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For radiation treatment of cancer and image-based quantitative assessment of treatment response, target structures like tumors and lymph nodes need to be segmented. In current clinical practice, this is done manually, which is time consuming and error-prone. To address this issue, a semi-automated graph-based segmentation approach was developed.
It was validated with 60 real datasets, segmented by two users manually and with this new algorithm, and 44 scans of a phantom dataset. The results showed a statistically significant improvement in intra- and interoperator consistency of segmentations, a statistically significant improvement in speed of segmentation, and reasonable accuracy against consensus images and phantoms. As such, the algorithm can be applied in cases that otherwise would use manual segmentation.