Monitoring slowly evolving tumors

被引：9

作者：

Konukoglu, E. ^{[1
]}

Wells, W. M. ^{[2
]}

Novellas, S. ^{[1
]}

Ayachel, N. ^{[1
]}

Kikinis, R. ^{[2
]}

Black, P. M. ^{[2
]}

Pohl, K. M. ^{[2
]}

机构：

[1] INRIA, Asclepios Res Project, Sophia Antipolis, France

[2] Brigham & Womens Hosp, Boston, MA 02115 USA

来源：

2008 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1-4 | 2008年

关键词：

tumor; follow-up; time series analysis;

D O I：

10.1109/ISBI.2008.4541120

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Change detection is a critical task in the diagnosis of many slowly evolving pathologies. This paper describes an approach that semi-automatically performs this task using longitudinal medical images. We are specifically interested in meningiomas, which experts often find difficult to monitor as the tumor evolution can be obscured by image artifacts. We test the method on synthetic data with known tumor growth as well as ten clinical data sets. We show that the results of our approach highly correlate with expert findings but seem to be less impacted by inter- and intra-rater variability.

引用

页码：812 / +

页数：2

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