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摘要翻译：
胶质瘤是原发性脑肿瘤中最常见和侵袭性最强的类型之一。脑皮层下结构的准确分割对胶质瘤的研究至关重要，因为它有助于监测胶质瘤的进展和评估治疗结果。然而，人工分割的磁共振成像(MRI)数据需要大量的人工劳动，难以获得，限制了精确定量测量在临床实践中的应用。在本工作中，我们试图通过建立一个基于三维卷积神经网络的神经胶质瘤自动分割模型来解决这个问题。我们的分割模型的主要困难来自于不同患者之间胶质瘤的位置、结构和形状差异很大的事实。为了准确地对每个体素进行分类，我们的模型通过从两个尺度的感受野中提取特征来捕获多尺度的上下文信息。为了充分挖掘肿瘤的结构，我们提出了一种新的结构，将坏死和非强化肿瘤(NCR/NET)、瘤周水肿(ED)和GD强化肿瘤(ET)的不同病变区域进行分级。另外，我们利用密集连接的卷积块来进一步提高性能。我们用一个补丁式训练模式来训练我们的模型，以缓解类不平衡问题。该方法在BRATS2017数据集上进行了验证，完整肿瘤、核心肿瘤和增强肿瘤的Dice得分分别为0.72、0.83和0.81。这些结果与已报道的最新结果相当，并且我们的方法在紧凑性、时间和空间效率方面优于现有的基于三维的方法。
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英文标题：
《MRI Tumor Segmentation with Densely Connected 3D CNN》
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作者：
Lele Chen, Yue Wu, Adora M. DSouza, Anas Z. Abidin, Axel Wismuller,
Chenliang Xu
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最新提交年份：
2018
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分类信息：
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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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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英文摘要：
Glioma is one of the most common and aggressive types of primary brain tumors. The accurate segmentation of subcortical brain structures is crucial to the study of gliomas in that it helps the monitoring of the progression of gliomas and aids the evaluation of treatment outcomes. However, the large amount of required human labor makes it difficult to obtain the manually segmented Magnetic Resonance Imaging (MRI) data, limiting the use of precise quantitative measurements in the clinical practice. In this work, we try to address this problem by developing a 3D Convolutional Neural Network~(3D CNN) based model to automatically segment gliomas. The major difficulty of our segmentation model comes with the fact that the location, structure, and shape of gliomas vary significantly among different patients. In order to accurately classify each voxel, our model captures multi-scale contextual information by extracting features from two scales of receptive fields. To fully exploit the tumor structure, we propose a novel architecture that hierarchically segments different lesion regions of the necrotic and non-enhancing tumor~(NCR/NET), peritumoral edema~(ED) and GD-enhancing tumor~(ET). Additionally, we utilize densely connected convolutional blocks to further boost the performance. We train our model with a patch-wise training schema to mitigate the class imbalance problem. The proposed method is validated on the BraTS 2017 dataset and it achieves Dice scores of 0.72, 0.83 and 0.81 for the complete tumor, tumor core and enhancing tumor, respectively. These results are comparable to the reported state-of-the-art results, and our method is better than existing 3D-based methods in terms of compactness, time and space efficiency.
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PDF链接：
https://arxiv.org/pdf/1802.02427
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