Three-dimensional tumor and organ segmentation based on deep learning
10.3969/j.issn.1005-202X.2024.09.009
- VernacularTitle:基于深度学习的三维肿瘤及器官分割
- Author:
De GU
1
;
Ning WANG
;
Yinbin ZHANG
;
Le LIU
Author Information
1. 江南大学物联网工程学院,江苏无锡 214122
- Keywords:
tumor segmentation;
organ segmentation;
three-dimensional convolutional neural network;
dilated cubic integration module;
cross-stage context fusion module
- From:
Chinese Journal of Medical Physics
2024;41(9):1122-1128
- CountryChina
- Language:Chinese
-
Abstract:
In response to the challenge posed by the significant shape and scale variations of tumors and organs in three-dimensional medical images,which often results in low segmentation accuracy,an end-to-end three-dimensional fully convolutional segmentation model is introduced.A dilated cubic integration module is designed to achieve multi-scale integration at different resolution stages,thereby enhancing the recognition capability on complex boundaries.Subsequently,a cross-stage context fusion module is incorporated to merge shallow and deep features,thereby facilitating convergence and more precise localization of the target objects.Finally,features from the encoder are concatenated by the decoder to realize segmentation.The average Dice similarity coefficients reach 85.37%on the brain tumor segmentation dataset and 83.99%on the abdominal organ segmentation dataset.Experimental results indicate that the proposed model exhibits high accuracy in three-dimensional tumor and organ segmentation.
