LAUSR

PURPOSE To reduce the likelihood of errors in organ delineations used for radiotherapy treatment planning, a knowledge-based quality control (KBQC) system which discriminates between valid and anomalous delineations is developed. METHODS AND MATERIALS The KBQC is comprised of a group-wise inference system and anomaly detection modules trained using historical priors from 296 locally advanced lung and prostate cancer patient CTs. The inference system discriminates different organs based on shape, relational, and intensity features. For a given delineated image set, the inference system solves a combinatorial optimization problem that results in an organ group whose relational features follows those of the training set considering the posterior probabilities obtained from support vector machine (SVM), discriminant subspace ensemble (DSE), and artificial neural network (ANN) classifiers. These classifiers are trained on non-relational features with a ten-fold cross-validation scheme. The anomaly detection module is a bank of ANN auto-encoders, each corresponding with an organ, trained on non-relational features. A heuristic rule detects anomalous organs that exceed predefined organ-specific tolerances for the feature reconstruction error and the classifier's posterior probabilities. Independent data sets with anomalous delineations were used to test the overall performance of the KBQC system. The anomalous delineations were manually manipulated, computer-generated, or propagated based on a transformation obtained by imperfect registrations. Both peer-review-based scoring system and shape similarity coefficient (DCS) were used to label ROIs as normal or anomalous in two independent test cohorts. RESULTS The accuracy of the classifiers was ≥99.8%, and the minimum per-class F1-scores were 0.99, 0.99, and 0.98 for SVM, DSE, and ANN respectively. The group-wise inference system reduced the miss-classification likelihood for the test data set with anomalous delineations compared to each individual classifier and a fused classifier that used the average posterior probability of all classifiers. For 15 independent locally advanced lung patients, the system detected >79% of the anomalous ROIs. For 1320 auto-segmented abdominopelvic organs, the anomaly detection system identified anomalous delineations which also had low Dice similarity coefficient values with respect to manually delineated organs in the training dataset. CONCLUSIONS The KBQC system detected anomalous delineations with superior accuracy compared to classification methods that judge only based on posterior probabilities. This article is protected by copyright. All rights reserved.