MO-FG-303-07: Quality Assurance of OAR Segmentation Using Machine Learning And Statistical Shape Models

Authors


Abstract

Purpose:

Automating the quality assurance of OAR segmentations is technically a challenging task due to the natural anatomical variations that cannot be easily quantified by computer algorithms. We report on QA method for OAR segmentations that uses recent advances in machine learning to automate the currently-used manual interpretation of segmentation quality.

Materials and Methods:

The approach relies on building a statistical model of an organ's shapes and variation across patients from a set of high-quality contours representative for the anatomy. When presented with a new OAR segmentation for evaluation, the QA procedure fits the shape to the statistical model to mark outlier contours and suggest a correction for their location based on the statistics inferred from the training datasets. Technically, the statistical model of OAR variation is build using a probabilistic PCA model where deformations between learning shapes are modeled as a Gaussian process to create a general non-parametric model that is flexible in capturing inter-patient variations.

Results:

The algorithm was tested in clinical practice by creating custom software that intercepts DICOM RT segmentation files send to the treatment planning system, verifies them against the statistical model, and presents to original and corrected segmentations side-by-side in the treatment planning for evaluating changes suggested by the QA procedure. Segmentation quality is quantified through the Dice and Hausdorff distance between the initial and corrected shape.

Conclusion:

Abnormalities in critical structures delineation can be quantified by a shape model derived from a database of verified segmentation to create a software tool that detects and quantifies point-by point variations from a normal segmentation. The approach was used to verify OAR segmentation and streamline clinical practice.

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