Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology

TL;DR

Highdicom is a Python library that simplifies encoding and decoding of DICOM-standard image annotations and model outputs, promoting interoperability in pathology and radiology ML applications.

Contribution

It provides a high-level API that abstracts DICOM complexities, enabling easier integration of ML models with clinical imaging systems.

Findings

Enables training and evaluation of ML models in pathology and radiology.

Achieves interoperability with clinical imaging systems.

Facilitates standard-compliant communication of model outputs.

Abstract

Machine learning is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but their lack of interoperability has been a major barrier for clinical integration and evaluation. The DICOM a standard specifies Information Object Definitions and Services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with data sets in DICOM format. Here we present the highdicom library, which provides a high-level application programming interface for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages. To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source.