Liver segmentation in living liver transplant donors: comparison of semiautomatic and manual methods

Evaluation of Various Methods of Liver Measurement in Comparison to Volumetric Segmentation Based on Computed Tomography

Background: A reliable assessment of liver volume, necessary before transplantation, remains a challenge. Our work aimed to assess the differences in the evaluation and measurements of the liver between independent observers and compare different formulas calculating its volume in relation to volumetric segmentation. Methods: Eight researchers measured standard liver dimensions based on 105 abdominal computed tomography (CT) scans. Based on the results obtained, the volume of the liver was calculated using twelve different methods. An independent observer performed a volumetric segmentation of the livers based on the same CT examinations. Results: Significant differences were found between the formulas and in relation to volumetric segmentation, with the closest results obtained for the Heinemann et al. method. The measurements of individual observers differed significantly from one another. The observers also rated different numbers of livers as enlarged. Conclusions: Due to significant differences, despite its time-consuming nature, the use of volumetric liver segmentation in the daily assessment of liver volume seems to be the most accurate method.

The impact of hepatic and splenic volumetric assessment in imaging for chronic liver disease: a narrative review

Chronic liver disease is responsible for significant morbidity and mortality worldwide. Abdominal computed tomography (CT) and magnetic resonance imaging (MRI) can fully visualise the liver and adjacent structures in the upper abdomen providing a reproducible assessment of the liver and biliary system and can detect features of portal hypertension. Subjective interpretation of CT and MRI in the assessment of liver parenchyma for early and advanced stages of fibrosis (pre-cirrhosis), as well as severity of portal hypertension, is limited. Quantitative and reproducible measurements of hepatic and splenic volumes have been shown to correlate with fibrosis staging, clinical outcomes, and mortality. In this review, we will explore the role of volumetric measurements in relation to diagnosis, assessment of severity and prediction of outcomes in chronic liver disease patients. We conclude that volumetric analysis of the liver and spleen can provide important information in such patients, has the potential to stratify patients' stage of hepatic fibrosis and disease severity, and can provide critical prognostic information. CRITICAL RELEVANCE STATEMENT: This review highlights the role of volumetric measurements of the liver and spleen using CT and MRI in relation to diagnosis, assessment of severity, and prediction of outcomes in chronic liver disease patients. KEY POINTS: Volumetry of the liver and spleen using CT and MRI correlates with hepatic fibrosis stages and cirrhosis. Volumetric measurements correlate with chronic liver disease outcomes. Fully automated methods for volumetry are required for implementation into routine clinical practice.

Liver volumetry and liver-regenerative interventions: history, rationale, and emerging tools

BACKGROUND

Postoperative hepatic insufficiency (PHI) is the most feared complication after hepatectomy. Volume of the future liver remnant (FLR) is one objectively measurable indicator to identify patients at risk of PHI. In this review, we summarized the development and rationale for the use of liver volumetry and liver-regenerative interventions and highlighted emerging tools that could yield new advancements in liver volumetry.

METHODS

A review of MEDLINE/PubMed, Embase, and Cochrane Library databases was conducted to identify literature related to liver volumetry. The references of relevant articles were reviewed to identify additional publications.

RESULTS

Liver volumetry based on radiologic imaging was developed in the 1980s to identify patients at risk of PHI and later used in the 1990s to evaluate grafts for living donor living transplantation. The field evolved in the 2000s by the introduction of standardized FLR based on the hepatic metabolic demands and in the 2010s by the introduction of the degree of hypertrophy and kinetic growth rate as measures of the FLR regenerative and functional capacity. Several liver-regenerative interventions, most notably portal vein embolization, are used to increase resectability and reduce the risk of PHI. In parallel with the increase in automation and machine assistance to physicians, many semi- and fully automated tools are being developed to facilitate liver volumetry.

CONCLUSION

Liver volumetry is the most reliable tool to detect patients at risk of PHI. Advances in imaging analysis technologies, newly developed functional measures, and liver-regenerative interventions have been improving our ability to perform safe hepatectomy.

CT volume analysis in living donor liver transplantation: accuracy of three different approaches

OBJECTIVES

The aim of this retrospective study is to compare and evaluate accuracy of three different approaches of liver volume quantification in living donor transplantations.

METHODS

This is a single-center, retrospective study of 60 donors. The total and right lobe liver volumes were analyzed in the portal-venous phase by two independent radiologists who estimated the volumes using manual, semi-automated and automated segmentation methods. The measured right lobe liver volume was compared to the real weight of the graft after back-table examinations.

RESULTS

The mean estimated overall liver volume was 1164.4 ± 137.0 mL for manual, 1277.4 ± 190.4 mL for semi-automated and 1240.1 ± 108.5 mL for automated segmentation. The mean estimated right lobe volume was 762.0 ± 122.4 mL for manual, 792.9 ± 139.9 mL for semi-automated and 765.4 ± 132.7 mL for automated segmentation. The mean graft weight was 711.2 ± 142.9 g. The manual method better correlated with the graft weight (r = 0.730) in comparison with the semi-automated (r = 0.685) and the automated (r = 0.699) methods (p < 0.001). The mean error ratio in volume estimation by each application was 12.7 ± 16.6% for manual, 17.1 ± 17.3% for semi-automated, 14.7 ± 16.8% for automated methods. There was a statistically significant difference between the mean error ratio of the manual and the semi-automated segmentations (p = 0.017), and no statistically significant difference between the manual and the automated applications (p = 0.199).

CONCLUSION

Volume analysis application better correlates with graft weight, but there is no obvious difference between correlation coefficients of all three methods. All three modalities had an error ratio, of which the semi-automated method showed the highest value.

CRITICAL RELEVANCE STATEMENT

Volume analysis application was more accurate, but there is no drastic difference between correlation coefficients of all three methods.

Fully Automated and Explainable Liver Segmental Volume Ratio and Spleen Segmentation at CT for Diagnosing Cirrhosis

PURPOSE

To evaluate the performance of a deep learning (DL) model that measures the liver segmental volume ratio (LSVR) (ie, the volumes of Couinaud segments I-III/IV-VIII) and spleen volumes from CT scans to predict cirrhosis and advanced fibrosis.

MATERIALS AND METHODS

For this Health Insurance Portability and Accountability Act-compliant, retrospective study, two datasets were used. Dataset 1 consisted of patients with hepatitis C who underwent liver biopsy (METAVIR F0-F4, 2000-2016). Dataset 2 consisted of patients who had cirrhosis from other causes who underwent liver biopsy (Ishak 0-6, 2001-2021). Whole liver, LSVR, and spleen volumes were measured with contrast-enhanced CT by radiologists and the DL model. Areas under the receiver operating characteristic curve (AUCs) for diagnosing advanced fibrosis (≥METAVIR F2 or Ishak 3) and cirrhosis (≥METAVIR F4 or Ishak 5) were calculated. Multivariable models were built on dataset 1 and tested on datasets 1 (hold out) and 2.

