计算机科学
人工智能
分割
模式识别(心理学)
卷积神经网络
背景(考古学)
图像分割
特征(语言学)
计算机视觉
特征提取
语言学
生物
哲学
古生物学
作者
Xin Fei,Xiaojie Li,Canghong Shi,Hongping Ren,Imran Mumtaz,J. Guo,Yu Wu,Yong Luo,Jiancheng Lv,Xi Wu
标识
DOI:10.1016/j.compbiomed.2023.106985
摘要
Accurate segmentation of medical images is an important step during radiotherapy planning and clinical diagnosis. However, manually marking organ or lesion boundaries is tedious, time-consuming, and prone to error due to subjective variability of radiologist. Automatic segmentation remains a challenging task owing to the variation (in shape and size) across subjects. Moreover, existing convolutional neural networks based methods perform poorly in small medical objects segmentation due to class imbalance and boundary ambiguity. In this paper, we propose a dual feature fusion attention network (DFF-Net) to improve the segmentation accuracy of small objects. It mainly includes two core modules: the dual-branch feature fusion module (DFFM) and the reverse attention context module (RACM). We first extract multi-resolution features by multi-scale feature extractor, then construct DFFM to aggregate the global and local contextual information to achieve information complementarity among features, which provides sufficient guidance for accurate small objects segmentation. Moreover, to alleviate the degradation of segmentation accuracy caused by blurred medical image boundaries, we propose RACM to enhance the edge texture of features. Experimental results on datasets NPC, ACDC, and Polyp demonstrate that our proposed method has fewer parameters, faster inference, and lower model complexity, and achieves better accuracy than more state-of-the-art methods.
科研通智能强力驱动
Strongly Powered by AbleSci AI