Abstract
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.
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URL
http://arxiv.org/abs/1903.11834