Three-dimensional assessment of MR imaging-guided percutaneous cryotherapy using multi-performer repeated segmentations: the value of supervised learning

Date Published:

2005 Apr


RATIONALE AND OBJECTIVES: Accurate and reproducible segmentations of two-dimensional images are an important prerequisite for assessing tumor ablations three dimensionally (3D). We evaluated whether supervised learning methods would improve multiperformer repeated segmentations of magnetic resonance images (MRI) obtained before and after MRI-guided cryotherapy of renal cell carcinoma. MATERIALS AND METHODS: Three medical students independently performed five manual segmentations of a biopsy-proven renal cell carcinoma that was treated with percutaneous MRI-guided cryotherapy. Using pretreatment (T2-weighted fast recovery fast spin echo [FRFSE]) and posttreatment (T1-weighted, fat-suppressed, dynamically enhanced) MRIs, regions of tumor cryonecrosis were segmented. The same tasks were repeated after an experienced abdominal radiologist provided supervised learning. Segmentation sensitivity was compared with an estimated 3D-ground truth via voxel counts for regions of tumor, both before and after treatment, and for the regions of cryonecrosis. The sensitivity of each repeated segmentation was compared against the estimated ground truth using sensitivity, overlap index, and volume (mL). RESULTS: Supervised learning significantly improved posttreatment segmentation sensitivity (P = .03). With supervised learning, the ranges of the performance metrics over the segmentation performers were: pretreated tumor, sensitivity 0.902-0.999, overlap index 0.935-0.961, and volume 19.15-23.71 mL; posttreated tumor, sensitivity 0.923-0.991, overlap index 0.952-0.981, and volume 20.67-22.70 mL; in the ablation zone, sensitivity 0.938-0.969, overlap index 0.940-0.962, and volume 31.79-32.36 mL. CONCLUSIONS: Supervised learning improved multiperformer repeated segmentations of MRIs obtained before and after MRI-guided percutaneous cryotherapy of renal cell carcinoma. These methods may prove useful in aiding the 3D assessment of percutaneous tumor ablations.
Last updated on 01/24/2017