+ Site Statistics
References:
54,258,434
Abstracts:
29,560,870
PMIDs:
28,072,757
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

Multi-parametric (ADC/PWI/T2-w) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme



Multi-parametric (ADC/PWI/T2-w) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme



Magma 28(1): 13-22



Glioblastoma multiforme (GBM) brain tumor is heterogeneous in nature, so its quantification depends on how to accurately segment different parts of the tumor, i.e. viable tumor, edema and necrosis. This procedure becomes more effective when metabolic and functional information, provided by physiological magnetic resonance (MR) imaging modalities, like diffusion-weighted-imaging (DWI) and perfusion-weighted-imaging (PWI), is incorporated with the anatomical magnetic resonance imaging (MRI). In this preliminary tumor quantification work, the idea is to characterize different regions of GBM tumors in an MRI-based semi-automatic multi-parametric approach to achieve more accurate characterization of pathogenic regions. For this purpose, three MR sequences, namely T2-weighted imaging (anatomical MR imaging), PWI and DWI of thirteen GBM patients, were acquired. To enhance the delineation of the boundaries of each pathogenic region (peri-tumoral edema, viable tumor and necrosis), the spatial fuzzy C-means algorithm is combined with the region growing method. The results show that exploiting the multi-parametric approach along with the proposed semi-automatic segmentation method can differentiate various tumorous regions with over 80 % sensitivity, specificity and dice score. The proposed MRI-based multi-parametric segmentation approach has the potential to accurately segment tumorous regions, leading to an efficient design of the pre-surgical treatment planning.

(PDF emailed within 0-6 h: $19.90)

Accession: 054484662

Download citation: RISBibTeXText

PMID: 24691860

DOI: 10.1007/s10334-014-0442-7


Related references

Segmentation of multi-isotope imaging mass spectrometry data for semi-automatic detection of regions of interest. Plos One 7(2): E30576, 2012

Semi-automated segmentation of a glioblastoma multiforme on brain MR images for radiotherapy planning. Nihon Hoshasen Gijutsu Gakkai Zasshi 66(4): 353-362, 2010

A Multi-Objective Decision Making Approach for Solving the Image Segmentation Fusion Problem. IEEE Transactions on Image Processing 26(8): 3831-3845, 2017

Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization. Ejnmmi Research 9(1): 19, 2019

Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation. Frontiers in Neuroinformatics 12: 69, 2018

Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software. Korean Journal of Radiology 18(3): 498-509, 2017

Classifying Glioblastoma Multiforme Follow-Up Progressive vs. Responsive Forms Using Multi-Parametric MRI Features. Frontiers in Neuroscience 10: 615, 2017

Comparison and assessment of semi-automatic image segmentation in computed tomography scans for image-guided kidney surgery. Medical Physics 38(11): 6265-6274, 2012

Semi-automatic segmentation software for quantitative clinical brain glioblastoma evaluation. Academic Radiology 19(8): 977-985, 2012

Development and evaluation of an automatic tumor segmentation tool: a comparison between automatic, semi-automatic and manual segmentation of mandibular odontogenic cysts and tumors. Journal of Cranio-Maxillo-Facial Surgery 43(3): 355-359, 2016

A quantitative comparison of CT alone vs CT/MR image fusion in radiation treatment planning of glioblastoma multiforme Is there added value with MR. International Journal of Radiation Oncology Biology Physics 54(2 Supplement): 242-243, 2002

Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma. European Journal of Radiology 108: 147-154, 2018

Image segmentation method based on multi-spectral image fusion and morphology reconstruction. 2008

Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation. Medical Image Analysis 16(1): 140-149, 2012

Discrepancy between lesion distributions on methionine PET and MR images in patients with glioblastoma multiforme: insight from a PET and MR fusion image study. Journal of Neurology, Neurosurgery, and Psychiatry 75(10): 1457-1462, 2004