RESULTS

Datasets 1 and 2 consisted of 406 patients (median age, 50 years [IQR, 44-56 years]; 297 men) and 207 patients (median age, 50 years [IQR, 41-57 years]; 147 men), respectively. In dataset 1, the prediction of cirrhosis was similar between the manual versus automated measurements for spleen volume (AUC, 0.86 [95% CI: 0.82, 0.9] vs 0.85 [95% CI: 0.81, 0.89]; significantly noninferior, P < .001) and LSVR (AUC, 0.83 [95% CI: 0.78, 0.87] vs 0.79 [95% CI: 0.74, 0.84]; P < .001). The best performing multivariable model achieved AUCs of 0.94 (95% CI: 0.89, 0.99) and 0.79 (95% CI: 0.71, 0.87) for cirrhosis and 0.8 (95% CI: 0.69, 0.91) and 0.71 (95% CI: 0.64, 0.78) for advanced fibrosis in datasets 1 and 2, respectively.

CONCLUSION

The CT-based DL model performed similarly to radiologists. LSVR and splenic volume were predictive of advanced fibrosis and cirrhosis.Keywords: CT, Liver, Cirrhosis, Computer Applications-Detection/Diagnosis Supplemental material is available for this article. © RSNA, 2022.

Validation of a deep learning segmentation algorithm to quantify the skeletal muscle index and sarcopenia in metastatic renal carcinoma

OBJECTIVES

To validate a deep learning (DL) algorithm for measurement of skeletal muscular index (SMI) and prediction of overall survival in oncology populations.

METHODS

A retrospective single-center observational study included patients with metastatic renal cell carcinoma between 2007 and 2019. A set of 37 patients was used for technical validation of the algorithm, comparing manual vs DL-based evaluations. Segmentations were compared using mean Dice similarity coefficient (DSC), SMI using concordance correlation coefficient (CCC) and Bland-Altman plots. Overall survivals (OS) were compared using log-rank (Kaplan-Meier) and Mann-Whitney tests. Generalizability of the prognostic value was tested in an independent validation population (N = 87).

RESULTS

Differences between two manual segmentations (DSC = 0.91, CCC = 0.98 for areas) or manual vs. automated segmentation (DSC = 0.90, CCC = 0.98 for areas, CCC = 0.97 for SMI) had the same order of magnitude. Bland-Altman plots showed a mean difference of -3.33 cm² [95%CI: -15.98, 9.1] between two manual segmentations, and -3.28 cm² [95% CI: -14.77, 8.21] for manual vs. automated segmentations. With each method, 20/37 (56%) patients were classified as sarcopenic. Sarcopenic vs. non-sarcopenic groups had statistically different survival curves with median OS of 6.0 vs. 12.5 (p = 0.008) and 6.0 vs. 13.9 (p = 0.014) months respectively for manual and DL methods. In the independent validation population, sarcopenic patients according to DL had a lower OS (10.7 vs. 17.3 months, p = 0.033).

CONCLUSION

A DL algorithm allowed accurate estimation of SMI compared to manual reference standard. The DL-calculated SMI demonstrated a prognostic value in terms of OS.

KEY POINTS

• A deep learning algorithm allows accurate estimation of skeletal muscle index compared to a manual reference standard with a concordance correlation coefficient of 0.97. • Sarcopenic patients according to SMI thresholds after segmentation by the deep learning algorithm had statistically significantly lower overall survival compared to non-sarcopenic patients.

Accuracy and Efficiency of Right-Lobe Graft Weight Estimation Using Deep-Learning-Assisted CT Volumetry for Living-Donor Liver Transplantation

CT volumetry (CTV) has been widely used for pre-operative graft weight (GW) estimation in living-donor liver transplantation (LDLT), and the use of a deep-learning algorithm (DLA) may further improve its efficiency. However, its accuracy has not been well determined. To evaluate the efficiency and accuracy of DLA-assisted CTV in GW estimation, we performed a retrospective study including 581 consecutive LDLT donors who donated a right-lobe graft. Right-lobe graft volume (GV) was measured on CT using the software implemented with the DLA for automated liver segmentation. In the development group (n = 207), a volume-to-weight conversion formula was constructed by linear regression analysis between the CTV-measured GV and the intraoperative GW. In the validation group (n = 374), the agreement between the estimated and measured GWs was assessed using the Bland–Altman 95% limit-of-agreement (LOA). The mean process time for GV measurement was 1.8 ± 0.6 min (range, 1.3–8.0 min). In the validation group, the GW was estimated using the volume-to-weight conversion formula (estimated GW [g] = 206.3 + 0.653 × CTV-measured GV [mL]), and the Bland–Altman 95% LOA between the estimated and measured GWs was −1.7% ± 17.1%. The DLA-assisted CT volumetry allows for time-efficient and accurate estimation of GW in LDLT.

Imaging Evaluation of Living Liver Donor Candidates: Techniques, Protocols, and Anatomy

The need for liver transplants is increasing because the prevalence of liver diseases and the indications for transplants are growing. In response to the shortage of grafts from deceased donors, more transplants are being performed worldwide with grafts from living donors. Radiologic evaluation is an integral component in the assessment of donor candidates to ensure their eligibility and to choose the most appropriate surgical approach. MRI is the preferred modality for evaluation of the liver parenchyma and biliary tree. In most centers, a combination of MRI and CT is used to take advantage of the higher spatial resolution of CT for evaluation of arteries. However, MRI-only assessment is feasible. In addition to assessment of the liver parenchyma for abnormalities such as steatosis, a detailed evaluation of the hepatic vascular and biliary system for pertinent anatomic variants is crucial, because these variants can affect surgical techniques and outcomes in both recipients and donors. In this pictorial article, after a brief review of the most common surgical techniques and postsurgical liver anatomy, the biliary and vascular anatomy are discussed, with specific attention paid to the variants that are pertinent to this surgical procedure. The roles of liver segmentation and volumetric assessment and current imaging techniques and protocols are also discussed. Online supplemental material is available for this article. ©RSNA, 2021.

Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters

Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.

Automated Contouring of Contrast and Noncontrast Computed Tomography Liver Images With Fully Convolutional Networks

PURPOSE

The deformable nature of the liver can make focal treatment challenging and is not adequately addressed with simple rigid registration techniques. More advanced registration techniques can take deformations into account (eg, biomechanical modeling) but require segmentations of the whole liver for each scan, which is a time-intensive process. We hypothesize that fully convolutional networks can be used to rapidly and accurately autosegment the liver, removing the temporal bottleneck for biomechanical modeling.

