Coupling Convolutional Neural Networks and Hough Voting for Robust Segmentation of Ultrasound Volumes
Coupling Convolutional Neural Networks and Hough Voting for Robust Segmentation of Ultrasound Volumes
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DOI：
10.1007/978-3-319-45886-1_36
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发表时间：
2016-09
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影响因子：
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通讯作者：
Christine Kroll;F. Milletarì;Nassir Navab;Seyed-Ahmad Ahmadi
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文献类型：
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作者：
Christine Kroll;F. Milletarì;Nassir Navab;Seyed-Ahmad Ahmadi
本文分析了卷积神经网络(CNN)在医学体积解剖结构定位和分割中的适用性和性能，这些约束条件包括：可用训练数据量小、处理时间短和计算资源有限。我们的分割方法使用CNN来同时进行分类和特征提取。为了自动定位和分割感兴趣的解剖结构，已经开发了霍夫投票策略。我们的结果表明：(I)由于包含了先验形状知识，(I)提高了稳健性；(Ii)即使在训练期间只有小数据集可用时，分割也非常准确；(Iii)速度和计算要求与临床中通常存在的要求相匹配。
This paper analyses the applicability and performance of Convolutional Neural Networks (CNN) to localise and segment anatomical structures in medical volumes under clinically realistic constraints: small amount of available training data, the need of a short processing time and limited computational resources. Our segmentation approach employs CNNs for simultaneous classification and feature extraction. A Hough voting strategy has been developed in order to automatically localise and segment the anatomy of interest. Our results show (i) improved robustness, due to the inclusion of prior shape knowledge, (ii) highly accurate segmentation even when only small datasets are available during training, (iii) speed and computational requirements that match those that are usually present in clinical settings.