Active Learning for Efficient Segmentation of Liver with Convolutional Neural Network–Corrected Labeling in Magnetic Resonance Imaging–Derived Proton Density Fat Fraction.
This study aimed to propose an efficient method for self-automated segmentation of the liver using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) through deep active learning. We developed an active learning framework for liver segmentation using labeled and unlabeled data...
| Published in: | Journal of Digital Imaging Vol. 34; no. 5; pp. 1225 - 1237 |
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| Main Authors: | , , , , , , , |
| Format: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153241284&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153241284 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2021 vid: 34 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 153241284 152601063 153241284 153241284 10.1007/s10278-021-00516-4 153241284 ppf: 1225 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Active Learning for Efficient Segmentation of Liver with Convolutional Neural Network–Corrected Labeling in Magnetic Resonance Imaging–Derived Proton Density Fat Fraction. aug: au: Cho, Yongwon Kim, Min Ju Park, Beom Jin Sim, Ki Choon Keu, Yeom Suk Han, Yeo Eun Sung, Deuk Jae Han, Na Yeon affil: Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, 02841, Seoul, Republic of Korea sug: subj: Learning Methods Liver Anatomy and Histology Neural Networks (Computer) Utilization Magnetic Resonance Imaging Education Deep Learning Staining and Labeling Human Paired T-Tests Descriptive Statistics Neural Networks (Computer) Nonalcoholic Fatty Liver Disease Education ab: This study aimed to propose an efficient method for self-automated segmentation of the liver using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) through deep active learning. We developed an active learning framework for liver segmentation using labeled and unlabeled data in MRI-PDFF. A total of 77 liver samples on MRI-PDFF were obtained from patients with nonalcoholic fatty liver disease. For the training, tuning, and testing of the liver segmentation, the ground truth of 71 (internal) and 6 (external) MRI-PDFF scans for training and testing were verified by an expert reviewer. For 100 randomly selected slices, manual and deep learning (DL) segmentations for visual assessments were classified, ranging from very accurate to mostly accurate. The dice similarity coefficients for each step were 0.69 ± 0.21, 0.85 ± 0.12, and 0.94 ± 0.01, respectively (p-value = 0.1389 between the first step and the second step or p-value = 0.0144 between the first step and the third step for paired t-test), indicating that active learning provides superior performance compared with non-active learning. The biases in the Bland-Altman plots for each step were − 24.22% (from − 82.76 to − 2.70), − 21.29% (from − 59.52 to 3.06), and − 0.67% (from − 10.43 to 4.06). Additionally, there was a fivefold reduction in the required annotation time after the application of active learning (2 min with, and 13 min without, active learning in the first step). The number of very accurate slices for DL (46 slices) was greater than that for manual segmentations (6 slices). Deep active learning enables efficient learning for liver segmentation on a limited MRI-PDFF. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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