Accurate segmentation of medical images into anatomically meaningful regions is critical for the extraction of quantitative indices or biomarkers. The common pipeline for segmentation comprises regions of interest detection stage and segmentation stage, which are independent of each other and typically performed using separate deep learning networks. The performance of the segmentation stage highly relies on the extracted set of spatial features and the receptive fields. In this work, we propose an end-to-end network, called Trilateral Attention Network (TaNet), for real-time detection and segmentation in medical images. TaNet has a module for region localization, and three segmentation pathways: 1) handcrafted pathway with hand-designed convolutional kernels, 2) detail pathway with regular convolutional kernels, and 3) a global pathway to enlarge the receptive field. The first two pathways encode rich handcrafted and low-level features extracted by hand-designed and regular kernels while the global pathway encodes high-level context information. By jointly training the network for localization and segmentation using different sets of features, TaNet achieved superior performance, in terms of accuracy and speed, when evaluated on an echocardiography dataset for cardiac segmentation. The code and models will be made publicly available in TaNet Github page.
翻译:将医疗图像准确分解成具有解剖意义的区域对于提取定量指数或生物标志至关重要。用于分解的普通管道由利益检测阶段和分解阶段的区域组成,这些区域彼此独立,通常使用不同的深层学习网络进行。分解阶段的性能高度依赖于抽取的一套空间特征和可接收字段。在这项工作中,我们提议建立一个端对端网络,称为三边注意网络,用于实时检测和医学图像的分解。TaNet有一个区域本地化模块,三个分解途径:1) 带手工设计的卷轴的手工制作路径,2)带正常的卷轴的细路,3) 扩大可接收场的全球路径。头两条路径由手工设计和常规内核抽取的丰富手工艺和低级别特征编码,而全球路径编码是高层次背景信息。通过联合培训使用不同类型特征的本地化和分解网络,TaNet实现的高级性能,以精确性和速度为条件,2) 用常规的卷心心,3) 扩大全球路径,以扩大可接收场域域。在对磁路路段进行数据进行评估时,将采用磁分析。