Agn Mikael, Munck Af Rosenschöld Per, Puonti Oula, Lundemann Michael J, Mancini Laura, Papadaki Anastasia, Thust Steffi, Ashburner John, Law Ian, Van Leemput Koen
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark.
Radiation Physics, Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden.
Med Image Anal. 2019 May;54:220-237. doi: 10.1016/j.media.2019.03.005. Epub 2019 Mar 22.
In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. The proposed method may be a valuable step towards automating the delineation of brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy.
在本文中，我们提出了一种方法，用于同时分割脑肿瘤和一组广泛的危及器官，以用于胶质母细胞瘤的放射治疗计划。该方法将用于全脑分割的对比度自适应生成模型与使用卷积受限玻尔兹曼机的肿瘤形状新空间正则化模型相结合。我们通过实验证明，该方法能够适应与任何可用训练数据有很大差异的图像采集，确保其在各个治疗部位的适用性；其肿瘤分割精度与当前的技术水平相当；并且它能为放射治疗计划目的充分捕捉大多数危及器官。所提出的方法可能是朝着自动描绘接受放射治疗的胶质母细胞瘤患者的脑肿瘤和危及器官迈出的有价值的一步。
