Recognizing Pediatric Tuberous Sclerosis Complex Based on Multi-Contrast MRI and Deep Weighted Fusion Network

• Published in: Bioengineering (Basel) Vol. 10; no. 7; p. 870
• Main Authors: Jiang, Dian, Liao, Jianxiang, Zhao, Cailei, Zhao, Xia, Lin, Rongbo, Yang, Jun, Li, Zhi-Cheng, Zhou, Yihang, Zhu, Yanjie, Liang, Dong, Hu, Zhanqi, Wang, Haifeng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.07.2023; MDPI
• Subjects: Algorithms; Artificial neural networks; Bioengineering; Brain research; Cerebrospinal fluid; Children; Classification; convolutional neural network; Deep learning; Image enhancement; Lesions; Magnetic resonance imaging; multi-contrast MRI; Neural networks; Neuroimaging; Pediatrics; Physiological aspects; rare neurodevelopmental disorder; Signal to noise ratio; Teaching methods; Tuberous sclerosis; tuberous sclerosis complex; Visualization; China; tuberous sclerosis complex; rare neurodevelopmental disorder; children; convolutional neural network; multi-contrast MRI
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering10070870

Multi-contrast magnetic resonance imaging (MRI) is wildly applied to identify tuberous sclerosis complex (TSC) children in a clinic. In this work, a deep convolutional neural network with multi-contrast MRI is proposed to diagnose pediatric TSC. Firstly, by combining T2W and FLAIR images, a new synthesis modality named FLAIR3 was created to enhance the contrast between TSC lesions and normal brain tissues. After that, a deep weighted fusion network (DWF-net) using a late fusion strategy is proposed to diagnose TSC children. In experiments, a total of 680 children were enrolled, including 331 healthy children and 349 TSC children. The experimental results indicate that FLAIR3 successfully enhances the visibility of TSC lesions and improves the classification performance. Additionally, the proposed DWF-net delivers a superior classification performance compared to previous methods, achieving an AUC of 0.998 and an accuracy of 0.985. The proposed method has the potential to be a reliable computer-aided diagnostic tool for assisting radiologists in diagnosing TSC children.