Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation最新文献

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AUTOMATED DETECTION OF MALARIAL RETINOPATHY USING TRANSFER LEARNING. 利用迁移学习自动检测疟原虫视网膜病变。

Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation

Pub Date : 2020-03-01 Epub Date: 2020-05-18 DOI: 10.1109/ssiai49293.2020.9094595

A Kurup, P Soliz, S Nemeth, V Joshi

Cerebral Malaria (CM) is a severe neurological syndrome of malaria mainly found in children and is associated with highly specific retinal lesions. The manifestation of these indications of CM in the retina is called malarial retinopathy (MR). All patients showing clinical signs of CM are commonly diagnosed and treated accordingly; however, 23% of them are misdiagnosed as they suffer from another infection with identical clinical symptoms. Due to these underlying symptoms, the false positive cases may go untreated and could result in death of the patients. A diagnostic test is needed that is highly specific in order to reduce false positives. The purpose of this study to demonstrate a technique based on a transfer learning technique using images from three different retinal cameras to identify the hemorrhages and whitening lesions in the retina which can accurately identify the patients with MR. The MR detection model gives a specificity of 100% and a sensitivity of 90% with an AUC of 0.98. The algorithm demonstrates the potential of accurate MR detection with a low-cost retinal camera.

脑型疟疾（CM）是一种严重的神经系统疟疾综合征，主要发生在儿童身上，与高度特异性视网膜病变有关。CM在视网膜上的表现称为疟疾性视网膜病变（MR）。所有出现 CM 临床症状的患者通常都会得到相应的诊断和治疗，但其中 23% 的患者会被误诊，因为他们患有另一种临床症状相同的感染。由于这些潜在症状，假阳性病例可能得不到治疗，并可能导致患者死亡。为了减少假阳性病例，我们需要一种特异性很强的诊断测试。本研究的目的是展示一种基于迁移学习技术的技术，利用三台不同视网膜相机的图像来识别视网膜上的出血和变白病变，从而准确识别 MR 患者。磁共振检测模型的特异性为 100%，灵敏度为 90%，AUC 为 0.98。该算法展示了利用低成本视网膜相机准确检测 MR 的潜力。

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引用次数: 0

A Maximum-Likelihood Approach for ADC Estimation of Lesions in Visceral Organs. 内脏器官病变 ADC 估算的最大似然法

Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation

Pub Date : 2012-01-01 DOI: 10.1109/SSIAI.2012.6202443

Abhinav K Jha, Jeffrey J Rodríguez

Accurate estimation of the apparent diffusion coefficient (ADC) of lesions in diffusion-weighted magnetic resonance imaging (DWMRI) is important to predict and monitor anti-cancer therapy response. The task of ADC estimation of lesions is complicated due to noise in the image, different variances in signal strengths at different b values and other random phenomena. In organs that have visceral motion, due to motion across scans, estimating the ADC becomes even more complex. To get rid of inaccuracies due to motion, only a single ADC value of the lesion is estimated, conventionally using a linear-regression (LR) approach. The LR approach is based on an inaccurate noise model and also suffers from other deficiencies. In this paper, we propose an easy-to-implement and computationally-fast maximum-likelihood (ML) method to estimate the ADC value of heterogeneous lesions in visceral organs. The proposed method takes into account the Rician distribution of noise in DWMRI. In the process, we also derive the statistical model for the measured mean signal intensity in DWMRI. We show using Monte-Carlo simulations that that the proposed method is more accurate than the LR method.

在弥散加权磁共振成像（DWMRI）中准确估计病灶的表观弥散系数（ADC）对于预测和监测抗癌治疗反应非常重要。由于图像中的噪声、不同 b 值下信号强度的差异以及其他随机现象，病变 ADC 的估算工作非常复杂。在有内脏运动的器官中，由于扫描时的运动，ADC 的估算变得更加复杂。为了消除运动造成的误差，传统上使用线性回归（LR）方法只估算病变部位的单个 ADC 值。线性回归方法基于不准确的噪声模型，而且还存在其他缺陷。在本文中，我们提出了一种易于实施且计算速度较快的最大似然法（ML）来估计内脏器官中异质病变的 ADC 值。该方法考虑到了 DWMRI 中噪声的 Rician 分布。在此过程中，我们还推导出了 DWMRI 中测得的平均信号强度的统计模型。我们通过蒙特卡洛模拟证明，所提出的方法比 LR 方法更准确。

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引用次数: 0

A Clustering Algorithm for Liver Lesion Segmentation of Diffusion-Weighted MR Images. 弥散加权MR图像肝脏病灶分割的聚类算法。

Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation

Pub Date : 2010-05-23 DOI: 10.1109/SSIAI.2010.5483911

Abhinav K Jha, Jeffrey J Rodríguez, Renu M Stephen, Alison T Stopeck

In diffusion-weighted magnetic resonance imaging, accurate segmentation of liver lesions in the diffusion-weighted images is required for computation of the apparent diffusion coefficient (ADC) of the lesion, the parameter that serves as an indicator of lesion response to therapy. However, the segmentation problem is challenging due to low SNR, fuzzy boundaries and speckle and motion artifacts. We propose a clustering algorithm that incorporates spatial information and a geometric constraint to solve this issue. We show that our algorithm provides improved accuracy compared to existing segmentation algorithms.

在弥散加权磁共振成像中，需要在弥散加权图像中对肝脏病变进行准确分割，以计算病变的表观扩散系数(ADC)，该参数是病变对治疗反应的指标。然而，由于低信噪比、模糊边界、斑点和运动伪影，分割问题具有挑战性。我们提出了一种结合空间信息和几何约束的聚类算法来解决这一问题。我们证明，与现有的分割算法相比，我们的算法提供了更高的精度。

引用次数: 25

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全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

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