorrtserver\n tag: 19.08-py3\n command: [\"trtserver\", \"--model-store=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models\"]\n requests:\n gpu: 1\n```\n\nWith the default setting, the TensorRT Inference Server can only access one GPU. When more GPUs are\navailable and intended to increase inference performance, the GPU request number may be customized\nto match the desired number (e.g. 4), as shown below:\n```YAML\n- name: trtis\n # TensorRT Inference Server, required by this AI application.\n container:\n image: nvcr.io/nvidia/tensorrtserver\n tag: 19.08-py3\n command: [\"trtserver\", \"--model-store=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models\"]\n requests:\n gpu: 4\n```\n\n**Note**: If another pipeline with a lesser number of GPUs for the TensorRT Inference Server\nhas been published and triggered, the changes made here will not affect that instance of\nTensorRT Inference Server.\n\n#### Customize the Orchestration Mode\n\nIn this release of the Clara Deploy Platform, Clara Pipeline Driver orchestration is supported,\nin addition to the Argo-based orchestration mode.\n\nThis mode can be customized with the `api-version` attribute in the pipeline definition YAML file:\nSetting the `api-version` to `0.3.0` causes the pipeline to run in Argo mode, whereas setting it\nto `0.4.0` enables the Clara Pipeline Driver mode. The following example from the \n`brain-tumor-pipeline.yaml` specifies Argo mode:\n\n```YAML\napi-version: 0.4.0\nname: brain-tumor-pipeline\n```\n\n### Create the Pipeline\n\nPlease refer to the `Setup` section to create this Pipeline.\n\n**Tip**: Note the pipeline ID as it will be used in a later section to define a new pipeline mapping\nin the DICOM adapter.\n\n### Configure the Clara DICOM Adapter\n\nWhen you send images to the Clara Deploy SDK, the DICOM Adapter component acts as the internal DICOM\nService Class Provider or a receiving agent.\n\nPlease refer to [Clara DICOM Adapter](https://docs.nvidia.com/clara/deploy/sdk/Services/DicomAdapter/public/docs/Setup.html) for setup instructions.\n\n### Trigger the Pipeline\n\nA pipeline is triggered when a DICOM series is sent to the AE title of\nthe Clara DICOM Adapter associated with that pipeline. To trigger this\npipeline, you will need a set of relevant DICOM images that can be used\nas source input data.\n\nFor this pipeline, a sample test dataset in DICOM format is available within the package in `/dcm`.\n\n1. Open a terminal and go to the directory that contains all your DICOM images.\n\n2. Trigger the pipeline by pushing DICOM data to the proper AE title:\n\n```bash\nstorescu -v +sd +r -xb -aet \"DCM4CHEE\" -aec \"BrainSeg\" AA.BBB.CC.DD 104 ./\n```\n\nReplace \"AA.BBB.CC.DD\" with the IP of your local machine.\n\nWhen the above step is successfully completed, you should see the following result in the terminal:\n\n```bash\nI: Received Store Response (Success)
\nI: Releasing Association\n```\n\n### Check Job Status and Download/View Payloads in Clara Console/Dashboard\n\n**With Clara Orchestrator (API version >= 0.4.0)**\n\n* Go to the Clara Management Console using a web browser:\nThe URL is: `:32002`\n* Go to the `JOBS` view. You should see a job with a name that includes the name of the job you specified\n* You can inspect the status of each operator inside that job\n\nPlease check [Clara Management Console](https://docs.nvidia.com/clara/deploy/sdk/clara-ux/public/docs/README.html) to see the web application in detail.\n\n**With Argo Orchestrator (API version <= 0.3.0)**\n\nGo to the Clara dashboard UI using a web browser: The URL is `:8000`.\n\n* You should see a job with a name that includes the name of the pipeline you created.\n* You can inspect the status of each operator inside that job\n\n### Verify That the External DICOM Receiver Received Your Images\n\nIf your pipeline outputs DICOM images and sends them to an external DICOM\nreceiver, you may want to verify that the images were indeed received. If you\nused the `storescp` utility from DCMTK as mentioned earlier, use a terminal\nto navigate to the \"dicom-destination-directory\" and verify whether the expected\nimages are stored there. Also, you may want to use a third-party DICOM\nviewer to view those images.","hasQuickStartGuide","advanced","","hasAdvanced","performance","hasPerformance","policies",[],{"_78":242,"_80":243,"_191":-5,"_198":31,"_201":31,"_216":241,"_204":103},{"_78":240,"_80":240,"_191":-5,"_198":31,"_201":31,"_216":241,"_204":103},{"_78":238,"_80":238,"_191":-5,"_198":31,"_201":31,"_216":239,"_204":103},{"_78":236,"_80":236,"_191":-5,"_198":31,"_201":31,"_216":237,"_204":103},{"_78":22,"_80":22,"_191":-5,"_198":31,"_201":31,"_216":235,"_204":103},{"_78":233,"_80":233,"_191":-5,"_198":31,"_201":31,"_216":234,"_204":103},{"_78":231,"_80":231,"_191":-5,"_198":31,"_201":31,"_216":232,"_204":103},{"_78":229,"_80":229,"_191":-5,"_198":31,"_201":31,"_216":230,"_204":103},{"_78":227,"_80":227,"_191":-5,"_198":31,"_201":31,"_216":228,"_204":103},"0.5.0-2003.12",[],"0.5.0-2004.7",[],"0.6.0-2006.4",[],"0.7.1-2008.1",[],[],"0.7.3-2011.5",[],"0.7.4-2103.2",[],"0.8.1-2108.1",[],"-","Latest (0.8.1-2108.1)","nvidia/clara/clara_ai_brain_tumor_pipeline","key","nvidia/clara/resources/clara_ai_brain_tumor_pipeline","resourceType","resources","resourceId","clara_ai_brain_tumor_pipeline","shortDescription","Clara Deploy AI Brain Tumor Pipeline","# Clara Deploy SDK is being consolidated into Clara Holoscan SDK\n\t\nMore info https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/collections/claradeploy\n\t\n\n\n# Clara AI Brain Tumor Segmentation Pipeline\n\nThis asset requires the Clara Deploy SDK. Follow the instructions on the\n[Clara Ansible](https://ngc.nvidia.com/catalog/model-scripts/nvidia:clara:clara_ansible) page\nto install the Clara Deploy SDK.\n\n## Overview\n\nThe Brain Tumor Segmentation pipeline is one of the reference pipelines provided with Clara Deploy SDK.\nA pre-trained model for volumetric (3D) brain tumor segmentation (only tumor core (TC) from T1c images) is used in the pipeline.\nThis pipeline depends on the Clara Deploy DICOM Adapter to receive DICOM images, specifically\nsingle channel 3D MRIs (T1c). Once the DICOM instances are stored, the pipeline is\nstarted to first convert the DICOM instances to a volume image in\n[MetaIO format](https://itk.org/Wiki/ITK/MetaIO/Documentation) and then process the input\nvolume image with the Brain Tumor Segmentation AI model. The AI model generates the labeled\ntumor cores within the brain as a binary mask on slices of the volume at the same size as\nthe input. The brain tumor core is labeled as 1, and the background is labeled as 0.\n\nIn its final steps, the pipeline outputs the binary mask as a new DICOM series using the original\nstudy instance UID; also, it outputs the original and segmented volumes to the Clara Deploy\nRender Server for visualization on the Clara Dashboard.\n\n## Pipeline Definition\n\nThe Brain Tumor Segmentation pipeline is defined in the Clara Deploy pipeline definition language.\nThis pipeline utilizes built-in reference containers to construct the following set of operators:\n\n- The dicom-reader operator converts input DICOM data into volume images in MHD format.\n- The brain-tumor-segmentation operator performs AI inference against the NVIDIA Triton Inference\nserver to generate brain- and tumor-segmentation volume images.\n- The dicom-writer converts the segmented volume image into DICOM instances, each with a new series\ninstance UID and the original DICOM-study instance UID.\n- The register-dicom-output operator registers the DICOM instances with the Clara Deploy DICOM\nAdapter, which in turn stores each instance on external DICOM devices per the configuration.\n- The register-volume-images-for-rendering operator registers original and segmented volume images\nwith the Clara Deploy Render Server for visualization.\n\nThe following are details of the pipeline definition, with comments describing how each operator\nfunctions, as well as their input and output.\n\n```yaml\napi-version: 0.4.0\nname: brain-tumor-pipeline\noperators:\n# dicom reader operator\n# Input: '/input' mapped directly to the input of the pipeline, which is populated by the DICOM Adaptor.\n# Output:'/output' for saving converted volume image in MHD format to file whose name\n# is the same as the DICOM series instance ID.\n- name: dicom-reader\n description: Converts DICOM instances into MHD, one file per DICOM series.\n container:\n image: clara/dicom-reader\n tag: latest\n input:\n - path: /input\n output:\n - path: /output\n# brain-tumor-segmentation operator\n# Input: `/input` containing volume image data, MHD format, with a single volume.\n# Output: `/output` containing segmented volume image, MHD format.\n# `/publish` containing original and segmented volume images, MHD format,\n# along with rendering configuration file.\n- name: brain-tumor-segmentation\n description: Segmentation of brain tumor core inferencing using DL trained model.\n container:\n image: clara/ai-braintumor\n tag: latest\n requests:\n memory: 8192\n input:\n - from: dicom-reader\n path: /input\n output:\n - path: /output\n name: segmentation\n - path: /publish\n name: rendering\n models:\n # change the following line to match the name created for the model\n - name: segmentation_mri_brain_tumors_br16_t1c2tc_v1\n# dicom writer operator\n# Input1: `/input` containing a volume image file, in MHD format, name matching the DICOM series instance UID.\n# Input2: `/dicom` containing original DICOM instances, i.e. dcm file.\n# Output: `/output` containing the DICOM instances converted from the volume image, with updated attributes\n# based on original DICOM instances.