摘要翻译：
晚期钆增强心脏MRI（LGE-CMRI）检测心房颤动(AF)患者的心房瘢痕是一种有希望的分层、指导消融治疗和预测治疗成功的技术。瘢痕组织的可视化和量化需要从LGE-CMRI图像中分割左心房(LA)和高强度瘢痕区域。由于健康组织信号的消除、低信噪比和图像质量的限制，这两个分割任务具有挑战性。大多数方法需要人工监督和/或第二次明亮血液MRI采集来进行解剖分割。从一次LGE-CMRI采集中自动分割LA解剖和瘢痕组织的需求很高。在本研究中，我们提出了一种新的全自动多视点双任务(MVTT)递归注意力模型，该模型结合序贯学习和扩张残差学习，在LGE-CMRI图像上直接工作，通过一个创新的注意力模型同时分割LA（包括附着肺静脉）和描绘心房瘢痕。与其他最先进的方法相比，提出的MVTT取得了令人信服的改进，能够生成一个患者特定的解剖学和心房瘢痕评估模型。
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英文标题：
《Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial
  Scars Segmentation》
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作者：
Jun Chen, Guang Yang, Zhifan Gao, Hao Ni, Elsa Angelini, Raad
  Mohiaddin, Tom Wong, Yanping Zhang, Xiuquan Du, Heye Zhang, Jennifer Keegan,
  David Firmin
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success. Visualisation and quantification of scar tissues require a segmentation of both the left atrium (LA) and the high intensity scar regions from LGE-CMRI images. These two segmentation tasks are challenging due to the cancelling of healthy tissue signal, low signal-to-noise ratio and often limited image quality in these patients. Most approaches require manual supervision and/or a second bright-blood MRI acquisition for anatomical segmentation. Segmenting both the LA anatomy and the scar tissues automatically from a single LGE-CMRI acquisition is highly in demand. In this study, we proposed a novel fully automated multiview two-task (MVTT) recursive attention model working directly on LGE-CMRI images that combines a sequential learning and a dilated residual learning to segment the LA (including attached pulmonary veins) and delineate the atrial scars simultaneously via an innovative attention model. Compared to other state-of-the-art methods, the proposed MVTT achieves compelling improvement, enabling to generate a patient-specific anatomical and atrial scar assessment model.
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PDF链接：
https://arxiv.org/pdf/1806.04597