Created by W.Langdon from gp-bibliography.bib Revision:1.8010
Approach: We train machine learning (ML) models to map (2-D) patient features to 3-D organ-at-risk (OAR) metrics upon a database of 60 pediatric abdominal computed tomographies with liver and spleen segmentations. Next, we use the models in an automatic pipeline that outputs a personalized phantom given the patient's features, by assembling 3-D imaging from the database. A step to improve phantom realism (i.e., avoid OAR overlap) is included. We compare five ML algorithms, in terms of predicting OAR left-right (LR), anterior-posterior (AP), inferior-superior (IS) positions, and surface Dice-Sorensen coefficient (sDSC). Furthermore, two existing human-designed phantom construction criteria and two additional control methods are investigated for comparison.
Results: Different ML algorithms result in similar test mean absolute errors: approx 8mm for liver LR, IS, and spleen AP, IS; approx 5mm for liver AP and spleen LR; approx 80 percent for abdomen sDSC; and approx 60 percent to 65 percent for liver and spleen sDSC. One ML algorithm (GP-GOMEA) significantly performs the best for 6/9 metrics. The control methods and the human-designed criteria in particular perform generally worse, sometimes substantially (+5-mm error for spleen IS, -10 percent sDSC for liver). The automatic step to improve realism generally results in limited metric accuracy loss, but fails in one case (out of 60).
Conclusion: Our ML-based pipeline leads to phantoms that are significantly and substantially more individualized than currently used human-designed criteria.",
Today: 3D libraries of humans organ layout used by manual expert, replace by genetic programming personalised 3D surrogate (4:42) GP replace whole manual pipeline (4:59) See also \cite{Virgolin:2020:PMB} Human-interpretable. Cutting-edge GP (8:27) https://github.com/marcovirgolin/GP-GOMEA \cite{Virgolin:2017:GECCO} Model size v. accuracy R^2 CWI, Amsterdam UMC, TUDelft, Institute of Oncology Ljubljana, Princess maxima center pediatric oncology, LUMC, UMC Utrecht, Princess Margret Cancer Centre UHN, the university of Manchester.
2021 HUMIES prize giving video https://www.youtube.com/watch?v=jrT0sfq6WjM 43:10 -- 47:08 Childhood radiation treatment 2D images, years later 3D radiation dose outside target human tissue",
Genetic Programming entries for Marco Virgolin Ziyuan Wang Tanja Alderliesten Peter A N Bosman