METHODS AND MATERIALS

Manual liver segmentations on computed tomography scans from 183 patients treated at our institution and 30 scans from the Medical Image Computing & Computer Assisted Intervention challenges were collected for this study. Three architectures were investigated for rapid automated segmentation of the liver (VGG-16, DeepLabv3 +, and a 3-dimensional UNet). Fifty-six cases were set aside as a final test set for quantitative model evaluation. Accuracy of the autosegmentations was assessed using Dice similarity coefficient and mean surface distance. Qualitative evaluation was also performed by 3 radiation oncologists on 50 independent cases with previously clinically treated liver contours.

RESULTS

The mean (minimum-maximum) mean surface distance for the test groups with the final model, DeepLabv3 +, were as follows: μContrast(N = 17): 0.99 mm (0.47-2.2), μNon_Contrast(N = 19)l: 1.12 mm (0.41-2.87), and μMiccai(N = 30)t: 1.48 mm (0.82-3.96). The qualitative evaluation showed that 30 of 50 autosegmentations (60%) were preferred to manual contours (majority voting) in a blinded comparison, and 48 of 50 autosegmentations (96%) were deemed clinically acceptable by at least 1 reviewing physician.

CONCLUSIONS

The autosegmentations were preferred compared with manually defined contours in the majority of cases. The ability to rapidly segment the liver with high accuracy achieved in this investigation has the potential to enable the efficient integration of biomechanical model-based registration into a clinical workflow.

Assessment of Radiation Dose Delivered and Volume Measurement By Low- and High-Dose Diagnostic Computed Tomography: Anthropomorphic Liver Phantom Study

AIM

Liver volume measurement is a mandatory test before measure liver surgeries and transplantation. We aimed a study on the difference in volume measurement and radiation dose to an anthropomorphic liver phantom using high-dose and low-dose diagnostic computed tomography (CT).

MATERIALS AND METHODS

Several measurements of the manual total volume measurement done on an anthropomorphic liver phantom mounted with thermoluminescent dosimeter. We exposed the phantom with diagnostic CT, low-dose CT, and a low-dose CT with copper filter.

RESULTS

Phantom underwent ten scanning for each exposure. There was no significant difference in the total volume measurement in comparison to the phantom volume. The volume of phantom measured by low-dose CT, low-dose CT with copper phantom, and high-dose CT were 1869 ± 18 cm3, 1852 ± 24 cm3, and 1908 ± 12 cm3, (P = 0.3), respectively. However, the radiation dose delivered was significantly different (1.54 mGy, 0.77 mGy, and 5.84 mGy [P = 0.001], respectively).

CONCLUSION

Total liver volume measurement provides essential clinical information in several clinical conditions. We recommended that the volume measured by a low-dose CT has an excellent correlation with the diagnostic quality CT and should be a routine in the routine clinical practice. CT volumetry achieves the same result while using very less radiation exposure. It may also be used with functional imaging to give complete information.

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

  · The proposed neural network helps to segment the liver accurately, providing detailed information about patient-specific liver anatomy and volume.. · With the help of a deep learning-based neural network, fully automatic segmentation of the liver on MRI scans can be performed in seconds.. · A fully automatic segmentation scheme makes liver segmentation on MRI a valuable tool for treatment planning..

CITATION FORMAT

· Winther H, Hundt C, Ringe KI et al. A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI. Fortschr Röntgenstr 2021; 193: 305 - 314.

Prediction of microvascular invasion of hepatocellular carcinoma: value of volumetric iodine quantification using preoperative dual-energy computed tomography

Background

To investigate the potential value of volumetric iodine quantification using preoperative dual-energy computed tomography (DECT) for predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC).

Methods

This retrospective study included patients with single HCC treated through surgical resection who underwent preoperative DECT. Quantitative DECT features, including normalized iodine concentration (NIC) to the aorta and mixed-energy CT attenuation value in the arterial phase, were three-dimensionally measured for peritumoral and intratumoral regions: (i) layer-by-layer analysis for peritumoral layers (outer layers 1 and 2; numbered in close order from the tumor boundary) and intratumoral layers (inner layers 1 and 2) with 2-mm layer thickness and (ii) volume of interest (VOI)-based analysis with different volume coverage (tumor itself; VOI_O1, tumor plus outer layer 1; VOI_O2, tumor plus outer layers 1 and 2; VOI_I1, tumor minus inner layer 1; VOI_I2, tumor minus inner layers 1 and 2). In addition, qualitative CT features, including peritumoral enhancement and tumor margin, were assessed. Qualitative and quantitative CT features were compared between HCC patients with and without MVI. Diagnostic performance of DECT parameters of layers and VOIs was assessed using receiver operating characteristic curve analysis.

Results

A total of 36 patients (24 men, mean age 59.9 ± 8.5 years) with MVI (n = 14) and without MVI (n = 22) were included. HCCs with MVI showed significantly higher NICs of outer layer 1, outer layer 2, VOI_O1, and VOI_O2 than those without MVI (P = 0.01, 0.04, 0.02, 0.02, respectively). Among the NICs of layers and VOIs, the highest area under the curve was obtained in outer layer 1 (0.747). Qualitative features, including peritumoral enhancement and tumor margin, and the mean CT attenuation of each layer and each VOI were not significantly different between HCCs with and without MVI (both P >  0.05).

Conclusions

Volumetric iodine quantification of peritumoral and intratumoral regions in arterial phase using DECT may help predict the MVI of HCC.

Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation

PURPOSE

To evaluate the performance of an artificial intelligence (AI) based software solution tested on liver volumetric analyses and to compare the results to the manual contour segmentation.

MATERIALS AND METHODS

We retrospectively obtained 462 multiphasic CT datasets with six series for each patient: three different contrast phases and two slice thickness reconstructions (1.5/5 mm), totaling 2772 series. AI-based liver volumes were determined using multi-scale deep-reinforcement learning for 3D body markers detection and 3D structure segmentation. The algorithm was trained for liver volumetry on approximately 5000 datasets. We computed the absolute error of each automatically- and manually-derived volume relative to the mean manual volume. The mean processing time/dataset and method was recorded. Variations of liver volumes were compared using univariate generalized linear model analyses. A subgroup of 60 datasets was manually segmented by three radiologists, with a further subgroup of 20 segmented three times by each, to compare the automatically-derived results with the ground-truth.

RESULTS

The mean absolute error of the automatically-derived measurement was 44.3 mL (representing 2.37 % of the averaged liver volumes). The liver volume was neither dependent on the contrast phase (p = 0.697), nor on the slice thickness (p = 0.446). The mean processing time/dataset with the algorithm was 9.94 s (sec) compared to manual segmentation with 219.34 s. We found an excellent agreement between both approaches with an ICC value of 0.996.

CONCLUSION

The results of our study demonstrate that AI-powered fully automated liver volumetric analyses can be done with excellent accuracy, reproducibility, robustness, speed and agreement with the manual segmentation.

Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors

PURPOSE

To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transplant donors at computerized tomography (CT) imaging.

