Residual based attention-Unet combing DAC and RMP modules for automatic liver tumor segmentation in CT.
Math Biosci Eng
; 19(5): 4703-4718, 2022 03 09.
Article
in En
| MEDLINE
| ID: mdl-35430836
ABSTRACT
Purpose:
Due to the complex distribution of liver tumors in the abdomen, the accuracy of liver tumor segmentation cannot meet the needs of clinical assistance yet. This paper aims to propose a new end-to-end network to improve the segmentation accuracy of liver tumors from CT.Method:
We proposed a hybrid network, leveraging the residual block, the context encoder (CE), and the Attention-Unet, called ResCEAttUnet. The CE comprises a dense atrous convolution (DAC) module and a residual multi-kernel pooling (RMP) module. The DAC module ensures the network derives high-level semantic information and minimizes detailed information loss. The RMP module improves the ability of the network to extract multi-scale features. Moreover, a hybrid loss function based on cross-entropy and Tversky loss function is employed to distribute the weights of the two-loss parts through training iterations.Results:
We evaluated the proposed method in LiTS17 and 3DIRCADb databases. It significantly improved the segmentation accuracy compared to state-of-the-art methods.Conclusions:
Experimental results demonstrate the satisfying effects of the proposed method through both quantitative and qualitative analyses, thus proving a promising tool in liver tumor segmentation.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Image Processing, Computer-Assisted
/
Liver Neoplasms
Type of study:
Qualitative_research
Limits:
Humans
Language:
En
Journal:
Math Biosci Eng
Year:
2022
Document type:
Article
Affiliation country:
China
Publication country:
EEUU
/
ESTADOS UNIDOS
/
ESTADOS UNIDOS DA AMERICA
/
EUA
/
UNITED STATES
/
UNITED STATES OF AMERICA
/
US
/
USA