卷积神经网络
模态(人机交互)
图像融合
人工智能
计算机科学
特征(语言学)
单光子发射计算机断层摄影术
放射科
模式识别(心理学)
核医学
医学
图像(数学)
语言学
哲学
作者
Meidi Chen,Zijin Chen,Yun Xi,Xiaoya Qiao,Xiaonong Chen,Qiu Huang
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-03-01
卷期号:27 (3): 1524-1534
被引量:1
标识
DOI:10.1109/jbhi.2022.3228603
摘要
In secondary hyperparathyroidism (SHPT) disease, preoperatively localizing hyperplastic parathyroid glands is crucial in the surgical procedure. These glands can be detected via the dual-modality imaging technique single-photon emission computed tomography/computed tomography (SPECT/CT) since it has high sensitivity and provides an accurate location. However, due to possible low-uptake glands in SPECT images, manually labeling glands is challenging, not to mention automatic label methods. In this work, we present a deep learning method with a novel fusion network to detect hyperplastic parathyroid glands in SPECT/CT images. Our proposed fusion network follows the convolutional neural network (CNN) with a three-pathway architecture that extracts modality-specific feature maps. The fusion network, composed of the channel attention module, the feature selection module, and the modality-specific spatial attention module, is designed to integrate complementary anatomical and functional information, especially for low-uptake glands. Experiments with patient data show that our fusion method improves performance in discerning low-uptake glands compared with current fusion strategies, achieving an average sensitivity of 0.822. Our results prove the effectiveness of the three-pathway architecture with our proposed fusion network for solving the glands detection task. To our knowledge, this is the first study to detect abnormal parathyroid glands in SHPT disease using SPECT/CT images, which promotes the application of preoperative glands localization.
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