Liu Yan, Stojadinovic Strahinja, Hrycushko Brian, Wardak Zabi, Lau Steven, Lu Weiguo, Yan Yulong, Jiang Steve B, Zhen Xin, Timmerman Robert, Nedzi Lucien, Gu Xuejun
School of Electrical Engineering and Information, Sichuan University, Chengdu, Sichuan, China.
Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, United States of America.
PLoS One. 2017 Oct 6;12(10):e0185844. doi: 10.1371/journal.pone.0185844. eCollection 2017.
Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.
准确且自动的脑转移瘤靶区勾画是高效立体定向放射外科（SRS）治疗计划的关键步骤。在这项工作中，我们开发了一种深度学习卷积神经网络（CNN）算法，用于在对比增强T1加权磁共振成像（MRI）数据集上分割脑转移瘤。我们将基于CNN的算法集成到自动脑转移瘤分割工作流程中，并在多模态脑肿瘤图像分割挑战赛（BRATS）数据和临床患者数据上进行了验证。在BRATS数据上的验证结果显示，肿瘤核心区域的平均DICE系数（DCs）为0.75±0.07，强化肿瘤区域为0.81±0.04，这超过了2015年BRATS挑战赛中的大多数技术。患者病例的分割结果显示，平均DCs为0.67±0.03，受试者工作特征曲线下面积为0.98±0.01。所开发的自动分割策略超越了当前的基准水平，为多发性脑转移瘤的SRS治疗计划提供了一个有前景的工具。
