Interactive segmentation of brain tumors in MR images using 3D region growing

1993 ◽  
Author(s):  
James E. Cabral, Jr. ◽  
Keith S. White ◽  
Yongmin Kim ◽  
Eric L. Effmann
Keyword(s):  
Brain Tumors ◽  
Region Growing ◽  
Mr Images ◽  
Interactive Segmentation ◽  
3D Region Growing

Integrated positron emission tomography and magnetic resonance imaging–guided resection of brain tumors: a report of 103 consecutive procedures

2006 ◽  
Vol 104 (2) ◽  
pp. 238-253 ◽  
Author(s):  
Benoît Pirotte ◽  
Serge Goldman ◽  
Olivier Dewitte ◽  
Nicolas Massager ◽  
David Wikler ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Brain Tumors ◽  
Mr Imaging ◽  
Tumor Resection ◽  
Tracer Uptake ◽  
Emission Tomography ◽  
Mr Images ◽  
Pet Tracer ◽  
Positron Emission ◽  
Pet Scanning

Object The aim of this study was to evaluate the integration of positron emission tomography (PET) scanning data into the image-guided resection of brain tumors. Methods Positron emission tomography scans obtained using fluorine-18 fluorodeoxyglucose (FDG) and l-[methyl-11C]methionine (MET) were combined with magnetic resonance (MR) images in the navigational planning of 103 resections of brain tumors (63 low-grade gliomas [LGGs] and 40 high-grade gliomas [HGGs]). These procedures were performed in 91 patients (57 males and 34 females) in whom tumor boundaries could not be accurately identified on MR images for navigation-based resection. The level and distribution of PET tracer uptake in the tumor were analyzed to define the lesion contours, which in turn yielded a PET volume. The PET scanning–demonstrated lesion volume was subsequently projected onto MR images and compared with MR imaging data (MR volume) to define a final target volume for navigation-based resection—the tumor contours were displayed in the microscope’s eyepiece. Maximal tumor resection was accomplished in each case, with the intention of removing the entire area of abnormal metabolic activity visualized during surgical planning. Early postoperative MR imaging and PET scanning studies were performed to assess the quality of tumor resection. Both pre- and postoperative analyses of MR and PET images revealed whether integrating PET data into the navigational planning contributed to improved tumor volume definition and tumor resection. Metabolic information on tumor heterogeneity or extent was useful in planning the surgery. In 83 (80%) of 103 procedures, PET studies contributed to defining a final target volume different from that obtained with MR imaging alone. Furthermore, FDG-PET scanning, which was performed in a majority of HGG cases, showed that PET volume was less extended than the MR volume in 16 of 21 cases and contributed to targeting the resection to the hypermetabolic (anaplastic) area in 11 (69%) of 16 cases. Performed in 59 LGG cases and 23 HGG cases, MET-PET demonstrated that the PET volume did not match the MR volume and improved the tumor volume definition in 52 (88%) of 59 and 18 (78%) of 23, respectively. Total resection of the area of increased PET tracer uptake was achieved in 54 (52%) of 103 procedures. Conclusions Imaging guidance with PET scanning provided independent and complementary information that helped to assess tumor extent and plan tumor resection better than with MR imaging guidance alone. The PET scanning guidance could help increase the amount of tumor removed and target image-guided resection to tumor portions that represent the highest evolving potential.

Region-growing fully convolutional neural network interactive segmentation of liver CT images

2021 ◽  
Vol 36 (9) ◽  
pp. 1294-1304
Author(s):  
Li-juan ZHANG ◽  
◽  
Run ZHANG ◽  
Dong-ming LI ◽  
Yang LI ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Region Growing ◽  
Ct Images ◽  
Interactive Segmentation ◽  
Liver Ct

Shape Prior Integrated in an Automated 3D Region Growing Method

2007 ◽  
Author(s):  
J-L. Rose ◽  
Ch. Revol-Muller ◽  
Mo. Almajdub ◽  
Em. Chereul ◽  
Ch. Odet
Keyword(s):  
Region Growing ◽  
Shape Prior ◽  
3D Region Growing

Fuzzy Multiscale Region Growing for Segmentation of MR Images of the Human Brain

2009 ◽  
pp. 242-246
Author(s):  
Karin Engel ◽  
Frederik Maucksch ◽  
Anja Perlich ◽  
Matthias Wolff ◽  
Klaus Toennies ◽  
...  
Keyword(s):  
Human Brain ◽  
Region Growing ◽  
Mr Images

scholarly journals Automatic liver vessel segmentation using 3D region growing and hybrid active contour model

2018 ◽  
Vol 97 ◽  
pp. 63-73 ◽  
Author(s):  
Ye-zhan Zeng ◽  
Sheng-hui Liao ◽  
Ping Tang ◽  
Yu-qian Zhao ◽  
Miao Liao ◽  
...  
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Region Growing ◽  
Vessel Segmentation ◽  
Contour Model ◽  
3D Region Growing
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