METHODS

A total of 12 (6 semi-, 6 full-automatic) methods are evaluated. The semi-automatic segmentation algorithms are based on both traditional iterative models including watershed, fast marching, region growing, active contours and modern techniques including robust statistical segmenter and super-pixels. These methods entail some sort of interaction mechanism such as placing initialization seeds on images or determining a parameter range. The automatic methods are based on deep learning and they include three framework templates (DeepMedic, NiftyNet and U-Net) the first two of which are applied with default parameter sets and the last two involve adapted novel model designs. For 20 living donors (6 training and 12 test datasets), a group of imaging scientists and radiologists created ground truths by performing manual segmentations on contrast material-enhanced CT images. Each segmentation is evaluated using five metrics (i.e. volume overlap and relative volume errors, average/RMS/maximum symmetrical surface distances). The results are mapped to a scoring system and a final grade is calculated by taking their average. Accuracy and repeatability were evaluated using slice by slice comparisons and volumetric analysis. Diversity and complementarity are observed through heatmaps. Majority voting and Simultaneous Truth and Performance Level Estimation (STAPLE) algorithms are utilized to obtain the fusion of the individual results.

RESULTS

The top four methods are determined to be automatic deep models having 79.63, 79.46 and 77.15 and 74.50 scores. Intra-user score is determined as 95.14. Overall, deep automatic segmentation outperformed interactive techniques on all metrics. The mean volume of liver of ground truth is found to be 1409.93 mL ± 271.28 mL, while it is calculated as 1342.21 mL ± 231.24 mL using automatic and 1201.26 mL ± 258.13 mL using interactive methods, showing higher accuracy and less variation on behalf of automatic methods. The qualitative analysis of segmentation results showed significant diversity and complementarity enabling the idea of using ensembles to obtain superior results. The fusion of automatic methods reached 83.87 with majority voting and 86.20 using STAPLE that are only slightly less than fusion of all methods that achieved 86.70 (majority voting) and 88.74 (STAPLE).

CONCLUSION

Use of the new deep learning based automatic segmentation algorithms substantially increases the accuracy and repeatability for segmentation and volumetric measurements of liver. Fusion of automatic methods based on ensemble approaches exhibits best results almost without any additional time cost due to potential parallel execution of multiple models.

Evaluation of liver regeneration and post-hepatectomy liver failure after hemihepatectomy in patients with hepatocellular carcinoma

Aim: To explore clinical factors associated with extent of liver regeneration after hemihepatectomy to treat hepatocellular carcinoma (HCC).

Methods: Future liver remnant volume (as a percentage of functional liver volume, %FLRV) and remnant liver volume were measured preoperatively and at 1, 5, 9, and 13 weeks postoperatively.

Results: After hepatectomy, 1 of 125 patients (0.8%) died within 3 months, 13 (10.4%) experienced liver failure, and 99 (79.2%) experienced complications. %FLRV was able to predict liver failure with an area under the receiver operating characteristic curve of 0.900, and a cut-off value of 42.7% showed sensitivity of 85.7% and specificity of 88.6%. Postoperative median growth ratio was 21.3% at 1 week, 30.9% at 5 weeks, 34.6% at 9 weeks, and 37.1% at 13 weeks. Multivariate analysis identified three predictors associated with liver regeneration: FLRV < 601 cm³, %FLRV, and liver cirrhosis. At postoperative weeks (POWs) 1 and 5, liver function indicators were significantly better among patients showing high extent of regeneration than among those showing low extent, but these differences disappeared by POW 9.

Conclusions: FLRV, %FLRV, and liver cirrhosis strongly influence extent of liver regeneration after hepatectomy. %FLRV values below 42.7% are associated with greater risk of post-hepatectomy liver failure.

A robust multi-variability model based liver segmentation algorithm for CT-scan and MRI modalities

Developing methods to segment the liver in medical images, study and analyze it remains a significant challenge. The shape of the liver can vary considerably from one patient to another, and adjacent organs are visualized in medical images with similar intensities, making the boundaries of the liver ambiguous. Consequently, automatic or semi-automatic segmentation of liver is a difficult task. Moreover, scanning systems and magnetic resonance imaging have different settings and parameters. Thus the images obtained differ from one machine to another. In this article, we propose an automatic model-based segmentation that allows building a faithful 3-D representation of the liver, with a mean Dice value equal to 90.3% on CT and MRI datasets. We compare our algorithm with a semi-automatic method and with other approaches according to the state of the art. Our method works with different data sources, we use a large quantity of CT and MRI images from machines in various hospitals and multiple DICOM images available from public challenges. Finally, for evaluation of liver segmentation approaches in state of the art, robustness is not adequacy addressed with a precise definition. Another originality of this article is the introduction of a novel measure of robustness, which takes into account the liver variability at different scales.

Automatic segmentation methods for liver and hepatic vessels from CT and MRI volumes, applied to the Couinaud scheme

BACKGROUND

Proper segmentation of the liver from medical images is critical for computer-assisted diagnosis, therapy and surgical planning. Knowledge of its vascular structure allows division of the liver into eight functionally independent segments, each with its own vascular inflow, known as the Couinaud scheme. Couinaud's description is the most widely used classification, since it is well-suited for surgery and accurate for the localization of lesions. However, automatic segmentation of the liver and its vascular structure to construct the Couinaud scheme remains a challenging task.

METHODS

We present a complete framework to obtain Couinaud's classification in three main steps; first, we propose a model-based liver segmentation, then a vascular segmentation based on a skeleton process, and finally, the construction of the eight independent liver segments. Our algorithms are automatic and allow 3D visualizations.

RESULTS

We validate these algorithms on various databases with different imaging modalities (Magnetic Resonance Imaging (MRI) and Computed Tomography (CT)). Experimental results are presented on diseased livers, which pose complex challenges because both the overall organ shape and the vessels can be severely deformed. A mean DICE score of 0.915 is obtained for the liver segmentation, and an average accuracy of 0.98 for the vascular network. Finally, we present an evaluation of our method for performing the Couinaud segmentation thanks to medical reports with promising results.

CONCLUSIONS

We were able to automatically reconstruct 3-D volumes of the liver and its vessels on MRI and CT scans. Our goal is to develop an improved method to help radiologists with tumor localization.

Should We Have Blind Faith in Liver Volumetry?

Introduction: Liver volumetry is a routine procedure performed before major hepatectomy or living donor liver transplantation (LDLT) to anticipate the remnant liver volume and prevent liver failure. However, many parameters may impact its accuracy and no large-scale studies have evaluated inter-rater variabilities. We aimed to determine the reliability of volumetric assessments for whole organs in deceased-donor liver transplantations (DDLT) and partial organs in LDLT settings. Patients & Methods: Eight operators (four surgeons + four radiologists) analysed 30 preoperative CT scans (15 whole cirrhotic livers in the DDLT group + 15 partial healthy grafts in the LDLT group), using five software systems. The computed volumes were compared with liver weight; liver density being considered as1. Results: Inter-rater and inter-software concordances were excellent with coefficients of correlation >0.9. However, calculations overestimated the real volumes in 25 cases by a mean of 249 ± 206 [14-771] cc in the DDLT group and 138 ± 92cc [39-375] in the LDLT group. The mean calculations were significantly higher than liver weights in the LDLT group only (p=0.04). The radiologists overestimated the surgeons’ assessment in 24 cases, the differences exceeding 6% in some cases. The type of software used significantly impacted results in the DDLTgroup. Conclusions: Despite its unanimously recognised utility, we highlight significant discrepancies between estimated and real liver volumes. The global overestimation may lead to leave of too small remnant liver, with potentially dramatic consequences. In case of border-line estimations, we recommend a repetition of the evaluation by another operator (surgeon + radiologist working in concert).

