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Reducing inter-observer variability and interaction time of MR liver volumetry by combining automatic CNN-based liver segmentation and manual corrections
Open accessAbstract
PURPOSE: To compare manual corrections of liver masks produced by a fully automatic segmentation method based on convolutional neural networks (CNN) with manual routine segmentations in MR images in terms of inter-observer variability and interaction time. METHODS: For testing, patient's precise reference segmentations that fulfill the quality requirements for liver surgery were manually created. One radiologist and two radiology residents were asked to provide manual routine segmentations. We used our automatic segmentation method Liver-Net to produce liver masks for the test cases and asked a radiologist assistant and one further resident to correct the automatic results. All observers were asked to measure their interaction time. Both manual routine and corrected segmentations were compared with the reference annotations. RESULTS: The manual routine segmentations achieved a mean Dice index of 0.95 and a mean relative error (RVE) of 4.7%. The quality of liver masks produced by the Liver-Net was on average 0.95 Dice and 4.5% RVE. Liver masks resulting from manual corrections of automatically generated segmentations compared to routine results led to a significantly lower inter-observer variability (mean per case absolute RVE difference across observers 0.69%) when compared to manual routine ones (2.75%). The mean interaction time was 2 min for manual corrections and 10 min for manual routine segmentations. CONCLUSIONS: The quality of automatic liver segmentations is on par with those from manual routines. Using automatic liver masks in the clinical workflow could lead to a reduction of segmentation time and a more consistent liver volume estimation across different observers.
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The new evaluation scheme measures segmentation error discrepancies based on expert radiologist corrections of automatically produced segmentations and yields smaller observer variability and is closer to actual segmentation errors.
“We introduce a new evaluation scheme to measure segmentation error discrepancies based on an expert radiologist corrections of automatically produced segmentations that yields smaller observer variability and is closer to actual segmentation errors.”
From SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images (2024) · cites this paper as Chlebus et al., 2019
In Materials and methods (Experiments), the paper evaluates automatically generated liver masks after corrections by a radiology resident and a radiologist assistant—not corrections by an expert radiologist. The Abstract (Results) reports lower inter-observer variability for corrected masks than for routine manual segmentations. The paper compares masks with reference segmentations, but does not establish that a new evaluation scheme measures discrepancies closer to actual segmentation errors. (LLM, conf=0.86)
Experimental — accuracy not yet measuredGround truth segmentation created by correcting a segmentation mask is different from the original mask mostly by segmentation error.
“Studies show that ground truth segmentation that was created by correcting a segmentation mask is different from the original mask mostly by segmentation error (Chlebus et al., 2019).”
From SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images (2024) · cites this paper as Chlebus et al., 2019
The Materials and methods (Experiments; page unavailable) say observers corrected major errors in automatic liver masks, supporting the idea that corrections address segmentation errors. But the Methods passage says the study’s reference segmentations were manually created and used to evaluate the corrected masks; it does not identify the corrected masks as ground truth or establish that their differences from the originals were mostly errors. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCorrecting segmentation masks introduces inductive bias to manual segmentation and may result in reduced observer variability compared to manual delineations from scratch.
“Correcting the computed segmentation mask introduces inductive bias to the manual segmentation and may result in reduced observer variability compared to manual delineations from scratch (Chlebus et al., 2019).”
From SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images (2024) · cites this paper as Chlebus et al., 2019
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Metadata
- Authors
- Chlebus, G., Meine, H., Thoduka, S., Abolmaali, N., van Ginneken, B., Hahn, H. K., & Schenk, A.
- Affiliations
- Radboud University Medical Center (NL)
- University of Bremen (DE)
- Städtisches Klinikum Dresden (DE)
- Constructor University (DE)
- Fraunhofer Institute for Digital Medicine (DE)
- Corresponding author
- Chlebus, Grzegorz
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2019-05-20
- Volume
- 14
- Issue
- 5
- Pages
- e0217228
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC6527212
- OpenAlex
- W2945683845
- Cited by
- 56
- References
- 23
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 7 · 2023: 9 · 2022: 15 · 2021: 7 · 2020: 7 · 2019: 1
- FWCI
- 3.35
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion, other-oa)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- Nvidia
- Fraunhofer-Gesellschaft
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- liver segmentation, liver volumetry, interobserver variability, convolutional neural networks, liver volume estimation, Dice index
- MeSH terms
- Algorithms, Carcinoma, Hepatocellular, Humans, Image Interpretation, Computer-Assisted, Liver, Liver Neoplasms, Magnetic Resonance Imaging, Observer Variation, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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