More info https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/collections/claradeploy
This asset requires the Clara Deploy SDK. Follow the instructions on the Clara Ansible page to install the Clara Deploy SDK.
The Brain Tumor Segmentation pipeline is one of the reference pipelines provided with Clara Deploy SDK. A pre-trained model for volumetric (3D) brain tumor segmentation (only tumor core (TC) from T1c images) is used in the pipeline. This pipeline depends on the Clara Deploy DICOM Adapter to receive DICOM images, specifically single channel 3D MRIs (T1c). Once the DICOM instances are stored, the pipeline is started to first convert the DICOM instances to a volume image in MetaIO format and then process the input volume image with the Brain Tumor Segmentation AI model. The AI model generates the labeled tumor cores within the brain as a binary mask on slices of the volume at the same size as the 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 study instance UID; also, it outputs the original and segmented volumes to the Clara Deploy Render Server for visualization on the Clara Dashboard.
The Brain Tumor Segmentation pipeline is defined in the Clara Deploy pipeline definition language. This pipeline utilizes built-in reference containers to construct the following set of operators:
The following are details of the pipeline definition, with comments describing how each operator functions, as well as their input and output.
api-version: 0.4.0 name: brain-tumor-pipeline operators: # dicom reader operator # Input: '/input' mapped directly to the input of the pipeline, which is populated by the DICOM Adaptor. # Output:'/output' for saving converted volume image in MHD format to file whose name # is the same as the DICOM series instance ID. - name: dicom-reader description: Converts DICOM instances into MHD, one file per DICOM series. container: image: clara/dicom-reader tag: latest input: - path: /input output: - path: /output # brain-tumor-segmentation operator # Input: `/input` containing volume image data, MHD format, with a single volume. # Output: `/output` containing segmented volume image, MHD format. # `/publish` containing original and segmented volume images, MHD format, # along with rendering configuration file. - name: brain-tumor-segmentation description: Segmentation of brain tumor core inferencing using DL trained model. container: image: clara/ai-braintumor tag: latest requests: memory: 8192 input: - from: dicom-reader path: /input output: - path: /output name: segmentation - path: /publish name: rendering models: # change the following line to match the name created for the model - name: segmentation_mri_brain_tumors_br16_t1c2tc_v1 # dicom writer operator # Input1: `/input` containing a volume image file, in MHD format, name matching the DICOM series instance UID. # Input2: `/dicom` containing original DICOM instances, i.e. dcm file. # Output: `/output` containing the DICOM instances converted from the volume image, with updated attributes # based on original DICOM instances. - name: dicom-writer description: Converts MHD into DICOM instance with attributes based on the original instances. container: image: clara/dicom-writer tag: latest input: - from: brain-tumor-segmentation name: segmentation path: /input - path: /dicom output: - path: /output name: dicom # register-volume-images-for-rendering operator # Input: Published original and segmented volume images, MHD format, along with rendering configuration file # from brain-tumor-segmentation operator. # Output: N/A. Input data will be sent to the destination, namely `renderserver` for Render Server DataSet Service. - name: register-volume-images-for-rendering description: Register volume images, MHD format, for rendering. container: image: clara/register-results tag: latest command: ["python", "register.py", "--agent", "renderserver"] input: - from: brain-tumor-segmentation name: rendering path: /input # register-dicom-output operator # Input: `/input` containing DICOM instances in the named output, `dicom` from dicom-writer operator. # Output: N/A. Input data will be sent to the destinations, namely DICOM devices, by the Clara DICOM SCU agent. - name: register-dicom-output description: Register converted DICOM instances with Results Service to be sent to external DICOM devices. container: image: clara/register-results tag: latest command: ["python", "register.py", "--agent", "ClaraSCU", "--data", "[\"MYPACS\"]"] input: - from: dicom-writer name: dicom path: /input
Please refer to the
Quick Start Guide on how to register a pipeline, configure the DICOM Adapter,
and execute the pipeline.
An End User License Agreement is included with the product. By pulling and using the Clara Deploy asset on NGC, you accept the terms and conditions of these licenses.
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.