In this paper, we proposed and validated a fully automatic pipeline for hippocampal surface generation via 3D U-net coupled with active shape modeling (ASM). Principally, the proposed pipeline consisted of three steps. In the beginning, for each magnetic resonance image, a 3D U-net was employed to obtain the automatic hippocampus segmentation at each hemisphere. Secondly, ASM was performed on a group of pre-obtained template surfaces to generate mean shape and shape variation parameters through principal component analysis. Ultimately, hybrid particle swarm optimization was utilized to search for the optimal shape variation parameters that best match the segmentation. The hippocampal surface was then generated from the mean shape and the shape variation parameters. The proposed pipeline was observed to provide hippocampal surfaces at both hemispheres with high accuracy, correct anatomical topology, and sufficient smoothness.
翻译:在本文中,我们提议并验证了一条完全自动的河马运动地表生成管道,通过3D U-net以及主动形状模型(ASM)生成河马运动地表,主要由三个步骤组成:最初,每个磁共振图像都使用了3D U-net,以获得每个半球的自动河马运动地块分割;第二,在一组预先获得的模版表面上进行了ASM,以通过主要组成部分分析产生平均形状和形状变异参数;最后,利用混合粒子群温优化来寻找最符合分层的最佳形状变异参数。河马运动地表随后从平均形状和形状变异参数中产生。观察到,拟议的管道为两个半球的河马运动地表提供了高精度、准确的解剖面学和足够的平滑度。