Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention最新文献

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Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders. 使用稀疏重构和堆叠去噪自动编码器在组织病理学图像中进行稳健的细胞检测和分割

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

Pub Date : 2015-10-01 Epub Date: 2015-11-18 DOI: 10.1007/978-3-319-24574-4_46

Hai Su, Fuyong Xing, Xiangfei Kong, Yuanpu Xie, Shaoting Zhang, Lin Yang

Computer-aided diagnosis (CAD) is a promising tool for accurate and consistent diagnosis and prognosis. Cell detection and segmentation are essential steps for CAD. These tasks are challenging due to variations in cell shapes, touching cells, and cluttered background. In this paper, we present a cell detection and segmentation algorithm using the sparse reconstruction with trivial templates and a stacked denoising autoencoder (sDAE). The sparse reconstruction handles the shape variations by representing a testing patch as a linear combination of shapes in the learned dictionary. Trivial templates are used to model the touching parts. The sDAE, trained with the original data and their structured labels, is used for cell segmentation. To the best of our knowledge, this is the first study to apply sparse reconstruction and sDAE with structured labels for cell detection and segmentation. The proposed method is extensively tested on two data sets containing more than 3000 cells obtained from brain tumor and lung cancer images. Our algorithm achieves the best performance compared with other state of the arts.

计算机辅助诊断（CAD）是一种很有前途的工具，可用于准确、一致的诊断和预后。细胞检测和分割是计算机辅助诊断的重要步骤。由于细胞形状的变化、触摸细胞和杂乱的背景，这些任务具有挑战性。在本文中，我们提出了一种细胞检测和分割算法，该算法使用带有琐碎模板的稀疏重建和堆叠去噪自编码器（sDAE）。稀疏重构将检测片段表示为所学字典中形状的线性组合，从而处理形状变化。琐碎模板用于对接触部分进行建模。使用原始数据及其结构化标签训练的 sDAE 用于细胞分割。据我们所知，这是首次将稀疏重构和带有结构化标签的 sDAE 应用于细胞检测和分割的研究。我们在两个数据集上对所提出的方法进行了广泛测试，这两个数据集包含了从脑肿瘤和肺癌图像中获取的 3000 多个细胞。与其他同类技术相比，我们的算法取得了最佳性能。

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引用次数: 0

Automated Model-Based Segmentation of the Left and Right Ventricles in Tagged Cardiac MRI. 标记心脏MRI中左心室和右心室的自动模型分割。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

Pub Date : 2003-01-01 DOI: 10.1007/978-3-540-39899-8_63

Albert Montillo, Dimitris Metaxas, Leon Axel

We describe an automated, model-based method to segment the left and right ventricles in 4D tagged MR. We fit 3D epicardial and endocardial surface models to ventricle features we extract from the image data. Excellent segmentation is achieved using novel methods that (1) initialize the models and (2) that compute 3D model forces from 2D tagged MR images. The 3D forces guide the models to patient-specific anatomy while the fit is regularized via internal deformation strain energy of a thin plate. Deformation continues until the forces equilibrate or vanish. Validation of the segmentations is performed quantitatively and qualitatively on normal and diseased subjects.

我们描述了一种基于模型的自动方法，用于在4D标记的MR中分割左心室和右心室。我们将3D心外膜和心内膜表面模型与我们从图像数据中提取的心室特征相拟合。使用（1）初始化模型和（2）从2D标记的MR图像计算3D模型力的新方法实现了出色的分割。3D力将模型引导到患者特定的解剖结构，同时通过薄板的内部变形应变能来正则化拟合。变形持续到力平衡或消失。对正常和患病受试者进行分割的定量和定性验证。

引用次数: 20

Automated Segmentation of the Left and Right Ventricles in 4D Cardiac SPAMM Images. 4D心脏SPAMM图像中左心室和右心室的自动分割。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

Pub Date : 2002-09-01 Epub Date: 2002-10-10 DOI: 10.1007/3-540-45786-0_77

Albert Montillo, Dimitris Metaxas, Leon Axel

In this paper we describe a completely automated volume-based method for the segmentation of the left and right ventricles in 4D tagged MR (SPAMM) images for quantitative cardiac analysis. We correct the background intensity variation in each volume caused by surface coils using a new scale-based fuzzy connectedness procedure. We apply 3D grayscale opening to the corrected data to create volumes containing only the blood filled regions. We threshold the volumes by minimizing region variance or by an adaptive statistical thresholding method. We isolate the ventricular blood filled regions using a novel approach based on spatial and temporal shape similarity. We use these regions to define the endocardium contours and use them to initialize an active contour that locates the epicardium through the gradient vector flow of an edgemap of a grayscale-closed image. Both quantitative and qualitative results on normal and diseased patients are presented.

在本文中，我们描述了一种完全自动的基于体积的方法，用于在4D标记的MR（SPAMM）图像中分割左心室和右心室，用于定量心脏分析。我们使用一种新的基于尺度的模糊连通性程序来校正由表面线圈引起的每个体积中的背景强度变化。我们将3D灰度开口应用于校正后的数据，以创建仅包含血液填充区域的体积。我们通过最小化区域方差或通过自适应统计阈值方法来对体积进行阈值设置。我们使用一种基于空间和时间形状相似性的新方法来分离心室充满血液的区域。我们使用这些区域来定义心内膜轮廓，并使用它们来初始化通过灰度闭合图像的边缘图的梯度矢量流定位心外膜的活动轮廓。给出了正常和患病患者的定量和定性结果。

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引用次数: 59

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