Short-term reproducibility of radiomic features in liver parenchyma and liver malignancies on contrast-enhanced CT imaging

PURPOSE

To evaluate the short-term reproducibility of radiomic features in liver parenchyma and liver cancers in patients who underwent consecutive contrast-enhanced CT (CECT) with intravenous iodinated contrast within 2 weeks by chance.

METHODS

The Institutional Review Board approved this HIPAA-compliant retrospective study and waived the requirement for patients' informed consent. Patients were included if they had a liver malignancy (liver metastasis, n = 22, intrahepatic cholangiocarcinoma, n = 10, and hepatocellular carcinoma, n = 6), had two consecutive CECT within 14 days, and had no prior or intervening therapy. Liver tumors and liver parenchyma were segmented and radiomic features (n = 254) were extracted. The number of reproducible features (with concordance correlation coefficients > 0.9) was calculated for patient subgroups with different variations in contrast injection rate and pixel resolution.

RESULTS

The number of reproducible radiomic features decreased with increasing variations in contrast injection rate and pixel resolution. When including all CECTs with injection rates differences of less than 15% vs. up to 50%, 63/254 vs. 0/254 features were reproducible for liver parenchyma and 68/254 vs. 50/254 features were reproducible for malignancies. When including all CT with pixel resolution differences of 0-5% or 0-15%, 20/254 vs. 0/254 features were reproducible for liver parenchyma; 34/254 liver malignancy features were reproducible with pixel differences up to 15%.

CONCLUSION

A greater number of liver malignancy radiomic features were reproducible compared to liver parenchyma features, but the proportion of reproducible features decreased with increasing variations in contrast injection rates and pixel resolution.

Accuracy of preoperative CT liver volumetry in living donor hepatectomy and its clinical implications

Background

An accurate preoperative volumetric assessment of donor liver is essential for successful living donor liver transplant by ensuring adequate remnant and graft recipient weight ratio (GRWR).

Methods

The study cohort consisted of 744 right lobe (RL), 65 left lobe (LL) and 33 left lateral sector (LLS) grafts from July 2010 to January 2014. A semi-automated interactive commercial software called AW Volume share 6 was used for volumetry. Bland Altman plot was used for assessing the agreement between estimated graft weight (EGW) and actual graft weight (AGW).

Results

There was no statistically significant difference between EGW and AGW for RL graft weight (722±134 vs. 717±126 gm; P=0.06). Although Bland Altman graph showed that 95% limits of agreement was more in LL (-164 to +110) than RL (-156 to +147) and LLS grafts (-137 to +239), CT scan significantly overestimated LL graft weight (EGW =460±118 gm vs. AGW =433±102 gm; P=0.003) and underestimated LLS graft weight (EGW =203±48 gm vs. AGW =254±49 gm; P<0.001).

Conclusions

CT volumetry overestimate LL graft and underestimate LLS graft weight. This should be factored in when selecting LL graft by taking higher GRWR.

Digital liver biopsy: Bio-imaging of fatty liver for translational and clinical research

The rapidly growing field of functional, molecular and structural bio-imaging is providing an extraordinary new opportunity to overcome the limits of invasive liver biopsy and introduce a "digital biopsy" for in vivo study of liver pathophysiology. To foster the application of bio-imaging in clinical and translational research, there is a need to standardize the methods of both acquisition and the storage of the bio-images of the liver. It can be hoped that the combination of digital, liquid and histologic liver biopsies will provide an innovative synergistic tri-dimensional approach to identifying new aetiologies, diagnostic and prognostic biomarkers and therapeutic targets for the optimization of personalized therapy of liver diseases and liver cancer. A group of experts of different disciplines (Special Interest Group for Personalized Hepatology of the Italian Association for the Study of the Liver, Institute for Biostructures and Bio-imaging of the National Research Council and Bio-banking and Biomolecular Resources Research Infrastructure) discussed criteria, methods and guidelines for facilitating the requisite application of data collection. This manuscript provides a multi-Author review of the issue with special focus on fatty liver.

Implementing Radiation Dose-Volume Liver Response in Biomechanical Deformable Image Registration

PURPOSE

Understanding anatomic and functional changes in the liver resulting from radiation therapy is fundamental to the improvement of normal tissue complication probability models needed to advance personalized medicine. The ability to link pretreatment and posttreatment imaging is often compromised by significant dose-dependent volumetric changes within the liver that are currently not accounted for in deformable image registration (DIR) techniques. This study investigated using delivered dose, in combination with other patient factors, to biomechanically model longitudinal changes in liver anatomy for follow-up care and re-treatment planning.

METHODS AND MATERIALS

Population models describing the relationship between dose and hepatic volume response were produced using retrospective data from 33 patients treated with focal radiation therapy. A DIR technique was improved by implementing additional boundary conditions associated with the dose-volume response in series with a previously developed biomechanical DIR algorithm. Evaluation of this DIR technique was performed on computed tomography imaging from 7 patients by comparing the model-predicted volumetric change within the liver with the observed change, tracking vessel bifurcations within the liver through the deformation process, and then determining target registration error between the predicted and identified posttreatment bifurcation points.

RESULTS

Evaluation of the proposed DIR technique showed that all lobes were volumetrically deformed to within the respective contour variability of each lobe. The average target registration error achieved was 7.3 mm (2.8 mm left-right and anterior-posterior and 5.1 mm superior-inferior), with the superior-inferior component within the average limiting slice thickness (6.0 mm). This represented a significant improvement (P<.01, Wilcoxon test) over the application of the previously published biomechanical DIR algorithm (10.9 mm).

CONCLUSIONS

This study demonstrates the feasibility of implementing dose-driven volumetric response in deformable registration, enabling improved accuracy of modeling liver anatomy changes, which could allow for improved dose accumulation, particularly for patients who require additional liver radiation therapy.

Liver segmentation: indications, techniques and future directions

OBJECTIVES

Liver volumetry has emerged as an important tool in clinical practice. Liver volume is assessed primarily via organ segmentation of computed tomography (CT) and magnetic resonance imaging (MRI) images. The goal of this paper is to provide an accessible overview of liver segmentation targeted at radiologists and other healthcare professionals.

