# Trilateral Attention Network for Real-time Medical Image Segmentation

arXiv: https://arxiv.org/abs/2106.09201 · HTML: https://mlsanity.com/paper/2106.09201/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation](https://mlsanity.com/paper/2004.02147/) (2020)
- [Deep Semantic Segmentation of Natural and Medical Images: A Review](https://mlsanity.com/paper/1910.07655/) (2019)
- [Multi-scale self-guided attention for medical image segmentation](https://mlsanity.com/paper/1906.02849/) (2019)
- [CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation](https://mlsanity.com/paper/2009.10549/) (2020)
- [Local Binary Convolutional Neural Networks](https://mlsanity.com/paper/1608.06049/) (2016)
- [Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation](https://mlsanity.com/paper/2003.10304/) (2020)
- [Triply Supervised Decoder Networks for Joint Detection and Segmentation](https://mlsanity.com/paper/1809.09299/) (2018)
- [End-to-End Face Parsing via Interlinked Convolutional Neural Networks](https://mlsanity.com/paper/2002.04831/) (2020)
- [Unified Representation Learning for Efficient Medical Image Analysis](https://mlsanity.com/paper/2006.11223/) (2020)

## Built on by (most cited first, 1 of 1)

- [ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI](https://mlsanity.com/paper/2201.10366/) (2022)

## Abstract (arXiv)

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.