Abstract
Lesion volume delineation of Positron Emission Tomography images is challenging because of the low spatial resolution and high noise level. Aim of this work is the development of an operator independent segmentation method of metabolic images. For this purpose, an algorithm for the biological tumor volume delineation based on random walks on graphs has been used. Twenty-four cerebral tumors are segmented to evaluate the functional follow-up after Gamma Knife radiotherapy treatment. Experimental results show that the segmentation algorithm is accurate and has real-time performance. In addition, it can reflect metabolic changes useful to evaluate radiotherapy response in treated patients.
| Lingua originale | English |
|---|---|
| Titolo della pubblicazione ospite | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pagine | 579-589 |
| Numero di pagine | 11 |
| Stato di pubblicazione | Published - 2015 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
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Stefano, A., Vitabile, S., Gambino, O., Pirrone, R., Ardizzone, E., Gilardi, M. C., Stefano, A., Russo, G., Ippolito, M., D’Arrigo, C., D’Urso, D., Marletta, F., & Sabini, M. G. (2015). An automatic method for metabolic evaluation of gamma knife treatments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pagg. 579-589)