GBM Volumetry using the 3D Slicer Medical Image Computing Platform
Jan Egger,Tina Kapur,Andriy Fedorov,Steve Pieper,James V. Miller,Harini Veeraraghavan,Bernd Freisleben,Alexandra J. Golby,Christopher Nimsky,Ron Kikinis +9 more
TLDR
In this study, 4 physicians segmented glioblastoma multiforme patients in 10 patients, once using the competitive region-growing based GrowCut segmentation module of Slicer, and once purely by drawing boundaries completely manually on a slice-by-slice basis.Abstract:
Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions. Typically, the tumor volume is computed on a slice-by-slice basis using MRI scans obtained at regular intervals. (3D)Slicer - a free platform for biomedical research - provides an alternative to this manual slice-by-slice segmentation process, which is significantly faster and requires less user interaction. In this study, 4 physicians segmented GBMs in 10 patients, once using the competitive region-growing based GrowCut segmentation module of Slicer, and once purely by drawing boundaries completely manually on a slice-by-slice basis. Furthermore, we provide a variability analysis for three physicians for 12 GBMs. The time required for GrowCut segmentation was on an average 61% of the time required for a pure manual segmentation. A comparison of Slicer-based segmentation with manual slice-by-slice segmentation resulted in a Dice Similarity Coefficient of 88.43 ± 5.23% and a Hausdorff Distance of 2.32 ± 5.23 mm.read more
Citations
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Journal ArticleDOI
A survey of MRI-based medical image analysis for brain tumor studies
TL;DR: The state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas is reviewed, giving special attention to recent developments in radiological tumor assessment guidelines.
Journal ArticleDOI
Robust Radiomics feature quantification using semiautomatic volumetric segmentation.
Chintan Parmar,Chintan Parmar,Chintan Parmar,Emmanuel Rios Velazquez,Emmanuel Rios Velazquez,Ralph T.H. Leijenaar,Mohammed Jermoumi,Mohammed Jermoumi,Sara Carvalho,Raymond H. Mak,Sushmita Mitra,B. Uma Shankar,Ron Kikinis,Benjamin Haibe-Kains,Philippe Lambin,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +16 more
TL;DR: 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors and can be employed for quantitative image feature extraction and image data mining research in large patient cohorts.
Proceedings ArticleDOI
ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images
TL;DR: New extensions to the ITK-SNAP interactive image visualization and segmentation tool that support semi-automatic segmentation of multi-modality imaging datasets in a way that utilizes information from all available modalities simultaneously are described.
Journal ArticleDOI
MRI based medical image analysis: Survey on brain tumor grade classification
Geethu Mohan,M. Monica Subashini +1 more
TL;DR: The current trends in segmentation and classification relevant to tumor infected human brain MR images with a target on gliomas which include astrocytoma are retrospected.
Journal ArticleDOI
Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images
Jue Jiang,Yu-Chi Hu,Chia-Ju Liu,Darragh Halpenny,Matthew D. Hellmann,Joseph O. Deasy,Gig S. Mageras,Harini Veeraraghavan +7 more
TL;DR: A multi-scale CNN approach for volumetrically segmenting lung tumors which enables accurate, automated identification of and serial measurement of tumor volumes in the lung.
References
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