SU-E-J-234: Auto-Segmentation Strategies for Treatment Targets and Critical Organs in Head-And-Neck Cancer Patients

Authors


Abstract

Purpose:

To determine an optimum combination of model- and atlas-based segmentation methods for the automatic segmentation of targets and critical organs for head-and-neck cancer patients.

Methods:

Ten base-of-tongue cancer patients with clinical staging ranging from T1N2bM0 to T2N2cM0 were selected retrospectively for this study. Patient CT images were imported into RayStation TPS (V4.0) where both model-based segmentation (MBS) and atlas-based segmentation (ABS) are available. MBS includes models with adjustable shape, size and property parameters provided by RayStation for the critical organs of spinal cord, brainstem, parotids, and mandible. ABS requires user-defined atlases with images and contours. To create atlases specifically for our patient cases, “standard” patients were selected by physicians for each TMN stage. The ABS contours include all MBS contours plus the primary and nodal GTV and up to three levels of CTV. Contours for each patient were auto-delineated using MBS and ABS methods which were then modified by physicians and used as ground truth. Auto-segmented and physician corrected contours were compared and evaluated using Dice similarity coefficient.

Results:

The auto-segmented contours were considered acceptable if the Dice values were above 0.7. For the cord, brainstem, parotids(L+R), and mandible, the number of patients whose Dice value was acceptable were 0, 4, 5, 7, and 10, respectively for MBS method, and 4, 4, 5, 4, and 7, respectively for ABS method. For the targets, none of the auto-segmented contour had an accpetable Dice value. The best one was CTV2 with the average Dice of 0.5.

Conclusion:

With the exception of cord, MBS was better for delineation critical organs. Auto-segmented GTV and CTV contours using ABS method agreed poorly with physician corrected contours because of the large amount of inter-patient variation in disease location even with the exact same TMN staging. Better auto-segmentation methods should be explored to improve target delineation.

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