METHODS

Using images from CT and MRI, this paper reviews the indications for liver segmentation, technical approaches used in segmentation software and the developing roles of liver segmentation in clinical practice.

RESULTS

Liver segmentation for volumetric assessment is indicated prior to major hepatectomy, portal vein embolisation, associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) and transplant. Segmentation software can be categorised according to amount of user input involved: manual, semi-automated and fully automated. Manual segmentation is considered the "gold standard" in clinical practice and research, but is tedious and time-consuming. Increasingly automated segmentation approaches are more robust, but may suffer from certain segmentation pitfalls. Emerging applications of segmentation include surgical planning and integration with MRI-based biomarkers.

CONCLUSIONS

Liver segmentation has multiple clinical applications and is expanding in scope. Clinicians can employ semi-automated or fully automated segmentation options to more efficiently integrate volumetry into clinical practice.

TEACHING POINTS

• Liver volume is assessed via organ segmentation on CT and MRI examinations. • Liver segmentation is used for volume assessment prior to major hepatic procedures. • Segmentation approaches may be categorised according to the amount of user input involved. • Emerging applications include surgical planning and integration with MRI-based biomarkers.

The correlation between preoperative volumetry and real graft weight: comparison of two volumetry programs

Purpose

Liver volumetry is a vital component in living donor liver transplantation to determine an adequate graft volume that meets the metabolic demands of the recipient and at the same time ensures donor safety. Most institutions use preoperative contrast-enhanced CT image-based software programs to estimate graft volume. The objective of this study was to evaluate the accuracy of 2 liver volumetry programs (Rapidia vs. Dr. Liver) in preoperative right liver graft estimation compared with real graft weight.

Methods

Data from 215 consecutive right lobe living donors between October 2013 and August 2015 were retrospectively reviewed. One hundred seven patients were enrolled in Rapidia group and 108 patients were included in the Dr. Liver group. Estimated graft volumes generated by both software programs were compared with real graft weight measured during surgery, and further classified into minimal difference (≤15%) and big difference (>15%). Correlation coefficients and degree of difference were determined. Linear regressions were calculated and results depicted as scatterplots.

Results

Minimal difference was observed in 69.4% of cases from Dr. Liver group and big difference was seen in 44.9% of cases from Rapidia group (P = 0.035). Linear regression analysis showed positive correlation in both groups (P < 0.01). However, the correlation coefficient was better for the Dr. Liver group (R² = 0.719), than for the Rapidia group (R² = 0.688).

Conclusion

Dr. Liver can accurately predict right liver graft size better and faster than Rapidia, and can facilitate preoperative planning in living donor liver transplantation.

Volume change of segments II and III of the liver after gastrectomy in patients with gastric cancer

PURPOSE

We aimed to evaluate the relationship between gastrectomy and the volume of liver segments II and III in patients with gastric cancer.

METHODS

Computed tomography images of 54 patients who underwent curative gastrectomy for gastric adenocarcinoma were retrospectively evaluated by two blinded observers. Volumes of the total liver and segments II and III were measured. The difference between preoperative and postoperative volume measurements was compared.

RESULTS

Total liver volumes measured by both observers in the preoperative and postoperative scans were similar (P > 0.05). High correlation was found between both observers (preoperative r=0.99; postoperative r=0.98). Total liver volumes showed a mean reduction of 13.4% after gastrectomy (P = 0.977). The mean volume of segments II and III showed similar decrease in measurements of both observers (38.4% vs. 36.4%, P = 0.363); the correlation between the observers were high (preoperative r=0.97, P < 0.001; postoperative r=0.99, P < 0.001). Volume decrease in the rest of the liver was not different between the observers (8.2% vs. 9.1%, P = 0.388). Time had poor correlation with volume change of segments II and III and the total liver for each observer (observer 1, rseg2/3=0.32, rtotal=0.13; observer 2, rseg2/3=0.37, rtotal=0.16).

CONCLUSION

Segments II and III of the liver showed significant atrophy compared with the rest of the liver and the total liver after gastrectomy. Volume reduction had poor correlation with time.

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

PURPOSE

To compare the repeatability, agreement, and efficiency of MRI- and CT-based semiautomated liver segmentation for the assessment of total and subsegmental liver volume.

METHODS

This retrospective study was conducted in 31 subjects who underwent contemporaneous liver MRI and CT. Total and subsegmental liver volumes were segmented from contrast-enhanced 3D gradient-recalled echo MRI sequences and CT images. Semiautomated segmentation was based on variational interpolation and Laplacian mesh optimization. All segmentations were repeated after 2 weeks. Manual segmentation of CT images using an active contour tool was used as the reference standard. Repeatability and agreement of the methods were evaluated with intra-class correlation coefficients (ICC) and Bland-Altman analysis. Total interaction time was recorded.

RESULTS

Intra-reader ICC were ≥0.987 for MRI and ≥0.995 for CT. Intra-reader repeatability was 30 ± 217 ml (bias ± 1.96 SD) (95% limits of agreement: -187 to 247 ml) for MRI and -10 ± 143 ml (-153 to 133 ml) for CT. Inter-method ICC between semiautomated and manual volumetry were ≥0.995 for MRI and ≥0.986 for CT. Inter-method segmental ICC varied between 0.584 and 0.865 for MRI and between 0.596 and 0.890 for CT. Inter-method agreement was -14 ± 136 ml (-150 to 122 ml) for MRI and 50 ± 226 ml (-176 to 276 ml) for CT. Inter-method segmental agreement ranged from 10 ± 47 ml (-37 to 57 ml) to 2 ± 214 ml (-212 to 216 ml) for MRI and 9 ± 45 ml (-36 to 54 ml) to -46 ± 183 ml (-229 to 137 ml) for CT. Interaction time (mean ± SD) was significantly shorter for MRI-based semiautomated segmentation (7.2 ± 0.1 min, p < 0.001) and for CT-based semiautomated segmentation (6.5 ± 0.2 min, p < 0.001) than for CT-based manual segmentation (14.5 ± 0.4 min).

CONCLUSION

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

Cost-effective, personalized, 3D-printed liver model for preoperative planning before laparoscopic liver hemihepatectomy for colorectal cancer metastases

Purpose

Three-dimensional (3D) printing for preoperative planning has been intensively developed in the recent years. However, the implementation of these solutions in hospitals is still difficult due to high costs, extremely expensive industrial-grade printers, and software that is difficult to obtain and learn along with a lack of a defined process. This paper presents a cost-effective technique of preparing 3D-printed liver models that preserves the shape and all of the structures, including the vessels and the tumor, which in the present case is colorectal liver metastasis.

Methods

The patient’s computed tomography scans were used for the separation and visualization of virtual 3D anatomical structures. Those elements were transformed into stereolithographic files and subsequently printed on a desktop 3D printer. The multipart structure was assembled and filled with silicone. The patient underwent subsequent laparoscopic right hemihepatectomy. The entire process is described step-by-step, and only free-to-use and mostly open-source software was used.

