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Abstract #1378

Classification of rim-enhancing brain abscess, glioblastoma, and brain metastasis using deep learning on multi-modality MRI

Chun-Yu Chang1, Tzu-Chao Chuang1, Teng-Yi Huang2, and Ping-Hong Lai3,4
1Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan, 2Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, 3Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, 4School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

Synopsis

Keywords: Diagnosis/Prediction, Diagnosis/Prediction

Motivation: Classification of rim-enhancing brain abscess, glioblastoma, and brain metastasis solely based on conventional MRI is challenging.

Goal(s): To develop a fully automatic system for classification of rim-enhancing lesions using deep learning with multi-modality MRI

Approach: An incremental strategy was used to train three models independently, including Model-B (CE-T1WI, T2WI, and T2-FLAIR), Model-BD (plus DWI and ADC), and Model-BDS (plus SWI). Furthermore, 3-T data were included to obtain Model-B+, BD+, and BDS+.

Results: Diffusion MRI improves the overall accuracy as expected, and the inclusion of 3T data possibly extend the flexibility of our model, reaching the highest accuracy of 0.796.

Impact: Our results demonstrated the value of multi-modality MRI in differentiation of three rim-enhancing lesions. We also highlighted the adaptability of our model on 1.5-T and 3-T data, possibly expanding its clinical use.

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Keywords