HIFUNet: Multi-class Segmentation of Uterine Regions from MR Images Using Global Convolutional Networks for HIFU Surgery Planning - Archive ouverte HAL Access content directly
Journal Articles IEEE Transactions on Medical Imaging Year : 2020

HIFUNet: Multi-class Segmentation of Uterine Regions from MR Images Using Global Convolutional Networks for HIFU Surgery Planning

Abstract

Accurate segmentation of uterus, uterine fibroids, and spine from MR images is crucial for high intensity fo-cused ultrasound (HIFU) therapy but remains still difficult to achieve because of 1) the large shape and size variations among individuals, 2) the low contrast between adjacent organs and tissues, and 3) the unknown number of uterine fibroids. To tackle this problem, in this paper, we propose a large kernel Encoder-Decoder Network based on a 2D segmentation model. The use of this large kernel can capture multi-scale contexts by enlarging the valid receptive field. In addition, a deep multiple atrous convolution block is also employed to enlarge the receptive field and extract denser feature maps. Our approach is compared to both conventional and other deep learning methods and the experimental results conducted on a large dataset show its effectiveness.
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Dates and versions

hal-02569014 , version 1 (10-05-2020)

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Chen Zhang, Huazhong Shu, Guanyu Yang, Faqi Li, Yingang Wen, et al.. HIFUNet: Multi-class Segmentation of Uterine Regions from MR Images Using Global Convolutional Networks for HIFU Surgery Planning. IEEE Transactions on Medical Imaging, 2020, 39 (11), pp.3309-3320. ⟨10.1109/tmi.2020.2991266⟩. ⟨hal-02569014⟩
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