Evaluating the Impact of Training Loss on MR to Synthetic CT Conversion
| Autor: | Paolo Zaffino, Maria Francesca Spadea, Pierangela Bruno, Moiz Khan Sherwani, Francesco Calimeri |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Machine Learning, Optimization, and Data Science ISBN: 9783030645823 LOD (1) |
| DOI: | 10.1007/978-3-030-64583-0_50 |
| Popis: | Radiation therapy is one of the most important strategies for treating patients with tumor. The rationale is to deliver high radiation doses to the tumor in order to damage its DNA while sparing, at the same time, healthy tissues. In order to optimize such a process, biomedical images play a fundamental role; in particular, Magnetic Resonance (MR) produces well-contrasted images for precisely contouring tumors and organs at risk. However, due to the physical information stored in it, Computed Tomography (CT) is mandatory for accurate radiation dose calculation. To overcome this limitation, several algorithms, usually based on deep learning techniques, were proposed for converting MR–to–synthetic CT (sCT). In this paper, we report about the evaluation of the impact of three different train losses, commonly used for non–medical applications. Tests were ran on a cohort of 15 brain MR/CT image pairs. An algorithm for MR–to–sCT conversion, previously developed for MR-only radiotherapy, was used as benchmark platform. Predicted sCT images were compared on the basis of intensities and edges reconstruction. Results show that potential improvements can be achieved if non–medical image loss will be adapted to this application. |
| Databáze: | OpenAIRE |
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