Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan)最新文献_第2页

Parsing 2D radiographs into anatomical regions is a challenging task with many applications. In the clinic, scans routinely include anterior-posterior (AP) and lateral (LAT) view radiographs. Since these orthogonal views provide complementary anatomic information, an integrated analysis can afford the greatest localization accuracy. To solve this integration we propose automatic landmark candidate detection, pruned by a learned geometric consensus detector model and refined by fitting a hierarchical active appearance organ model (H-AAM). Our main contribution is twofold. First, we propose a probabilistic joint consensus detection model which learns how landmarks in either or both views predict landmark locations in a given view. Second, we refine landmarks by fitting a joint H-AAM that learns how landmark arrangement and image appearance can help predict across views. This increases accuracy and robustness to anatomic variation. All steps require just seconds to compute and compared to processing the scouts separately, joint processing reduces mean landmark distance error from 27.3 mm to 15.7 mm in LAT view and from 12.7 mm to 11.2 mm in the AP view. The errors are comparable to human expert inter-observer variability and suitable for clinical applications such as personalized scan planning for dose reduction. We assess our method using a database of scout CT scans from 93 subjects with widely varying pathology.

在许多应用中，将二维x线照片解析为解剖区域是一项具有挑战性的任务。在临床上，常规扫描包括前后位(AP)和侧位(LAT) x线片。由于这些正交视图提供了互补的解剖信息，因此综合分析可以提供最大的定位精度。为了解决这种集成问题，我们提出了自动地标候选检测，通过学习几何一致性检测器模型进行修剪，并通过拟合分层活动外观器官模型(H-AAM)进行细化。我们的主要贡献是双重的。首先，我们提出了一个概率联合共识检测模型，该模型学习任一视图或两个视图中的地标如何预测给定视图中的地标位置。其次，我们通过拟合一个联合H-AAM来细化地标，该联合H-AAM学习地标的排列和图像外观如何帮助预测跨视图。这增加了对解剖变异的准确性和稳健性。所有步骤只需要几秒钟的计算时间，与单独处理侦察器相比，联合处理将LAT视图中的平均地标距离误差从27.3 mm减少到15.7 mm, AP视图中的平均地标距离误差从12.7 mm减少到11.2 mm。误差与人类专家之间的观察者可变性相当，适合临床应用，如个性化的剂量减少扫描计划。我们使用来自93名具有广泛不同病理的受试者的童子军CT扫描数据库来评估我们的方法。

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引用次数: 5

Accurate Whole-Brain Segmentation for Alzheimer's Disease Combining an Adaptive Statistical Atlas and Multi-atlas. 结合自适应统计图谱和多图谱对阿尔茨海默病进行精确的全脑分割。

Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan)

Pub Date : 2014-01-01 Epub Date: 2014-04-01 DOI: 10.1007/978-3-319-05530-5_7

Zhennan Yan, Shaoting Zhang, Xiaofeng Liu, Dimitris N Metaxas, Albert Montillo

Accurate segmentation of whole brain MR images including the cortex, white matter and subcortical structures is challenging due to inter-subject variability and the complex geometry of brain anatomy. However a precise solution would enable accurate, objective measurement of structure volumes for disease quantification. Our contribution is three-fold. First we construct an adaptive statistical atlas that combines structure specific relaxation and spatially varying adaptivity. Second we integrate an isotropic pairwise class-specific MRF model of label connectivity. Together these permit precise control over adaptivity, allowing many structures to be segmented simultaneously with superior accuracy. Third, we develop a framework combining the improved adaptive statistical atlas with a multi-atlas method which achieves simultaneous accurate segmentation of the cortex, ventricles, and sub-cortical structures in severely diseased brains, a feat not attained in [18]. We test the proposed method on 46 brains including 28 diseased brain with Alzheimer's and 18 healthy brains. Our proposed method yields higher accuracy than state-of-the-art approaches on both healthy and diseased brains.

由于受试者之间的可变性和大脑解剖结构的复杂几何形状，包括皮层、白质和皮层下结构在内的全脑MR图像的精确分割是具有挑战性的。然而，精确的解决方案将能够准确、客观地测量用于疾病量化的结构体积。我们的贡献是三倍的。首先，我们构建了一个自适应统计图谱，该图谱结合了结构特异性松弛和空间变化的自适应性。其次，我们集成了标签连通性的各向同性成对类特定MRF模型。这些共同允许对自适应性进行精确控制，允许以优异的精度同时分割许多结构。第三，我们开发了一个框架，将改进的自适应统计图谱与多图谱方法相结合，实现了对严重疾病大脑中皮层、心室和亚皮层结构的同时精确分割，这是[18]中没有实现的壮举。我们在46个大脑上测试了所提出的方法，其中包括28个患有阿尔茨海默病的大脑和18个健康的大脑。我们提出的方法在健康和患病的大脑上都比最先进的方法产生了更高的准确性。

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引用次数: 9

Multi-structure Atlas-Based Segmentation Using Anatomical Regions of Interest 基于多结构图谱的感兴趣解剖区域分割

Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan)

Pub Date : 2013-09-26 DOI: 10.1007/978-3-319-05530-5_21

O. J. D. Toro, H. Müller

引用次数: 11

Automatic Aorta Detection in Non-contrast 3D Cardiac CT Images Using Bayesian Tracking Method 基于贝叶斯跟踪的非对比三维心脏CT图像主动脉自动检测

Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan)

Pub Date : 2013-09-26 DOI: 10.1007/978-3-319-05530-5_13

Mingna Zheng, J. Carr, Y. Ge

引用次数: 6