Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction

Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction

Virgolin, M. (Centrum Wiskunde & Informatica (CWI))
Wang, Ziyuan (Amsterdam UMC)
Alderliesten, T. (TU Delft Algorithmics; Amsterdam UMC; Vrije Universiteit Amsterdam)
Bosman, P.A.N. (TU Delft Algorithmics; Centrum Wiskunde & Informatica (CWI))

2020-07-01

Purpose: Current phantoms used for the dose reconstruction of long-term childhood cancer survivors lack individualization. We design a method to predict highly individualized abdominal three-dimensional (3-D) phantoms automatically. 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-Sørensen 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: ∼8 mm for liver LR, IS, and spleen AP, IS; ∼5 mm for liver AP and spleen LR; ∼80 % for abdomen sDSC; and ∼60 % to 65% 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 % 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.

dose reconstruction
machine learning
pediatric cancer
phantom
radiation treatment

http://resolver.tudelft.nl/uuid:e975cb1a-13fe-4b79-93c7-18ea03998213

https://doi.org/10.1117/1.JMI.7.4.046501

2329-4302

Journal of Medical Imaging, 7 (4)

Institutional Repository

journal article

© 2020 M. Virgolin, Ziyuan Wang, T. Alderliesten, P.A.N. Bosman