计算机科学
人工智能
多任务学习
模式识别(心理学)
分割
计算机视觉
任务(项目管理)
编码器
特征提取
多数决原则
特征(语言学)
图像分割
语言学
哲学
管理
经济
操作系统
作者
Jinkui Hao,Ting Shen,Xueli Zhu,Yonghuai Liu,Ardhendu Behera,Dan Zhang,Bang Chen,Jiang Liu,Jiong Zhang,Yifan Zhao
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:41 (12): 3969-3980
被引量:1
标识
DOI:10.1109/tmi.2022.3202183
摘要
Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper, we propose a novel Voting-based Adaptive Feature Fusion multi-task network (VAFF-Net) for joint segmentation, detection, and classification of RV, FAZ, and RVJ in optical coherence tomography angiography (OCTA). A task-specific voting gate module is proposed to adaptively extract and fuse different features for specific tasks at two levels: features at different spatial positions from a single encoder, and features from multiple encoders. In particular, since the complexity of the microvasculature in OCTA images makes simultaneous precise localization and classification of retinal vascular junctions into bifurcation/crossing a challenging task, we specifically design a task head by combining the heatmap regression and grid classification. We take advantage of three different en face angiograms from various retinal layers, rather than following existing methods that use only a single en face. We carry out extensive experiments on three OCTA datasets acquired using different imaging devices, and the results demonstrate that the proposed method performs on the whole better than either the state-of-the-art single-purpose methods or existing multi-task learning solutions. We also demonstrate that our multi-task learning method generalizes across other imaging modalities, such as color fundus photography, and may potentially be used as a general multi-task learning tool. We also construct three datasets for multiple structure detection, and part of these datasets with the source code and evaluation benchmark have been released for public access.
科研通智能强力驱动
Strongly Powered by AbleSci AI