U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation

04/07/2020
by   Shuhang Wang, et al.
20

This paper proposes a novel U-Net variant using stacked dilated convolutions for medical image segmentation (SDU-Net). SDU-Net adopts the architecture of vanilla U-Net with modifications in the encoder and decoder operations (an operation indicates all the processing for feature maps of the same resolution). Unlike vanilla U-Net which incorporates two standard convolutions in each encoder/decoder operation, SDU-Net uses one standard convolution followed by multiple dilated convolutions and concatenates all dilated convolution outputs as input to the next operation. Experiments showed that SDU-Net outperformed vanilla U-Net, attention U-Net (AttU-Net), and recurrent residual U-Net (R2U-Net) in all four tested segmentation tasks while using parameters around 40 R2U-Net's.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 3

page 6

07/18/2018

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

In this paper, we present UNet++, a new, more powerful architecture for ...
05/10/2019

T-Net: Encoder-Decoder in Encoder-Decoder architecture for the main vessel segmentation in coronary angiography

In this paper, we proposed T-Net containing a small encoder-decoder insi...
10/17/2018

LadderNet: Multi-path networks based on U-Net for medical image segmentation

U-Net has been providing state-of-the-art performance in many medical im...
05/31/2020

DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation

Recently, deep learning has become much more popular in computer vision ...
09/27/2018

nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

The U-Net was presented in 2015. With its straight-forward and successfu...
05/01/2018

Semantic Binary Segmentation using Convolutional Networks without Decoders

In this paper, we propose an efficient architecture for semantic image s...
05/10/2021

CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation

Currently, developments of deep learning techniques are providing instru...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.