  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.
