Comparison of MRI- and CT-based semiautomated liver segmentation: a validation study

Gotra, Akshat; Chartrand, Gabriel; Vu, Kim‐Nhien; Vandenbroucke‐Menu, Franck; Massicotte-Tisluck, Karine; Guise, Jacques A. de; Tang, An

doi:10.1007/s00261-016-0912-7

Cited by 21 publications

(10 citation statements)

References 41 publications

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“…Further assessment of the intra‐reader repeatability of total liver volume in an independent cohort was −3 ± 67 (−70, 64) and 12 ± 97 (−85, 109); inter‐reader repeatability was 5 ± 98 (−93, 103) . In another cohort, intra‐reader repeatability was −19 ± 94 (−113, 75) and 30 ± 217 (−187, 247); inter‐reader repeatability was 6 ± 123 (−117, 129) …”

Section: Resultsmentioning

confidence: 95%

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Assessment of treatment response in non‐alcoholic steatohepatitis using advanced magnetic resonance imaging

Lin

Heba

Bettencourt

et al. 2017

Aliment Pharmacol Ther

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Background Magnetic resonance imaging derived measures of liver fat and volume are emerging as accurate, non-invasive imaging biomarkers in non-alcoholic steatohepatitis (NASH). Little is known about these measures in relation to histology longitudinally. Aims This study examines this relationship between MRI-derived proton-density fat-fraction (PDFF), total liver volume (TLV), total liver fat index (TLFI), vs. histology in a NASH trial. Methods This is a secondary analysis of a 24-week randomized, double-blind, placebo-controlled trial of 50 patients with biopsy-proven NASH randomized to oral ezetimibe 10mg daily (n=25) vs. placebo (n=25). Baseline and post-treatment anthropometrics, biochemical profiling, MRI, and biopsies were obtained. Results Baseline mean PDFF correlated strongly with TLFI (Spearman’s ρ=0.94, n=45, P<0.0001) and had good correlation with TLV (ρ=0.57, n=45, P<0.0001). Mean TLV correlated strongly with TLFI (ρ=0.78, n=45, P<0.0001). After 24 weeks, PDFF remained strongly correlated with TLFI (ρ=0.94, n=45, P<0.0001), maintaining good correlation with TLV (ρ=0.51, n=45, P=0.0004). TLV remained strongly correlated with TLFI (ρ=0.74, n=45, P<0.0001). Patients with Grade 1 vs. 3 steatosis had lower PDFF, TLV, and TLFI (P<0.0001, P=0.0003, P<0.0001, respectively). Regression analysis of changes in MRI-PDFF vs. TLV indicates that 10% reduction in MRI-PDFF predicts 257 mL reduction in TLV. Conclusions MRI-PDFF and TLV strongly correlated with TLFI. Decreases in steatosis were associated with an improvement in hepatomegaly. Lower values of these measures reflect lower histologic-steatosis grades. MRI-derived measures of liver fat and volume may be used as dynamic and more responsive imaging biomarkers in a NASH trial than histology. ClinicalTrials.gov number, NCT01766713.

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Section: Resultsmentioning

confidence: 95%

“…28 In another cohort, intra-reader repeatability was À19 AE 94 (À113, 75) and 30 AE 217 (À187, 247); inter-reader repeatability was 6 AE 123 (À117, 129). 29…”

Section: Intra-and Inter-reader Repeatability Of Total Liver Volumementioning

confidence: 99%

Assessment of treatment response in non‐alcoholic steatohepatitis using advanced magnetic resonance imaging

Lin

Heba

Bettencourt

et al. 2017

Aliment Pharmacol Ther

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“…Despite this inherent limitation, we consider the two-dimensional volume measurement to be a practical method since processing three-dimensional volume data requires more time, higher computational capacity, and larger data storage capacity and may potentially increase the need for operators' correction in cases of inaccurate segmentation results in contrast to our approach. Thus, most of the previous studies involving volume measurement of the liver and spleen have utilized two-dimensional CT images (18,(29)(30)(31)(32).…”

Section: Kjronlineorgmentioning

confidence: 99%

Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images

et al. 2020

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Objective: Measurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT) volumetry is considered to be the most reliable noninvasive method for liver and spleen volume measurement, it has limited application in clinical practice due to its time-consuming segmentation process. We aimed to develop and validate a deep learning algorithm (DLA) for fully automated liver and spleen segmentation using portal venous phase CT images in various liver conditions. Materials and Methods: A DLA for liver and spleen segmentation was trained using a development dataset of portal venous CT images from 813 patients. Performance of the DLA was evaluated in two separate test datasets: dataset-1 which included 150 CT examinations in patients with various liver conditions (i.e., healthy liver, fatty liver, chronic liver disease, cirrhosis, and post-hepatectomy) and dataset-2 which included 50 pairs of CT examinations performed at ours and other institutions. The performance of the DLA was evaluated using the dice similarity score (DSS) for segmentation and Bland-Altman 95% limits of agreement (LOA) for measurement of the volumetric indices, which was compared with that of ground truth manual segmentation. Results: In test dataset-1, the DLA achieved a mean DSS of 0.973 and 0.974 for liver and spleen segmentation, respectively, with no significant difference in DSS across different liver conditions (p = 0.60 and 0.26 for the liver and spleen, respectively). For the measurement of volumetric indices, the Bland-Altman 95% LOA was-0.17 ± 3.07% for liver volume and-0.56 ± 3.78% for spleen volume. In test dataset-2, DLA performance using CT images obtained at outside institutions and our institution was comparable for liver (DSS, 0.982 vs. 0.983; p = 0.28) and spleen (DSS, 0.969 vs. 0.968; p = 0.41) segmentation. Conclusion: The DLA enabled highly accurate segmentation and volume measurement of the liver and spleen using portal venous phase CT images of patients with various liver conditions.

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“…Manual liver segmentation is a tedious and time-consuming task, which also is prone to inter-observer variability, in particular for MRI data. Automation of the liver contouring process allows for shorter segmentation times and reduction of measurement subjectivity [3].…”

Section: Introductionmentioning

confidence: 99%

Reducing inter-observer variability and interaction time of MR liver volumetry by combining automatic CNN-based liver segmentation and manual corrections

et al. 2019

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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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Comparison of MRI- and CT-based semiautomated liver segmentation: a validation study

Abstract: MRI-based semiautomated segmentation provides similar repeatability and agreement to CT-based segmentation for total liver volume.

Cited by 21 publications

References 41 publications

Assessment of treatment response in non‐alcoholic steatohepatitis using advanced magnetic resonance imaging

Assessment of treatment response in non‐alcoholic steatohepatitis using advanced magnetic resonance imaging

Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images

Reducing inter-observer variability and interaction time of MR liver volumetry by combining automatic CNN-based liver segmentation and manual corrections

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