Fully automated multiorgan segmentation in abdominal magnetic resonance imaging with deep neural networks.
Fully automated multiorgan segmentation in abdominal magnetic resonance imaging with deep neural networks.
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DOI：
10.1002/mp.14429
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发表时间：
2020-10
期刊：
影响因子：
3.8
通讯作者：
Fan Z
中科院分区：
文献类型：
--
作者：
Chen Y;Ruan D;Xiao J;Wang L;Sun B;Saouaf R;Yang W;Li D;Fan Z
多危险器官（OARs）的分割是磁共振放射治疗计划和磁共振引导下的腹腔肿瘤适应性放疗的必要条件。当前的实践需要人工描述，这是劳动密集型的，耗时的，并且容易在观察者内部和观察者之间发生变化。我们开发了一种深度学习（DL）技术，用于临床腹部MR图像上多个桨的全自动分割，具有高精度，可靠性和效率。我们开发了基于二维U-net的基于深度学习的腹部多器官分割（ALAMO）技术，并在深度连接、辅助监督、多视图等数据增强和训练过程中进行了量身定制的密集连接网络结构。该模型采用多层MR图像，生成分割结果输出。我们的研究使用了102名受试者的3.0-Tesla T1 VIBE （Volumetric Interpolated Breath-hold Examination）图像，其中66张用于训练，16张用于验证，20张用于测试。共10只，包括肝、脾、胰腺、左/右肾、胃、十二指肠、小肠、脊髓和椎体。经验丰富的放射科医生手动标记每个桨，然后在必要时由高级放射科医生重新编辑，以创建基本事实。使用体积重叠和表面距离来测量性能。ALAMO技术生成的分割标签与人工结果吻合良好。10个桨叶中，除十二指肠的DSC为0.80外，其余9个桨叶的DSC较高，为0.87 ~ 0.96。在一分钟内完成了对320×288×180三维体块的推断。总的来说，ALAMO模型在性能上与最先进的技术相匹配。提出的ALAMO技术允许全自动腹部MR分割，具有高精度和实用的内存和计算时间要求。
Segmentation of multiple organs-at-risk (OARs) is essential for MR-only radiation therapy treatment planning and MR-guided adaptive radiotherapy of abdominal cancers. Current practice requires manual delineation that is labor-intensive, time-consuming, and prone to intra- and inter-observer variations. We developed a deep learning (DL) technique for fully automated segmentation of multiple OARs on clinical abdominal MR images with high accuracy, reliability, and efficiency. We developed Automated deep Learning-based Abdominal Multi-Organ segmentation (ALAMO) technique based on 2D U-net and a densely connected network structure with tailored design in data augmentation and training procedures such as deep connection, auxiliary supervision, and multi-view. The model takes in multi-slice MR images and generates the output of segmentation results. 3.0-Tesla T1 VIBE (Volumetric Interpolated Breath-hold Examination) images of 102 subjects were used in our study and split into 66 for training, 16 for validation, and 20 for testing. Ten OARs were studied, including the liver, spleen, pancreas, left/right kidneys, stomach, duodenum, small intestine, spinal cord, and vertebral bodies. An experienced radiologist manually labeled each OAR, followed by reediting, if necessary, by a senior radiologist, to create the ground-truth. The performance was measured using volume overlapping and surface distance. The ALAMO technique generated segmentation labels in good agreement with the manual results. Specifically, among the 10 OARs, 9 achieved high Dice Similarity Coefficients (DSCs) in the range of 0.87–0.96, except for the duodenum with a DSC of 0.80. The inference completed within one minute for a 3D volume of 320×288×180. Overall, the ALAMO model matched the state-of-the-art techniques in performance. The proposed ALAMO technique allows for fully automated abdominal MR segmentation with high accuracy and practical memory and computation time demands.
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DOI：
10.1002/jmri.26271
发表时间：
2018-12
期刊：
Journal of magnetic resonance imaging : JMRI
影响因子：
--
作者：
Chandarana H;Wang H;Tijssen RHN;Das IJ
通讯作者：
Das IJ
影响因子：
10.6
作者：
Gibson E;Giganti F;Hu Y;Bonmati E;Bandula S;Gurusamy K;Davidson B;Pereira SP;Clarkson MJ;Barratt DC
通讯作者：
Barratt DC
影响因子：
10.9
作者：
Iglesias JE;Sabuncu MR
通讯作者：
Sabuncu MR
影响因子：
10.9
作者：
Dou, Qi;Yu, Lequan;Heng, Pheng-Ann
通讯作者：
Heng, Pheng-Ann
影响因子：
10.9
作者：
Avendi, M. R.;Kheradvar, Arash;Jafarkhani, Hamid
通讯作者：
Jafarkhani, Hamid