Geometrically focused training and evaluation of organs-at-risk segmentation via deep learning.

backgroundDeep learning methods are promising in automating segmentation of organs at risk (OARs) in radiotherapy. However, the lack of a geometric indicator for dosimetry accuracy remains to be a problem. This issue is particularly pronounced in specific radiotherapy treatments where only the proximity of structures to the radiotherapy target affects the dose planning. In cervical cancer high dose-rate (HDR) brachytherapy, treatment planning is motivated by limiting dose to the hottest 2 cubic centimeters (D2cm PURPOSE: We propose a novel geometrically focused deep learning training method and evaluation metric, using cervical brachytherapy as a case study. A distance-penalized (DP) loss function was developed to focus attention on the near-to-target OAR regions. We also introduced and evaluated a novel geometric metric, weighted dice similarity coefficient (wDSC), correlated with OARs D2cm

methodsA model was trained using a 3D U-Net architecture and 170 T2-weighted magnetic resonance (MR) images (56 patients) with clinical contours. The dataset was split into subsets at the patient level: 45 patients (150 scans) as the training set for five-fold cross-validation and 11 patients (20 scans) as the testing set. Another dataset from our institution, consisting of 35 MR scans from 22 cervical cancer patients, was used as an independent internal testing set. A distance map, emphasizing errors near high-risk clinical target volume (CTV

resultsThe wDSC moderately correlated (r = -0.55) with D2cm

conclusionWe developed and evaluated a new geometric metric, wDSC, as a better indicator of D2cm