Browsing by Subject CNNs

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  • Authors: Hoang, Hong Son; Pham, Cam Phuong; Theo, van Walsum; Luu, Manh Ha (2020)

  • Liver segmentation is relevant for several clinical applications. Automatic liver segmentation using convolutional neural networks (CNNs) has been recently investigated. In this paper, we propose a new approach of combining a largest connected component (LCC) algorithm, as a post-processing step, with CNN approaches to improve liver segmentation accuracy. Specifically, in this study, the algorithm is combined with three well-known CNNs for liver segmentation: FCN-CRF, DRIU and V-net. We perform the experiment on a variety of liver CT images, ranging from non-contrast enhanced CT images to low-dose contrast enhanced CT images. The methods are evaluated using Dice score, Haudorff distan...

Browsing by Subject CNNs

Jump to: 0-9 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
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Showing results 1 to 1 of 1
  • 7.pdf.jpg
  • Article


  • Authors: Hoang, Hong Son; Pham, Cam Phuong; Theo, van Walsum; Luu, Manh Ha (2020)

  • Liver segmentation is relevant for several clinical applications. Automatic liver segmentation using convolutional neural networks (CNNs) has been recently investigated. In this paper, we propose a new approach of combining a largest connected component (LCC) algorithm, as a post-processing step, with CNN approaches to improve liver segmentation accuracy. Specifically, in this study, the algorithm is combined with three well-known CNNs for liver segmentation: FCN-CRF, DRIU and V-net. We perform the experiment on a variety of liver CT images, ranging from non-contrast enhanced CT images to low-dose contrast enhanced CT images. The methods are evaluated using Dice score, Haudorff distan...