A validation framework for brain tumor segmentation.
OBJECTIVE
We introduce a validation framework for the segmentation of brain tumors from magnetic resonance (MR) images. A novel unsupervised semiautomatic brain tumor segmentation algorithm is also presented.


METHODS
The proposed framework consists of 1) T1-weighted MR images of patients with brain tumors, 2) segmentation of brain tumors performed by four independent experts, 3) segmentation of brain tumors generated by a semiautomatic algorithm, and 4) a software tool that estimates the performance of segmentation algorithms.


RESULTS
We demonstrate the validation of the novel segmentation algorithm within the proposed framework. We show its performance and compare it with existent segmentation. The image datasets and software are available at http://www.brain-tumor-repository.org/.


CONCLUSIONS
We present an Internet resource that provides access to MR brain tumor image data and segmentation that can be openly used by the research community. Its purpose is to encourage the development and evaluation of segmentation methods by providing raw test and image data, human expert segmentation results, and methods for comparing segmentation results.
Archip
Neculai
N
Harvard Medical School, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, USA. narchip@bwh.harvard.edu
Jolesz
Ferenc A
FA
Warfield
Simon K
SK
D000465:Algorithms; D001932:Brain Neoplasms / Q000473:pathology*; D006801:Humans; D008279:Magnetic Resonance Imaging