Library of deep-learning image segmentation and outcomes model-implementations

Abstract

An open-source library of implementations for deep-learning-based image segmentation and outcomes models based on radiotherapy and radiomics is presented. As oncology treatment planning becomes increasingly driven by automation, such a library of model implementations is crucial to (i) validate existing models on datasets collected at different institutions, (ii) automate segmentation, (iii) create ensembles for improving performance and (iv) incorporate validated models in the clinical workflow. Inclusion of deep-learning-based image segmentation and outcomes models in the same library provides a fully automated and reproduceable pipeline to estimate prognosis. The library was developed with the Computational Environment for Radiological Research (CERR) software platform. Centralizing model implementations in CERR builds upon its rich set of radiotherapy and radiomics tools and caters to the world-wide user base. CERR provides well-validated feature extraction pipelines for radiotherapy dosimetry and radiomics with fine control over the calculation settings, allowing users to select appropriate parameters used in model derivation. Models for automatic image segmentation are distributed via containers, allowing them to be deployed with a variety of scientific computing architectures. The library includes implementations of popular DVH-based models outlined in the Quantitative Analysis of Normal Tissue Effects in the Clinic effort and recently published literature. Radiomics models include features from the Image Biomarker Standardization Initiative and application-specific features found to be relevant across multiple sites and image modalities. The library is distributed as a module within CERR at https://www.github.com/cerr/CERR under the GNU-GPL copyleft with additional restrictions on clinical and commercial use and provision to dual license in future.

Keywords: Deep-learning; Image segmentation; Library; Model implementations; Normal tissue complication; Radiomics; Radiotherapy outcomes; Tumor control.

Figure 1:

Schematic of invoking the container for deep-learning image segmentation model from CERR. CERR passes images in HDF5 format to the container and displays the resulting segmentation.

Figure 2:

Options to select region of interest as an input to segmentation model. (a) cropping to structure bounds and by a fixed amount. (b) cropping to the shoulder.

Figure 3:

Graphical User Interface to invoke deep learning segmentation models from CERR Viewer. In this example, the union of left and right lungs is used as a prior to select the region of interest.

Figure 4:

(a) JSON files to define models. (b) Parameters for a model are defined in the corresponding JSON file.

Figure 5:

Radiotherapy Outcomes Explorer (ROE). Graphical user interface allows the selection of protocols which includes models and constraints. Additionally, users can input risk factors for the patient. NTCP curves for esophagitis and pneumonitis models and TCP curve for BED are shown in this example along with the points on the NTCP curves where the clinical limits are reached.

Figure 6:

Schematic of EVA pipeline. Image transfer to MIM software triggers segmentation on the processing server and the resulting segmentation gets archived in MIM.