A system for brain tumor volume estimation via MR imaging and fuzzy connectedness

被引:94
作者
Liu, JG
Udupa, JK
Odhner, D
Hackney, D
Moonis, G
机构
[1] Univ Penn, Dept Radiol, Med Image Proc Grp, Philadelphia, PA 19104 USA
[2] Univ Penn, Dept Radiol, Neuroradiol Sect, Philadelphia, PA 19104 USA
关键词
brain tumor; image segmentation; magnetic resonance imaging; fuzzy connectedness; volume measurement; visualization;
D O I
10.1016/j.compmedimag.2004.07.008
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
This paper presents a method for the precise, accurate and efficient quantification of brain tumor (glioblastomas) via MRI that can be used routinely in the clinic. Tumor volume is considered useful in evaluating disease progression and response to therapy, and in assessing the need for changes in treatment plans. We use multiple MRI protocols including FLAIR, T1, and T1 with Gd enhancement to gather information about different aspects of the tumor and its vicinity. These include enhancing tissue, nonenhancing tumor, edema, and combinations of edema and tumor. We have adapted the fuzzy connectedness framework for tumor segmentation in this work and the method requires only limited user interaction in routine clinical use. The system has been tested for its precision, accuracy, and efficiency, utilizing 10 patient studies. The percent coefficient of variation (% CV) in volume due to operator subjectivity in specifying seeds for fuzzy connectedness segmentation is less than 1%. The mean operator and computer time required per study for estimating the volumes of both edema and enhancing tumor is about 16 min. The software package is designed to run under operator supervision. Delineation has been found to agree with the operators' visual inspection most of the time except in some cases when the tumor is close to the boundary of the brain. In the latter case, the scalp, surgical scar, or orbital contents are included in the delineation, and an operator has to exclude this manually. The methodology is rapid, robust, consistent, yielding highly reproducible measurements, and is likely to become part of the routine evaluation of brain tumor patients in our health system. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码:21 / 34
页数:14
相关论文
共 51 条
[1]   Three-dimensional bone-free rendering of the cerebral circulation by use of computed tomographic angiography and fuzzy connectedness [J].
Abrahams, JM ;
Saha, PK ;
Hurst, RW ;
LeRoux, PD ;
Udupa, JK .
NEUROSURGERY, 2002, 51 (01) :264-268
[2]  
Bernarding J, 1995, Medinfo, V8 Pt 1, P687
[3]   Robust three-dimensional object definition in CT and MRI [J].
Bland, PH ;
Meyer, CR .
MEDICAL PHYSICS, 1996, 23 (01) :99-107
[4]  
Brunie L, 1995, Technol Health Care, V3, P91
[5]   MRI SEGMENTATION USING FUZZY CLUSTERING-TECHNIQUES [J].
CLARK, MC ;
HALL, LO ;
GOLDGOF, DB ;
CLARKE, LP ;
VELTHUIZEN, RP ;
SILBIGER, MS .
IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE, 1994, 13 (05) :730-742
[6]   Automatic tumor segmentation using knowledge-based techniques [J].
Clark, MC ;
Hall, LO ;
Goldgof, DB ;
Velthuizen, R ;
Murtagh, FR ;
Silbiger, MS .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 1998, 17 (02) :187-201
[7]   Using neural networks to automatically detect brain tumours in MR images [J].
Dickson, S ;
Thomas, BT ;
Goddard, P .
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 1997, 8 (01) :91-99
[8]   Automatic segmentation of non-enhancing brain tumors in magnetic resonance images [J].
Fletcher-Heath, LM ;
Hall, LO ;
Goldgof, DB ;
Murtagh, FR .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 2001, 21 (1-3) :43-63
[9]   Tumour volume determination from MR images by morphological segmentation [J].
Gibbs, P ;
Buckley, DL ;
Blackband, SJ ;
Horsman, A .
PHYSICS IN MEDICINE AND BIOLOGY, 1996, 41 (11) :2437-2446
[10]   Utilization of experimental animal model for correlative multispectral MRI and pathological analysis of brain tumors [J].
Gordon, J ;
Mohamed, F ;
Vinitski, S ;
Knobler, RL ;
Curtis, M ;
Faro, S ;
Khalili, K .
MAGNETIC RESONANCE IMAGING, 1999, 17 (10) :1495-1502