As recent advances in AI are causing the decline of conventional diagnostic methods, the realization of end-to-end diagnosis is fast approaching. Ultrasound image segmentation is an important step in the diagnostic process. An accurate and robust segmentation model accelerates the process and reduces the burden of sonographers. In contrast to previous research, we take two inherent features of ultrasound images into consideration: (1) different organs and tissues vary in spatial sizes, (2) the anatomical structures inside human body form a relatively constant spatial relationship. Based on those two ideas, we propose a new image segmentation model combining Feature Pyramid Network (FPN) and Spatial Recurrent Neural Network (SRNN). We discuss why we use FPN to extract anatomical structures of different scales and how SRNN is implemented to extract the spatial context features in abdominal ultrasound images.

翻译：随着人工智能的最新进展导致传统诊断方法的式微，端到端诊断的实现正快速临近。超声图像分割是诊断过程中的重要环节。准确且鲁棒的分割模型能够加速诊断流程并减轻超声医师的工作负担。与既往研究不同，我们充分考虑了超声图像的两大固有特征：（1）不同器官与组织的空间尺寸存在差异，（2）人体内部的解剖结构构成相对恒定的空间关系。基于这两点认识，我们提出了一种融合特征金字塔网络（FPN）与空间循环神经网络（SRNN）的新型图像分割模型。本文阐述了为何采用FPN提取不同尺度的解剖结构，以及如何通过SRNN实现腹部超声图像中空间上下文特征的提取。