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首页> 外文会议>Conference on Medical Imaging: Computer-Aided Diagnosis >Probabilistic Segmentation of Small Metastatic Brain Tumors using Liquid State Machine Ensemble
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Probabilistic Segmentation of Small Metastatic Brain Tumors using Liquid State Machine Ensemble

机译：液态机组合奏小转移性脑肿瘤的概率分割

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Segmenting small brain tumors (diameter ≤ 0.5 cm) on contrast enhanced MRI images presents a particular problem, as enhancing blood vessels of similar size can be detected as false positives. The capabilities of Liquid State Machines (LSM) ensembles to separate high dimensional data arc used in this project to overcome this problem. Contrast enhanced MRI images were first transformed into time series before being fed into the LSM, which consists of a 3 dimensional array of spiking neurons, the resulting activation patterns of both the excitatory and inhibitory neurons differed from each other to a high enough degree that enhancing tumors and blood vessel of similar size could be distinguished from one another. An ensemble of two LSM's, which differed in the way the time series information was input was used to enhance data separation. The combined output of the LSM ensemble was then used as input into a random forest to classify the final result as tumor vs. non-tumor. In comparison with deep learning CNN our results show excellent small tumor recognition and generate probability maps that cover the tumors but ignore blood vessels and other contrast-enhancing objects.
机译：对比度增强的MRI图像分割小脑肿瘤（直径≤0.5cm）呈现特定问题，因为可以检测到类似大小的增强血管作为假阳性。液态机器（LSM）合奏的功能，以将该项目中使用的高维数据弧分离以克服此问题。将对比度增强的MRI图像首先转化为时间序列，然后进入LSM之前，其由尖刺神经元的3维阵列组成，所得兴奋性和抑制性神经元的产生激活模式彼此不同地达到足够高的程度可以彼此区分类似尺寸的肿瘤和血管。两个LSM的集合，其中输入时间序列信息的方式不同，以增强数据分离。然后将LSM合奏的组合输出用作随机林的输入，以将最终结果分类为肿瘤与非肿瘤。与深度学习的比较CNN我们的结果表明出色的小肿瘤识别和产生覆盖肿瘤但忽略血管和其他对比度增强物体的概率图。

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