Accurate segmentation of uterus, uterine fibroids, and spine from MR images is crucial for high intensity focused 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.

中文翻译：

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HIFUNet：使用全球卷积网络进行HIFU手术计划，从MR图像对子宫区域进行多类分割。

从MR图像准确分割子宫，子宫肌瘤和脊柱对于高强度聚焦超声（HIFU）治疗至关重要，但由于1）个体之间的形状和大小差异较大，2）相邻组织之间的对比度低而仍然难以实现器官和组织，以及3）子宫肌瘤数目不明。为了解决这个问题，本文提出了一种基于二维分割模型的大型内核编解码器网络。使用大内核可以通过扩大有效接受域来捕获多尺度上下文。此外，还采用了一个深空环卷积块来扩大接收场并提取更密集的特征图。