6533b85ffe1ef96bd12c0fdc
RESEARCH PRODUCT
Machine Learning for Modeling the Biomechanical Behavior of Human Soft Tissue
Antonio J. Serrano-lópezMaria J. Ruperez-morenoDelia Lorente-garridoF. Martínez-martínezCarlos MonserratMarcelino Martínez-soberJosé D. Martín-guerreroS. Martínez-sanchissubject
Computer sciencebusiness.industrymedicine.medical_treatmentDecision treeSoft tissue02 engineering and technologyMachine learningcomputer.software_genre01 natural sciencesFinite element methodData modeling010101 applied mathematicsRadiation therapy0202 electrical engineering electronic engineering information engineeringmedicine020201 artificial intelligence & image processingArtificial intelligence0101 mathematicsbusinesscomputerdescription
An accurate modeling of the biomechanical properties of human soft tissue is crucial in many clinical applications, such as, radiotherapy administration or surgery. The finite element method (FEM) is the usual choice to carry out such modeling due to its high accuracy. However, FEM is computationally very costly, and hence, its application in real-time or even off-line with short delays are still challenges to overcome. This paper proposes a framework based on Machine Learning to learn FEM modeling, thus having a tool able to yield results that may be sufficiently fast for clinical applications. In particular, the use of ensembles of Decision Trees has shown its suitability in modeling the behavior of the liver and the breast.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-12-01 | 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) |