Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging.
BACKGROUND
Segmentation of pre-operative low-grade gliomas (LGGs) from magnetic resonance imaging is a crucial step for studying imaging biomarkers. However, segmentation of LGGs is particularly challenging because they rarely enhance after gadolinium administration. Like other gliomas, they have irregular tumor shape, heterogeneous composition, ill-defined tumor boundaries, and limited number of image types. To overcome these challenges we propose a semi-automated segmentation method that relies only on T2-weighted (T2W) and optionally post-contrast T1-weighted (T1W) images.


METHODS
First, the user draws a region-of-interest (ROI) that completely encloses the tumor and some normal tissue. Second, a normal brain atlas and post-contrast T1W images are registered to T2W images. Third, the posterior probability of each pixel/voxel belonging to normal and abnormal tissues is calculated based on information derived from the atlas and ROI. Finally, geodesic active contours use the probability map of the tumor to shrink the ROI until optimal tumor boundaries are found. This method was validated against the true segmentation (TS) of 30 LGG patients for both 2D (1 slice) and 3D. The TS was obtained from manual segmentations of three experts using the Simultaneous Truth and Performance Level Estimation (STAPLE) software. Dice and Jaccard indices and other descriptive statistics were computed for the proposed method, as well as the experts' segmentation versus the TS. We also tested the method with the BraTS datasets, which supply expert segmentations.


CONCLUSIONS
For 2D segmentation vs. TS, the mean Dice index was 0.90 ± 0.06 (standard deviation), sensitivity was 0.92, and specificity was 0.99. For 3D segmentation vs. TS, the mean Dice index was 0.89 ± 0.06, sensitivity was 0.91, and specificity was 0.99. The automated results are comparable with the experts' manual segmentation results.


CONCLUSIONS
We present an accurate, robust, efficient, and reproducible segmentation method for pre-operative LGGs.
Department of Radiology, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA. akkus.zeynettin@mayo.edu.
Zeynettin
Z
Akkus
Department of Radiology, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA. Sedlar.Jiri@mayo.edu.
Jiri
J
Sedlar
Department of Radiology, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA. Coufalova.Lucie@mayo.edu.|International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic. Coufalova.Lucie@mayo.edu.|Neurosurgical Department of 1st Faculty of Medicine of Charles University, Military University Hospital, Prague, Czech Republic. Coufalova.Lucie@mayo.edu.
Lucie
L
Coufalova
Department of Radiology, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA. Korfiatis.Panagiotis@mayo.edu.
Panagiotis
P
Korfiatis
Department of Radiology, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA. Kline.Timothy@mayo.edu.
Timothy L
TL
Kline
Department of Radiology, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA. Warner.Joshua@mayo.edu.
Joshua D
JD
Warner
Department of Radiology, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA. Agrawal.Jay@mayo.edu.
Jay
J
Agrawal
Department of Radiology, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA. bje@mayo.edu.
Bradley J
BJ
Erickson
D000465:Algorithms; D001932:Brain Neoplasms / Q000473:pathology* / Q000601:surgery; D005910:Glioma / Q000473:pathology* / Q000601:surgery; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods; D012680:Sensitivity and Specificity