@inproceedings{a597102ed2e74e8fad6c835238291b64,
title = "Longitudinal assessment of brain tumors using a repeatable prior-based segmentation",
abstract = "This paper presents an automatic method for a repeatable, prior-based segmentation and classification of brain tumors in longitudinal MR scans. The method is designed to overcome the inter/intra observer variability and to provide a repeatable delineation of the tumor boundaries in a set of follow-up scans of the same patient. The method effectively incorporates manual delineation of the first scan in the time-series to segment and classify a series of follow-up scans. Experimental results on 16 datasets yield a mean surface distance error of 0.22mm and a mean volume overlap difference of 12.34\% as compared to manual segmentation by an expert radiologist.",
keywords = "MRI, brain tumor, follow-up, segmentation",
author = "L. Weizman and L. Joskowicz and L. Ben-Sira and B. Shofty and S. Constantini and D. Ben-Bashat",
year = "2011",
doi = "10.1109/ISBI.2011.5872740",
language = "אנגלית",
isbn = "9781424441280",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1733--1736",
booktitle = "2011 8th IEEE International Symposium on Biomedical Imaging",
address = "ארצות הברית",
note = "8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011 ; Conference date: 30-03-2011 Through 02-04-2011",
}