Purpose: Clinical validation and quantitative evaluation of computed tomography (CT) image autosegmentation using Smart Probabilistic Image Contouring Engine (SPICE). Methods and Materials: CT images of 125 treated patients (32 head and neck [HN], 40 thorax, 23 liver, and 30 prostate) in 7 independent institutions were autosegmented using SPICE and computational times were recorded. The number of structures autocontoured were 25 for the HN, 7 for the thorax, 3 for the liver, and 6 for the male pelvis regions. Using the clinical contours as reference, autocontours of 22 selected structures were quantitatively evaluated using Dice Similarity Coefficient (DSC) and Mean Slice-wise Hausdorff Distance (MSHD). All 40 autocontours were evaluated by a radiation oncologist from the institution that treated the patients. Results: The mean computational times to autosegment all the structures using SPICE were 3.1 to 11.1 minutes per patient. For the HN region, the mean DSC was >0.70 for all evaluated structures, and the MSHD ranged from 3.2 to 10.0 mm. For the thorax region, the mean DSC was 0.95 for the lungs and 0.90 for the heart, and the MSHD ranged from 2.8 to 12.8 mm. For the liver region, the mean DSC was >0.92 for all structures, and the MSHD ranged from 5.2 to 15.9 mm. For the male pelvis region, the mean DSC was >0.76 for all structures, and the MSHD ranged from 4.8 to 10.5 mm. Out of the 40 autocontoured structures reviews by experts, 25 were scored useful as autocontoured or with minor edits for at least 90% of the patients and 33 were scored useful autocontoured or with minor edits for at least 80% of the patients. Conclusions: Compared with manual contouring, autosegmentation using SPICE for the HN, thorax, liver, and male pelvis regions is efficient and shows significant promise for clinical utility.
|Original language||English (US)|
|Number of pages||8|
|Journal||International Journal of Radiation Oncology Biology Physics|
|Publication status||Published - Nov 15 2013|
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Cancer Research