Program Information
BEST IN PHYSICS (JOINT IMAGING-THERAPY)- Semi-Automated Probabilistic Segmentation of Head and Neck Anatomy Through Structure Specific Feature Selection From Multi-Sequence MRI
H Veeraraghavan1*, M Folkert2, J Deasy3, M Traughber4, (1) ,,,(2) Memorial Sloan Kettering Cancer Center, New York, NY, (3) Memorial Sloan Kettering Cancer Center, New York, NY, (4) Philips Healthcare, Cleveland, Ohio
TH-C-WAB-1 Thursday 10:30AM - 12:30PM Room: Wabash BallroomPurpose:
To develop semi-automatic methods for robust segmentation of head and neck anatomy through structure-specific image features selection from multiple MRI sequences.
Methods:
We developed a semi-automatic approach for probabilistic segmentation of head and neck anatomy that combines several candidate segmentations using random forests and structure-specific image features selection from multiple MRI sequences. Starting from user-drawn line segments on a single axial image, in each tree, structure segmentations are generated from composite feature images computed at the tree split nodes. Composite features are computed through random combinations of the MRI sequences and image features, including texture and Laplacian of Gaussian gradients. We used seven different MRI pulse sequences obtained from a Phillips scanner, including Dixon water only T1, Dixon fat only T1, Dixon in-phase, Dixon opposed-phase, T1-weighted, T2-weighted and diffusion weighted whole body imaging with background body signal suppression (DWIBS). The highest quality anatomical structure segmentation in a tree is extracted from all available split node segmentations with segmentation quality measured using within-segment statistics including kurtosis, edge quality, and inter-segment dissimilarity. The joint segmentation with probabilistic score is obtained by combining all the generated tree segmentations.
Results:
We used our approach to segment anatomical structures in head and neck images using the seven MRI sequences listed above. The algorithm returned the segmentations, their probabilistic scores, and the highest quality feature images for each structure. We evaluated our approach by computing DICE overlap scores with manually segmented ground truths and obtained an overall segmentation accuracy of 80% for the various radiotherapeutically-relevant anatomic structures.
Conclusion:
We developed a semi-automatic, random forest-based probabilistic segmentation of head and neck anatomy by combining multiple MRI pulse sequences. Our approach achieves accurate segmentation for various radiotherapeutically-relevant anatomic structures. Additionally, it extracts the highest quality composite feature images for obtaining optimal segmentation for the same structures.
Funding Support, Disclosures, and Conflict of Interest: Phillips
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