Archip Neculai, Jolesz Ferenc A, Warfield Simon K
Harvard Medical School, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, USA.
Acad Radiol. 2007 Oct;14(10):1242-51. doi: 10.1016/j.acra.2007.05.025.
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.
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.
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/.
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.
我们介绍了一种用于从磁共振（MR）图像中分割脑肿瘤的验证框架。还提出了一种新颖的无监督半自动脑肿瘤分割算法。
所提出的框架包括1）脑肿瘤患者的T1加权MR图像，2）由四位独立专家进行的脑肿瘤分割，3）由半自动算法生成的脑肿瘤分割，以及4）一种估计分割算法性能的软件工具。
我们在提出的框架内展示了这种新颖分割算法的验证。我们展示了它的性能，并将其与现有分割方法进行比较。图像数据集和软件可在http://www.brain-tumor-repository.org/获取。
我们提供了一个互联网资源，可访问MR脑肿瘤图像数据和分割结果，供研究界公开使用。其目的是通过提供原始测试和图像数据、人类专家分割结果以及比较分割结果的方法，鼓励分割方法的开发和评估。
