Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies.
OBJECTIVE
Tracking the progression of low grade tumors (LGTs) is a challenging task, due to their slow growth rate and associated complex internal tumor components, such as heterogeneous enhancement, hemorrhage, and cysts. In this paper, the authors show a semiautomatic method to reliably track the volume of LGTs and the evolution of their internal components in longitudinal MRI scans.


METHODS
The authors' method utilizes a spatiotemporal evolution modeling of the tumor and its internal components. Tumor components gray level parameters are estimated from the follow-up scan itself, obviating temporal normalization of gray levels. The tumor delineation procedure effectively incorporates internal classification of the baseline scan in the time-series as prior data to segment and classify a series of follow-up scans. The authors applied their method to 40 MRI scans of ten patients, acquired at two different institutions. Two types of LGTs were included: Optic pathway gliomas and thalamic astrocytomas. For each scan, a "gold standard" was obtained manually by experienced radiologists. The method is evaluated versus the gold standard with three measures: gross total volume error, total surface distance, and reliability of tracking tumor components evolution.


RESULTS
Compared to the gold standard the authors' method exhibits a mean Dice similarity volumetric measure of 86.58% and a mean surface distance error of 0.25 mm. In terms of its reliability in tracking the evolution of the internal components, the method exhibits strong positive correlation with the gold standard.


CONCLUSIONS
The authors' method provides accurate and repeatable delineation of the tumor and its internal components, which is essential for therapy assessment of LGTs. Reliable tracking of internal tumor components over time is novel and potentially will be useful to streamline and improve follow-up of brain tumors, with indolent growth and behavior.
School of Engineering and Computer Science, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.
Lior
L
Weizman
Department of Radiology, Tel Aviv Medical Center, Tel Aviv University, Tel Aviv 64239, Israel.
Liat Ben
LB
Sira
School of Engineering and Computer Science and The Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem, Jerusalem 91904, Israel.
Leo
L
Joskowicz
Department of Radiology, Stanford University, Stanford, California 94305.
Daniel L
DL
Rubin
Department of Radiology, Stanford University, Stanford, California 94305.
Kristen W
KW
Yeom
Tel Aviv Medical Center, Dana Children's Hospital, Tel Aviv University, Tel Aviv 64239, Israel.
Shlomi
S
Constantini
Tel Aviv Medical Center, Dana Children's Hospital, Tel Aviv University, Tel Aviv 64239, Israel.
Ben
B
Shofty
Tel Aviv Medical Center, Functional Brain Center, Tel Aviv University, Tel Aviv 64239, Israel.
Dafna Ben
DB
Bashat
D000293:Adolescent; D000465:Algorithms; D001254:Astrocytoma / Q000473:pathology; D001921:Brain / Q000473:pathology; D001932:Brain Neoplasms / Q000473:pathology*; D002648:Child; D002675:Child, Preschool; D018450:Disease Progression; D005500:Follow-Up Studies; D005910:Glioma / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009367:Neoplasm Staging; D016011:Normal Distribution; D065866:Optic Tract / Q000473:pathology; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity