Automatic discrimination of different sequences and phases of liver MRI using a dense feature fusion neural network: a preliminary study.
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/valida...
| Publicado en: | Abdominal Radiology Vol. 46; no. 10; pp. 4576 - 4588 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152397957&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152397957 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Oct2021 vid: 46 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152397957 150624365 10.1007/s00261-021-03142-4 152397957 ppf: 4576 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic discrimination of different sequences and phases of liver MRI using a dense feature fusion neural network: a preliminary study. aug: au: Wang, Shu-Hui Du, Jing Xu, Hui Yang, Dawei Ye, Yuxiang Chen, Yinan Zhu, Yajing Ba, Te Yuan, Chunwang Yang, Zheng-Han affil: Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, 100050, Beijing, China sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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