# Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation

arXiv: https://arxiv.org/abs/1903.11834 · HTML: https://mlsanity.com/paper/1903.11834/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2019.

## Built on by (most cited first, 14 of 14)

- [Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2110.08762/) (2021)
- [MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.07451/) (2021)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos](https://mlsanity.com/paper/2107.00875/) (2021)
- [MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation](https://mlsanity.com/paper/2207.06799/) (2022)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [ReCal-Net: Joint Region-Channel-Wise Calibrated Network for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2109.12448/) (2021)
- [Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection](https://mlsanity.com/paper/2312.06295/) (2023)
- [DeepPyramid: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2207.01453/) (2022)
- [PMFSNet: Polarized Multi-scale Feature Self-attention Network For Lightweight Medical Image Segmentation](https://mlsanity.com/paper/2401.07579/) (2024)
- [Evaluation of Preprocessing Techniques for U-Net Based Automated Liver Segmentation](https://mlsanity.com/paper/2103.14301/) (2021)
- [DeepPyram: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2109.05352/) (2021)
- [PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation](https://mlsanity.com/paper/2111.10618/) (2021)
- [An image segmentation algorithm based on multi-scale feature pyramid network](https://mlsanity.com/paper/2305.10631/) (2023)

## Abstract (arXiv)

Liver lesion segmentation is a difficult yet critical task for medical image analysis. Recently, deep learning based image segmentation methods have achieved promising performance, which can be divided into three categories: 2D, 2.5D and 3D, based on the dimensionality of the models. However, 2.5D and 3D methods can have very high complexity and 2D methods may not perform satisfactorily. To obtain competitive performance with low complexity, in this paper, we propose a Feature-fusion Encoder-Decoder Network (FED-Net) based 2D segmentation model to tackle the challenging problem of liver lesion segmentation from CT images. Our feature fusion method is based on the attention mechanism, which fuses high-level features carrying semantic information with low-level features having image details. Additionally, to compensate for the information loss during the upsampling process, a dense upsampling convolution and a residual convolutional structure are proposed. We tested our method on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge and achieved competitive results compared with other state-of-the-art methods.