Multi-Atlas Fusion Using a Tissue Appearance Model
J Yang1*, A Garden1, Y Zhang1, L Zhang1, L Court1, L Dong1,2, (1) UT MD Anderson Cancer Center, Houston, TX, (2) Scripps Proton Therapy Center, SAN DIEGO, CAWE-E-213CD-9 Wednesday 2:00:00 PM - 3:50:00 PM Room: 213CD
To improve multi-atlas based auto-segmentation by integrating a tissue appearance model with the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm to perform multi-atlas fusion.
Ten head-and-neck planning CT images were acquired (resolution: 1.0x1.0x2.5mm³) and the parotid glands were contoured manually by a head-and-neck oncologist. We performed 10 leave-one-out tests by using one patient as test patient and the rest of 9 patients as atlases. Deformable registration was first applied to transform the atlas parotid contours to the test image one by one. The STAPLE algorithm was initialized by a parotid tissue appearance model, which was estimated from the test image and encoded the intensity information of parotid glands. The individual deformed contours were then fused using the STAPLE algorithm to produce a best approximation of the true contour. The tissue appearance model was also applied to a deformable model segmentation to further refine the fused contours.
The multi-atlas fusion using the tissue appearance model produced an average Dice coefficient of 85.2%±3.1% (left parotid) and 84.9%±3.9% (right parotid) over the 10 tests between the auto-contour and the manual contour, and an average mean surface distance of 1.6±0.3mm and 1.6±0.4mm for left and right parotids respectively. This demonstrated a good agreement between the manual contours and the auto-delineated contours. Our results also showed that, without using the tissue appearance model, the auto-delineated parotid contours might include nearby bony structures; however, using the appearance model was able to correct this problem.
Including the intensity information using a tissue appearance model into STAPLE algorithm for multi-atlas fusion showed improvement in refining the anatomical boundaries in the multi-atlas based auto-segmentation.