@inproceedings{28627f8af5e948db9696d27824eb1dc1,
title = "A nonparametric growth model for brain tumor segmentation in longitudinal MR sequences",
abstract = "Brain tumor segmentation and brain tumor growth assessment are inter-dependent and benefit from a joint evaluation. Starting from a generative model for multimodal brain tumor segmentation, we make use of a nonparametric growth model that is implemented as a conditional random field (CRF) including directed links with infinite weight in order to incorporate growth and inclusion constraints, reflecting our prior belief on tumor occurrence in the different image modalities. In this study, we validate this model to obtain brain tumor segmentations and volumetry in longitudinal image data. Moreover, we use the model to develop a probabilistic framework for estimating the likelihood of disease progression, i.e. tumor regrowth, after therapy. We present experiments for longitudinal image sequences with T1, T1c, T2 and flair images, acquired for ten patients with low and high grade gliomas.",
author = "Esther Alberts and Guillaume Charpiat and Yuliya Tarabalka and Thomas Huber and Weber, {Marc Andr{\'e}} and Jan Bauer and Claus Zimmer and Menze, {Bjoern H.}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 1st International Workshop on Brainlesion, Brainles 2015 Held in Conjunction with International Conference on Medical Image Computing for Computer-Assisted Intervention, MICCAI 2015 ; Conference date: 05-10-2015 Through 05-10-2015",
year = "2016",
doi = "10.1007/978-3-319-30858-6_7",
language = "English",
isbn = "9783319308579",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "69--79",
editor = "Mauricio Reyes and Alessandro Crimi and Oskar Maier and Oskar Maier and Heinz Handels and Bjoern Menze",
booktitle = "Brainlesion",
}