IEEE Trans Med Imaging. 2020 Nov;39(11):3309-3320. doi: 10.1109/TMI.2020.2991266. Epub 2020 Oct 28.
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.
从磁共振图像中准确分割子宫、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变化很大，2）相邻器官和组织之间的对比度低，以及 3）子宫肌瘤的数量未知，因此仍然难以实现。为了解决这个问题，在本文中，我们提出了一种基于二维分割模型的大核编码器-解码器网络。这种大核的使用可以通过扩大有效感受野来捕获多尺度上下文。此外，还采用了深度多空洞卷积块来扩大感受野并提取更密集的特征图。我们的方法与传统方法和其他深度学习方法进行了比较，在大型数据集上的实验结果表明了其有效性。
