Comparison between manual and automated CT liver volumetry in assessment of the liver volume in living donor liver transplantation

Aim of the work To evaluate the effectiveness and advantages of automated CT volumetry in the assessment of liver volume in living donor liver transplantation and to compare this technique and its results with those calculated from manual volumetry. Materials and Methods This comparative study was conducted on dynamic contrast enhanced hepatic CT scans of 21 potential living liver donors. All potential donors underwent 1st step laboratory investigations to enter the 2nd step investigations for living donor liver transplantation operation. Automated liver volumetry was developed using the Myrian® XP-Liver software. To establish reference standard liver volumes, a radiologist manually traced the contour of the liver on each CT slice. We compared the results obtained with automated with those obtained with the reference standard for this study, manual volumetry. Results The study showed that automated CT liver volumetry achieved excellent agreement with manual volumetry without statistical significance. The average processing times for automated volumetry was 3.09 ± 0.44 min/case, whereas those for manual volumetry were 16.23 ± 0.81 min/case, the difference was statistically significant (p < 0.05). Conclusion Automated CT liver volumetry performed using the Myrian® XP-Liver software can accurately predict the preoperative liver volume and provide acceptable measurements comparable to the gold standard manual volumetry.

Fully automated whole-liver volume quantification on CT-image data: Comparison with manual volumetry using enhanced and unenhanced images as well as two different radiation dose levels and two reconstruction kernels

Objectives To evaluate the accuracy of fully automated liver volume quantification vs. manual quantification using unenhanced as well as enhanced CT-image data as well as two different radiation dose levels and also two image reconstruction kernels. Material and methods The local ethics board gave its approval for retrospective data analysis. Automated liver volume quantification in 300 consecutive livers in 164 male and 103 female oncologic patients (64±12y) performed at our institution (between January 2020 and May 2020) using two different dual-energy helicals: portal-venous phase enhanced, ref. tube current 300mAs (CARE Dose4D) for tube A (100 kV) and ref. 232mAs tube current for tube B (Sn140kV), slice collimation 0.6mm, reconstruction kernel I30f/1, recon. thickness of 0.6mm and 5mm, 80–100 mL iodine contrast agent 350 mg/mL, (flow 2mL/s) and unenhanced ref. tube current 100mAs (CARE Dose4D) for tube A (100 kV) and ref. 77mAs tube current for tube B (Sn140kV), slice collimation 0.6mm (kernel Q40f) were analyzed. The post-processing tool (syngo.CT Liver Analysis) is already FDA-approved. Two resident radiologists with no and 1-year CT-experience performed both the automated measurements independently from each other. Results were compared with those of manual liver volume quantification using the same software which was supervised by a senior radiologist with 30-year CT-experience (ground truth). Results In total, a correlation of 98% was obtained for liver volumetry based on enhanced and unenhanced data sets compared to the manual liver quantification. Radiologist #1 and #2 achieved an inter-reader agreement of 99.8% for manual liver segmentation (p<0.0001). Automated liver volumetry resulted in an overestimation (>5% deviation) of 3.7% for unenhanced CT-image data and 4.0% for contrast-enhanced CT-images. Underestimation (<5%) of liver volume was 2.0% for unenhanced CT-image data and 1.3% for enhanced images after automated liver volumetry. Number and distribution of erroneous volume measurements using either thin or thick slice reconstructions was exactly the same, both for the enhanced as well for the unenhanced image data sets (p> 0.05). Conclusion Results of fully automated liver volume quantification are accurate and comparable with those of manual liver volume quantification and the technique seems to be confident even if unenhanced lower-dose CT image data is used.

A Validation Study of Liver Volumetry Estimation by a Semiautomated Software in Patients Undergoing Hepatic Resections

Purpose The purpose of this study was to validate the use of a semiautomated software for liver volumetry preoperatively by comparing it with the volume of resected specimen in patients undergoing hepatic resections. Materials and Methods This is a single-center retrospective study of patients who underwent estimation of future liver remnant (FLR) using Myrian XP-Liver which is a semiautomated software for hepatectomy. The estimated resection volume, which is the sum of volume of normal liver to be resected and tumor volume, was compared with actual specimen weight to calculate the accuracy of the software. The statistical analysis was performed with SPSS software version 24. Results Data on FLR estimation using the semiautomated software was available for 200 out of 388 patients who underwent formal hepatic resections. The median resected volume of surgical specimen was 650 mL (interquartile range [IQR] 364–950), while the median estimated volume using the Myrian software was 617 mL (IQR 362–979). There was significant correlation between estimated resection volume calculated using the semiautomated method and actual specimen weight (p-value < 0.0001) with the Spearman’s correlation value of 0.956. Conclusion The estimated volume of liver to be resected as calculated by the semiautomated software was accurate and correlated significantly with the volume of resected specimen, and hence, the estimation of FLR volume may likely correlate with the true postoperative residual liver volume. In addition, the software-based liver segmentation, FLR estimation, and color-coded three-dimensional images provide a clear road map to the surgeon to facilitate safe resection.

A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI

Purpose To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning. Materials and Methods Datasets of Gd-EOB-DTPA-enhanced liver MR images of 100 patients were assembled. Ground truth segmentation of the hepatobiliary phase images was performed manually. Automatic image segmentation was achieved with a deep convolutional neural network. Results Our neural network achieves an intraclass correlation coefficient (ICC) of 0.987, a Sørensen–Dice coefficient of 96.7 ± 1.9 % (mean ± std), an overlap of 92 ± 3.5 %, and a Hausdorff distance of 24.9 ± 14.7 mm compared with two expert readers who corresponded to an ICC of 0.973, a Sørensen–Dice coefficient of 95.2 ± 2.8 %, and an overlap of 90.9 ± 4.9 %. A second human reader achieved a Sørensen–Dice coefficient of 95 % on a subset of the test set. Conclusion Our study introduces a fully automated liver volumetry scheme for Gd-EOB-DTPA-enhanced MR imaging. The neural network achieves competitive concordance with the ground truth regarding ICC, Sørensen–Dice, and overlap compared with manual segmentation. The neural network performs the task in just 60 seconds. Key Points: Citation Format