Results

As a result, a transparent, full-sized liver model with visible vessels and colorectal metastasis was created for under $150, which—taking into account 3D printer prices—is much cheaper than models presented in previous research papers.

Conclusions

The increased accessibility of 3D models for physicians before complex laparoscopic surgical procedures such as hepatic resections could lead to beneficial breakthroughs in these sophisticated surgeries, as many reports show that these models reduce operative time and improve short term outcomes.

A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer

Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called “digital biopsy,” that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non–small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of <3 minutes per nodule. We simulated additional digital biopsies using morphological procedures. Finally, we compared the features extracted from these digital biopsies with our reference standard using intraclass correlation coefficient (ICC) to characterize robustness. Comparing the reference standard segmentations to our digital biopsies, we found that 84/94 features had an ICC >0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required.

Fast and accurate liver volumetry prior to hepatectomy

BACKGROUND

Volumetric assessment of the liver is essential in the prevention of postresectional liver failure after partial hepatectomy. Currently used methods are accurate but time-consuming. This study aimed to test a new automated method for preoperative volumetric liver assessment.

METHODS

Patients who underwent a contrast enhanced portovenous phase CT-scan prior to hepatectomy in 2012 were included. Total liver volume (TLV) and future remnant liver volume (FRLV) were measured using TeraRecon Aquarius iNtuition(®) (autosegmentation) and OsiriX(®) (manual segmentation) software by two observers for each software package. Remnant liver volume percentage (RLV%) was calculated. Time needed to determine TLV and FRLV was measured. Inter-observer variability was assessed using Bland-Altman plots.

RESULTS

Twenty-seven patients were included. There were no significant differences in measured volumes between OsiriX(®) and iNtuition(®). Moreover, there were significant correlations between the OsiriX(®) observers, the iNtuition(®) observers and between OsiriX(®) and iNtuition(®) post-processing systems (all R(2) > 0.97). The median time needed for complete liver volumetric analysis was 18.4 ± 4.9 min with OsiriX(®) and 5.8 ± 1.7 min using iNtuition(®) (p < 0.001).

CONCLUSION

Both OsiriX(®) and iNtuition(®) liver volumetry are accurate and easily applicable. However, volumetric assessment of the liver with iNtuition(®) auto-segmentation is three times faster compared to manual OsiriX(®) volumetry.

Accuracy of simple approaches to assessing liver volume in radiological imaging

OBJECTIVES

The purpose of the study was to evaluate the accuracy of measured diameters and calculated volume indices for determining liver size and to derive a simple approach for estimating liver volume.

METHODS

Three hundred twenty-nine volunteers (cohort A) were grouped according to liver volume: small (n = 109), medium (n = 110), and large (n = 110). True liver volume was determined by magnetic resonance imaging (MRI) using manual segmentation. Maximum diameters (maxdiam) of the liver and distances in midclavicular line (MCL) were measured. Volume indices were calculated as a simple product of the measured diameters. The calculated volume indices were calibrated to predict true liver volume. Performance of the calibrated method was evaluated in a control group (cohort B) including randomly selected volunteers (n = 110) and a patient group with histopathologically proven parenchymal liver diseases (n = 28).

RESULTS

In cohort A, there was strong correlation between diameters and true liver volume (r s = 0.631-0.823). Calculated volume indices had slightly better correlation (maxdiam r s = 0.903, MCL r s = 0.920). A calibration index was calculated from the volumes and diameters determined in cohort A. Application of this calibration on cohort B verified a very strong correlation between calibrated volume indices and true liver volume (maxdiam r s = 0.920, MCL r s = 0.909). In addition, the low mean difference between predicted liver volume (maxdiam = -70.9 cm(3);MCL = -88.4 cm(3)) and true liver volume confirms that the calibrated method allows accurate assessment of liver volume.

CONCLUSIONS

Both simple diameters and volume indices allow estimating liver size. A simple calibration formula enables prediction of true liver volume without significant expense.

Gd-EOB enhanced MRI T1-weighted 3D-GRE with and without elevated flip angle modulation for threshold-based liver volume segmentation

BACKGROUND

Despite novel software solutions, liver volume segmentation is still a time-consuming procedure and often requires further manual optimization. With the high signal intensity of the liver parenchyma in Gd-EOB enhanced magnetic resonance imaging (MRI), liver volume segmentation may be improved.

PURPOSE

To evaluate the practicability of threshold-based segmentation of the liver volume using Gd-EOB-enhanced MRI including a customized three-dimensional (3D) sequence.

MATERIAL AND METHODS

A total of 20 patients examined with Gd-EOB MRI (hepatobiliary phase T1-weighted (T1W) 3D sequence [VIBE]; flip angle [FA], 10° and 30°) were enrolled in this retrospective study. The datasets were independently processed by two blinded observers (O1 and O2) in two ways: manual (man) and threshold-based (thresh; study method) segmentation of the liver each followed by an optimization step (man+opt and thresh+opt; man+opt [FA10°] served as reference method). Resulting liver volumes and segmentation times were compared. A liver conversion factor was calculated in percent, describing the non-hepatocellular fraction of the total liver volume, i.e. bile ducts and vessels.

RESULTS

Thresh+opt (FA10°) was significantly faster compared to the reference method leading to a median volume overestimation of 4%/8% (P < 0.001). Using thresh+opt (FA30°), segmentation was even faster (P < 0.001) and even reduced median volume deviation of 0%/2% (O1/O2; both P > 0.2). The liver conversion factor was found to be 10%.

CONCLUSION

Threshold-based liver segmentation employing Gd-EOB-enhanced hepatobiliary phase standard T1W 3D sequence is accurate and time-saving. The performance of this approach can be further improved by increasing the FA.

Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma

The purpose of this study is to investigate the utility of obtaining "core samples" of regions in CT volume scans for extraction of radiomic features. We asked four readers to outline tumors in three representative slices from each phase of multiphasic liver CT images taken from 29 patients (1128 segmentations) with hepatocellular carcinoma. Core samples were obtained by automatically tracing the maximal circle inscribed in the outlines. Image features describing the intensity, texture, shape, and margin were used to describe the segmented lesion. We calculated the intraclass correlation between the features extracted from the readers' segmentations and their core samples to characterize robustness to segmentation between readers, and between human-based segmentation and core sampling. We conclude that despite the high interreader variability in manually delineating the tumor (average overlap of 43% across all readers), certain features such as intensity and texture features are robust to segmentation. More importantly, this same subset of features can be obtained from the core samples, providing as much information as detailed segmentation while being simpler and faster to obtain.

MRI-determined liver proton density fat fraction, with MRS validation: Comparison of regions of interest sampling methods in patients with type 2 diabetes

PURPOSE

To assess the agreement between published magnetic resonance imaging (MRI)-based regions of interest (ROI) sampling methods using liver mean proton density fat fraction (PDFF) as the reference standard.

