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Active Learning for Efficient Segmentation of Liver with Convolutional Neural Network–Corrected Labeling in Magnetic Resonance Imaging–Derived Proton Density Fat Fraction

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Abstract

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.

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Availability of Data and Material and Code Availability

Even though the limitations of our dataset (KUAH and KUANH) for public use are regulated by the Personal Information Protection Act in South Korea, we could expect to share our datasets and source code as requested.

Abbreviations

BLS:

Boundary loss

BCE:

Binary cross-entropy

CNN:

Convolutional neural network

CRFs:

Conditional random fields

CT:

Computed tomography

DL:

Deep learning

DLS:

Dice loss

DSC:

Dice similarity coefficient

FCN:

Fully convolutional network

MRI-PDFF:

Magnetic resonance imaging–derived proton density fat fraction

RPN:

Region proposal network

3D:

Three dimensional

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Funding

The authors received funding for this study from the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (2020R1I1A1A01071600).

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Authors and Affiliations

  1. Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea

    Yongwon Cho, Min Ju Kim, Beom Jin Park, Ki Choon Sim, Yeo Eun Han, Deuk Jae Sung & Na Yeon Han

  2. Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123, Jeokgeum-ro, Ansan-si, 425-707, Republic of Korea

    Yeom Suk Keu

  3. AI Center, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea

    Yongwon Cho

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  1. Yongwon Cho
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Contributions

Y.C. and M.J.K. wrote the main manuscript. Y.C. performed the experiments and prepared the figures. B.J.P., K.C.S., Y.S.K., Y.E.H., D.J.S., and N.Y.H. prepared the dataset and confirmed the datasets. M.J.K. confirmed the datasets. All authors reviewed the manuscript. All authors were involved in writing the paper and approved the final submitted and published versions.

Corresponding author

Correspondence to Min Ju Kim.

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Ethics Approval

Our institutional review board approved this retrospective case-controlled study, and informed consent was waived. Experiments involving humans and/or the use of human tissue samples have not been performed in this study. In addition, no organs/tissues were procured from prisoners in this study.

Conflict of Interest

The authors declare no competing interests.

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Key Points

• Our study enables efficient self-automated segmentation of the liver in magnetic resonance imaging–derived proton density fat fraction through deep active learning

• Active learning helped improve abdominal liver segmentation where limited clinical training datasets were available. The results of the 3D nnU-net with active learning were better than those of the 2D nnU-net with active learning

• Our results demonstrate that a deep active learning framework (human-in-the-loop) can lower the cost and effort of annotation by efficiently training using limited 3D MRI-PDFF datasets

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Cho, Y., Kim, M.J., Park, B.J. et al. Active Learning for Efficient Segmentation of Liver with Convolutional Neural Network–Corrected Labeling in Magnetic Resonance Imaging–Derived Proton Density Fat Fraction. J Digit Imaging 34, 1225–1236 (2021). https://doi.org/10.1007/s10278-021-00516-4

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  • DOI: https://doi.org/10.1007/s10278-021-00516-4

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