Shu-Hui Wang1,2, Jing Du1, Hui Xu1, Dawei Yang1, Yuxiang Ye3, Yinan Chen3, Yajing Zhu3, Te Ba1, Chunwang Yuan4, Zheng-Han Yang5. 1. Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing, 100050, China. 2. Department of Radiology, Weihai Municipal Hospital, Weihai, Shandong Province, China. 3. Shanghai SenseTime Intelligent Technology Co. Ltd, Beijing, China. 4. Center of Interventional Oncology and Liver Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China. 5. Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing, 100050, China. yangzhenghan@vip.163.com.
Abstract
PURPOSE: To develop and validate a dense feature fusion neural network (DFuNN) to automatically recognize different sequences and phases of liver magnetic resonance imaging (MRI). MATERIALS AND METHODS: In total, 3869 sequences and phases from 384 liver MRI examinations, divided into training/validation (n = 2886 sequences from 287 patients) and test (n = 983 sequences from 97 patients) sets, were used in this retrospective study. Ten unenhanced sequences and enhanced phases were included. Manual sequence recognition, performed by two radiologists (20 and 10 years of experience) in a consensus reading, was used as the reference standard. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of the DFuNN on an identical unseen test set. Finally, we evaluated the factors impacting the model precision. RESULTS: A fusion block improved the performance of the DFuNN. DFuNN with a fusion block achieved good recognition performance for both complete and incomplete sequences and phases in the test set. The average sensitivity of recognition performance for complete sequence and phase inputs ranged from 88.06 to 100%, the average specificity ranged from 99.12 to 99.94%, and the median accuracy ranged from 98.02 to 99.95%. The DFuNN prediction accuracy for patients without cirrhosis were significantly higher than those for patients with cirrhosis (P = 0.0153). No significant difference was found in the accuracy across other factors. CONCLUSION: DFuNN can automatically and accurately identify specific unenhanced MRI sequences and enhanced MRI phases.
PURPOSE: To develop and validate a dense feature fusion neural network (DFuNN) to automatically recognize different sequences and phases of liver magnetic resonance imaging (MRI). MATERIALS AND METHODS: In total, 3869 sequences and phases from 384 liver MRI examinations, divided into training/validation (n = 2886 sequences from 287 patients) and test (n = 983 sequences from 97 patients) sets, were used in this retrospective study. Ten unenhanced sequences and enhanced phases were included. Manual sequence recognition, performed by two radiologists (20 and 10 years of experience) in a consensus reading, was used as the reference standard. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of the DFuNN on an identical unseen test set. Finally, we evaluated the factors impacting the model precision. RESULTS: A fusion block improved the performance of the DFuNN. DFuNN with a fusion block achieved good recognition performance for both complete and incomplete sequences and phases in the test set. The average sensitivity of recognition performance for complete sequence and phase inputs ranged from 88.06 to 100%, the average specificity ranged from 99.12 to 99.94%, and the median accuracy ranged from 98.02 to 99.95%. The DFuNN prediction accuracy for patients without cirrhosis were significantly higher than those for patients with cirrhosis (P = 0.0153). No significant difference was found in the accuracy across other factors. CONCLUSION:DFuNN can automatically and accurately identify specific unenhanced MRI sequences and enhanced MRI phases.
Entities:
Keywords:
Artificial intelligence; Liver; Magnetic resonance imaging (MRI)
Authors: Daniel I Glazer; David P Alper; Leslie K Lee; Rose L Wach; Stuart M Hooton; Giles W Boland; Ramin Khorasani Journal: AJR Am J Roentgenol Date: 2019-06-19 Impact factor: 3.959
Authors: Yunan Wu; Gregory M White; Tyler Cornelius; Indraneel Gowdar; Mohammad H Ansari; Mark P Supanich; Jie Deng Journal: Ann Transl Med Date: 2020-06
Authors: Parisa Moridian; Navid Ghassemi; Mahboobeh Jafari; Salam Salloum-Asfar; Delaram Sadeghi; Marjane Khodatars; Afshin Shoeibi; Abbas Khosravi; Sai Ho Ling; Abdulhamit Subasi; Roohallah Alizadehsani; Juan M Gorriz; Sara A Abdulla; U Rajendra Acharya Journal: Front Mol Neurosci Date: 2022-10-04 Impact factor: 6.261