\n- name: dicom-writer\n description: Converts MHD into DICOM instance with attributes based on the original instances.\n container:\n image: clara/dicom-writer\n tag: latest\n input:\n - from: brain-tumor-segmentation\n name: segmentation\n path: /input\n - path: /dicom\n output:\n - path: /output\n name: dicom\n# register-volume-images-for-rendering operator\n# Input: Published original and segmented volume images, MHD format, along with rendering configuration file\n# from brain-tumor-segmentation operator.\n# Output: N/A. Input data will be sent to the destination, namely `renderserver` for Render Server DataSet Service.\n- name: register-volume-images-for-rendering\n description: Register volume images, MHD format, for rendering.\n container:\n image: clara/register-results\n tag: latest\n command: [\"python\", \"register.py\", \"--agent\", \"renderserver\"]\n input:\n - from: brain-tumor-segmentation\n name: rendering\n path: /input\n# register-dicom-output operator\n# Input: `/input` containing DICOM instances in the named output, `dicom` from dicom-writer operator.\n# Output: N/A. Input data will be sent to the destinations, namely DICOM devices, by the Clara DICOM SCU agent.\n- name: register-dicom-output\n description: Register converted DICOM instances with Results Service to be sent to external DICOM devices.\n container:\n image: clara/register-results\n tag: latest\n command: [\"python\", \"register.py\", \"--agent\", \"ClaraSCU\", \"--data\", \"[\\\"MYPACS\\\"]\"]\n input:\n - from: dicom-writer\n name: dicom\n path: /input\n\n```\n\n## Executing the Pipeline\n\nPlease refer to the `Quick Start Guide` on how to register a pipeline, configure the DICOM Adapter,\nand execute the pipeline.\n\n## License\n\nAn [End User License Agreement](https://developer.nvidia.com/nvidia-clara-sdk-license)\nis included with the product. By pulling and using the Clara Deploy asset on NGC, you accept the\nterms and conditions of these licenses.\n\n## Suggested Reading\n\nRelease Notes, the Getting Started Guide, and the SDK itself are available at the [NVIDIA Developer forum](https://developer.nvidia.com/clara).\n\nFor answers to any questions you may have about this release, visit the [NVIDIA Devtalk forum](https://devtalk.nvidia.com/default/board/362/clara-sdk/).","displayName","Clara Deploy AI Brain Tumor Pipeline [Deprecated]",["D",1680632223134],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/clara/resources/clara_ai_brain_tumor_pipeline","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Clara.png","canGuestDownload","latestVersionSizeInBytes",22026984,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[288,289,290,291,292],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_254":312,"_294":312,"_295":313,"_297":298},{"_254":309,"_294":310,"_295":311,"_297":298},{"_254":305,"_294":306,"_295":307,"_297":298},{"_254":301,"_294":302,"_295":303,"_297":298},{"_254":293,"_294":293,"_295":296,"_297":298},"TensorFlow","value","filter",{"_299":300,"_294":293},"category","label","field","framework","Inference","soln_inference",{"_299":304,"_294":302},"technology","Healthcare","indus_healthcare",{"_299":308,"_294":306},"industry","DL","soln_dl",{"_299":304,"_294":310},"Clara Deploy",{"_299":298,"_294":312},{"_124":1448,"_126":1449},"ngcApps",[1401],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1382":1383,"_1384":1385,"_1386":1387,"_1388":1389,"_1390":325,"_156":351,"_1391":1392,"_1393":56,"_1394":103,"_1395":103,"_1396":1397,"_1398":103,"_1399":1400},"amplitude",{"_1379":1380,"_986":-7,"_1381":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1373":1374,"_1375":1376,"_1377":1378},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1793189367587,"1.29.0","defaultSearchSuggestions",{"_1256":212,"_1257":1258,"_1259":1260,"_1261":1262},"announcements",[],"consentState",{"_389":1246,"_1247":31,"_1248":31,"_1249":103,"_1250":1251},"consentConfig",{"_371":372,"_373":374,"_375":376,"_377":378,"_379":380,"_381":382,"_383":384,"_385":386,"_387":388,"_389":390},"showContactAdmin",1793189367589,[],[],[365,366,367,368,369,370],{"_45":59,"_47":69,"_49":119,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":119,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":66,"_49":119,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":119,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":119,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":119,"_51":-5,"_53":-5,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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Name	Size	Updated	Actions
app_braintumor-input_v1.zip	4.33 MB	November 6, 2020 UTC	curl wget Download
app_braintumor-model_v1.zip	16.65 MB	November 6, 2020 UTC	curl wget Download
brain-tumor-pipeline.yaml	4.14 KB	November 6, 2020 UTC	curl wget Download
source.zip	12.25 KB	November 6, 2020 UTC	curl wget Download