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Proceedings Paper

Segmentation of anatomical structures in cardiac CTA using multi-label V-Net
Author(s): Hui Tang; Mehdi Moradi; Ahmed El Harouni; Hongzhi Wang; Gopalkrishna Veni; Prasanth Prasanna; Tanveer Syeda-Mahmood
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Paper Abstract

Segmenting anatomical structures in the chest is a crucial step in many automatic disease detection applications. Multi-atlas based methods are developed for this task, however, due to the required deformable registration step, they are often computationally expensive and create a bottle neck in terms of processing time. In contrast, convolutional neural networks (CNNs) with 2D or 3D kernels, although slow to train, are very fast in the deployment stage and have been employed to solve segmentation tasks in medical imaging. A recent improvement in performance of neural networks in medical image segmentation was recently reported when dice similarity coefficient (DSC) was used to optimize the weights in a fully convolutional architecture called V-Net. However, in the previous work, only the DSC calculated for one foreground object is optimized, as a result the DSC based segmentation CNNs are only able to perform a binary segmentation. In this paper, we extend the V-Net binary architecture to a multi-label segmentation network and use it for segmenting multiple anatomical structures in cardiac CTA. The method uses multi-label V-Net optimized by the sum over DSC for all the anatomies, followed by a post-processing method to refine the segmented surface. Our method takes averagely less than 3 sec to segment a full CTA volume. In contrast, the fastest multi-atlas based methods published so far take around 10 mins. Our method achieves an average DSC of 76% for 16 segmented anatomies using four-fold cross validation, which is close to the state-of-the-art.

Paper Details

Date Published: 2 March 2018
PDF: 6 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057407 (2 March 2018); doi: 10.1117/12.2293811
Show Author Affiliations
Hui Tang, IBM Research - Almaden (United States)
Mehdi Moradi, IBM Research - Almaden (United States)
Ahmed El Harouni, IBM Research - Almaden (United States)
Hongzhi Wang, IBM Research - Almaden (United States)
Gopalkrishna Veni, IBM Research - Almaden (United States)
Prasanth Prasanna, IBM Research - Almaden (United States)
Tanveer Syeda-Mahmood, IBM Research - Almaden (United States)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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