In oncology, Positron Emission Tomography imaging is widely used in diagnostics of cancer metastases, in monitoring of progress in course of the cancer treatment, and in planning radiotherapeutic interventions. Accurate and reproducible delineation of the tumor in the Positron Emission Tomography scans remains a difficult task, despite being crucial for delivering appropriate radiation dose, minimizing adverse side-effects of the therapy, and reliable evaluation of treatment. In this piece of research we attempt to solve the problem of automated delineation of the tumor using 3d implementations of the spatial distance weighted fuzzy c-means, the deep convolutional neural network and a dictionary model. The methods, in diverse ways, combine intensity and spatial information.
@article{arxiv.1610.09493,
title = {Machine learning methods for accurate delineation of tumors in PET images},
author = {Jakub Czakon and Filip Drapejkowski and Grzegorz Zurek and Piotr Giedziun and Jacek Zebrowski and Witold Dyrka},
journal= {arXiv preprint arXiv:1610.09493},
year = {2016}
}
Comments
19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2016) - PETSEG challenge, Athens, Greece, 21/10/2016