Conference record. Asilomar Conference on Signals, Systems & Computers最新文献

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Spatial Patterns and Functional Profiles for Discovering Structure in fMRI Data. 在fMRI数据中发现结构的空间模式和功能轮廓。

Conference record. Asilomar Conference on Signals, Systems & Computers

Pub Date : 2008-10-01 DOI: 10.1109/ACSSC.2008.5074650

Polina Golland, Danial Lashkari, Archana Venkataraman

We explore unsupervised, hypothesis-free methods for fMRI analysis in two different types of experiments. First, we employ clustering to identify large-scale functionally homogeneous systems. We formulate a generative mixture model, derive the EM algorithm and apply it to delineate functional systems. We also investigate spectral clustering in application to this problem and demonstrate that both methods give rise to similar partitions of the brain based on resting state fMRI data. Second, we demonstrate how to extend this approach to include information about the experimental protocol. Specifically, we formulate a mixture model in the space of possible profiles of brain response to stimuli. In both applications, our methods confirm previously known results in brain mapping and point to new research directions for exploratory analysis of fMRI data.

我们在两种不同类型的实验中探索无监督、无假设的fMRI分析方法。首先，我们使用聚类来识别大规模的功能同构系统。我们建立了一个生成混合模型，推导了EM算法，并将其应用于描述功能系统。我们还研究了光谱聚类在此问题中的应用，并证明两种方法基于静息状态fMRI数据产生相似的大脑分区。其次，我们演示了如何扩展这种方法，以包括有关实验协议的信息。具体地说，我们在大脑对刺激的可能反应的空间中制定了一个混合模型。在这两个应用中，我们的方法证实了先前已知的脑映射结果，并为fMRI数据的探索性分析指出了新的研究方向。

引用次数: 4

Interactive segmentation for geographic atrophy in retinal fundus images. 视网膜眼底图像地理萎缩的交互式分割。

Conference record. Asilomar Conference on Signals, Systems & Computers

Pub Date : 2008-10-01 DOI: 10.1109/ACSSC.2008.5074488

Noah Lee, R Theodore Smith, Andrew F Laine

Fundus auto-fluorescence (FAF) imaging is a non-invasive technique for in vivo ophthalmoscopic inspection of age-related macular degeneration (AMD), the most common cause of blindness in developed countries. Geographic atrophy (GA) is an advanced form of AMD and accounts for 12-21% of severe visual loss in this disorder [3]. Automatic quantification of GA is important for determining disease progression and facilitating clinical diagnosis of AMD. The problem of automatic segmentation of pathological images still remains an unsolved problem. In this paper we leverage the watershed transform and generalized non-linear gradient operators for interactive segmentation and present an intuitive and simple approach for geographic atrophy segmentation. We compare our approach with the state of the art random walker [5] algorithm for interactive segmentation using ROC statistics. Quantitative evaluation experiments on 100 FAF images show a mean sensitivity/specificity of 98.3/97.7% for our approach and a mean sensitivity/specificity of 88.2/96.6% for the random walker algorithm.

眼底自动荧光(FAF)成像是一种非侵入性技术，用于在体内检查年龄相关性黄斑变性(AMD)，这是发达国家最常见的致盲原因。地理萎缩(Geographic atrophy, GA)是AMD的一种晚期形式，占该疾病严重视力丧失的12-21%[3]。GA的自动量化对于确定疾病进展和促进AMD的临床诊断具有重要意义。病理图像的自动分割仍然是一个未解决的问题。本文利用分水岭变换和广义非线性梯度算子进行交互式分割，提出了一种直观、简单的地理萎缩分割方法。我们将我们的方法与使用ROC统计进行交互分割的最先进的随机漫步器[5]算法进行比较。100张FAF图像的定量评价实验表明，我们的方法的平均灵敏度/特异性为98.3/97.7%，随机漫步器算法的平均灵敏度/特异性为88.2/96.6%。

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引用次数: 31

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