Version 0.2.0 (November 23, 2021)¶
This is a new and enhanced version of MONAI Deploy App SDK, just in time for Thanksgiving and RSNA 2021!🎉
Please visit GETTING STARTED guide and follow tutorials.
You can learn more about SDK usage through DEVELOPING WITH SDK.
Please let us know how you like it and what could be improved by submitting an issue or asking questions😀
What’s new in this version 0.2.0¶
Series Selection Operator¶
This is to support the use case where whole DICOM studies are used as input to an AI inference application even though only specific series are applicable.
The selection rules are defined in JSON, allowing multiple selections, each with a set of matching conditions. The rules processing engine is implemented in the
DICOMSeriesSelectorOperator, which itself is regarded as a base class with a default implementation. More advanced rules and processing engines can be implemented in the derived classes.
Multiple instances of the series selection operators, each having its own rules, can be chained in a MONAI Deploy application. In part this is made possible by the new App SDK Domain classes which encapsulate the selected series in a DICOM study, and are used as the output of each series selection operator.
DICOM Comprehensive Structured Report Writer¶
This is introduced to support generating DICOM SR SOP instances for AI classification results, and as such, the DICOM SR writer is limited to supporting textual results only.
The DICOM SR writer is implemented in
loads the AI result from a in-memory object as well as from a file path, with the in-memory object taking precedence
copies applicable DICOM tags from the original DICOM series used as input for the inference application, as well as generating tags anew when there is no DICOM series provided.
supports assigning DICOM tags via a dictionary with DICOM keywords and their respective values, so that an application can customize the tags in the DICOM SR instance
provides classes for an application to encapsulate the AI model information as well as DICOM equipment information, per IHE Radiology Technical Framework Supplement AI Results (AIR)
Updated example applications¶
The AI Spleen Segmentation application updated to demonstrate the use of series selection rules
The MedNIST Classifier application updated to demonstrate the use of DCIOM SR writing (without initial DICOM input)
Updated are the main functions of the built-in operators, which serve as examples on how to parse the output objects
Updated also are Jupyter notebook tutorials
Multiple issues were fixed and closed