MATERIALS AND METHODS

This retrospective, internal review board-approved study was conducted in 35 patients with type 2 diabetes. Liver PDFF was measured by magnetic resonance spectroscopy (MRS) using a stimulated-echo acquisition mode sequence and MRI using a multiecho spoiled gradient-recalled echo sequence at 3.0T. ROI sampling methods reported in the literature were reproduced and liver mean PDFF obtained by whole-liver segmentation was used as the reference standard. Intraclass correlation coefficients (ICCs), Bland-Altman analysis, repeated-measures analysis of variance (ANOVA), and paired t-tests were performed.

RESULTS

ICC between MRS and MRI-PDFF was 0.916. Bland-Altman analysis showed excellent intermethod agreement with a bias of -1.5 ± 2.8%. The repeated-measures ANOVA found no systematic variation of PDFF among the nine liver segments. The correlation between liver mean PDFF and ROI sampling methods was very good to excellent (0.873 to 0.975). Paired t-tests revealed significant differences (P < 0.05) with ROI sampling methods that exclusively or predominantly sampled the right lobe. Significant correlations with mean PDFF were found with sampling methods that included higher number of segments, total area equal or larger than 5 cm(2) , or sampled both lobes (P = 0.001, 0.023, and 0.002, respectively).

CONCLUSION

MRI-PDFF quantification methods should sample each liver segment in both lobes and include a total surface area equal or larger than 5 cm(2) to provide a close estimate of the liver mean PDFF.

Validation of a semiautomated liver segmentation method using CT for accurate volumetry

RATIONALE AND OBJECTIVES

To compare the repeatability and agreement of a semiautomated liver segmentation method with manual segmentation for assessment of total liver volume on CT (computed tomography).

MATERIALS AND METHODS

This retrospective, institutional review board-approved study was conducted in 41 subjects who underwent liver CT for preoperative planning. The major pathologies encountered were colorectal cancer metastases, benign liver lesions and hepatocellular carcinoma. This semiautomated segmentation method is based on variational interpolation and 3D minimal path-surface segmentation. Total and subsegmental liver volumes were segmented from contrast-enhanced CT images in venous phase. Two image analysts independently performed semiautomated segmentations and two other image analysts performed manual segmentations. Repeatability and agreement of both methods were evaluated with intraclass correlation coefficients (ICC) and Bland-Altman analysis. Interaction time was recorded for both methods.

RESULTS

Bland-Altman analysis revealed an intrareader agreement of -1 ± 27 mL (mean ± 1.96 standard deviation) with ICC of 0.999 (P < .001) for manual segmentation and 12 ± 97 mL with ICC of 0.991 (P < .001) for semiautomated segmentation. Bland-Altman analysis revealed an interreader agreement of -4 ± 22 mL with ICC of 0.999 (P < .001) for manual segmentation and 5 ± 98 mL with ICC of 0.991 (P < .001) for semiautomated segmentation. Intermethod agreement was found to be 3 ± 120 mL with ICC of 0.988 (P < .001). Mean interaction time was 34.3 ± 16.7 minutes for the manual method and 8.0 ± 1.2 minutes for the semiautomated method (P < .001).

CONCLUSIONS

A semiautomated segmentation method can substantially shorten interaction time while preserving a high repeatability and agreement with manual segmentation.

Multimodality 3D Tumor Segmentation in HCC Patients Treated with TACE

RATIONALE AND OBJECTIVES

To validate the concordance of a semiautomated multimodality lesion segmentation technique between contrast-enhanced magnetic resonance imaging (CE-MRI), cone-beam computed tomography (CBCT), and multidetector CT (MDCT) in patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE).

MATERIALS AND METHODS

This retrospective analysis included 45 patients with unresectable HCC who underwent baseline CE-MRI within 1 month before the treatment, intraprocedural CBCT during conventional TACE, and MDCT within 24 hours after TACE. Fourteen patients were excluded because of atypical lesion morphology, portal vein invasion, or small lesion size which precluded sufficient lesion visualization. Thirty-one patients with a total of 40 target lesions were included into the analysis. A tumor segmentation software, based on non-Euclidean geometry and theory of radial basis functions, was used to allow for the segmentation of target lesions in 3D on all three modalities. The algorithm created image-based masks located in a 3D region whose center and size were defined by the user, yielding the nomenclature "semiautomatic". On the basis of that, tumor volumes on all three modalities were calculated and compared using a linear regression model (R(2) values). Residual plots were used to analyze drift and variance of the values.

RESULTS

The mean value of tumor volumes was 18.72 ± 19.13 cm(3) (range, 0.41-59.16 cm(3)) on CE-MRI, 21.26 ± 21.99 cm(3) (range, 0.62-86.82 cm(3)) on CBCT, and 19.88 ± 20.88 cm(3) (range, 0.45-75.24 cm(3)) on MDCT. The average volumes of the tumor were not significantly different between CE-MRI and DP-CBCT, DP-CBCT and MDCT, MDCT and CE-MRI (P = .577, .770, and .794, respectively). A strong correlation between volumes on CE-MRI and CBCT, CBCT and MDCT, MDCT and CE-MRI was observed (R(2) = 0.974, 0.992 and 0.983, respectively). When plotting the residuals, no drift was observed for all methods showing deviations of no >10% of absolute volumes (in cm(3)).

CONCLUSIONS

A semiautomated 3D segmentation of HCC lesions treated with TACE provides high volumetric concordance across all tested imaging modalities.

Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets

Liver segmentation continues to remain a major challenge, largely due to its intense complexity with surrounding anatomical structures (stomach, kidney, and heart), high noise level and lack of contrast in pathological computed tomography (CT) data. We present an approach to reconstructing the liver surface in low contrast CT. The main contributions are: (1) a stochastic resonance-based methodology in discrete cosine transform domain is developed to enhance the contrast of pathological liver images, (2) a new formulation is proposed to prevent the object boundary, resulting from the cellular automata method, from leaking into the surrounding areas of similar intensity, and (3) a level-set method is suggested to generate intermediate segmentation contours from two segmented slices distantly located in a subject sequence. We have tested the algorithm on real datasets obtained from two sources, Hamad General Hospital and medical image computing and computer-assisted interventions grand challenge workshop. Various parameters in the algorithm, such as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], play imperative roles, thus their values are precisely selected. Both qualitative and quantitative evaluation performed on liver data show promising segmentation accuracy when compared with ground truth data reflecting the potential of the proposed method.

Chemotherapy-induced splenic volume increase is independently associated with major complications after hepatic resection for metastatic colorectal cancer

BACKGROUND

In patients with colorectal cancer liver metastases (CRCLM), chemotherapy-induced hepatic injury is associated with increased splenic volume, thrombocytopenia, and decreased long-term survival. The current study investigates the relationship between change in splenic volume after preoperative chemotherapy and development of postoperative complications.

STUDY DESIGN

The study group consisted of 80 patients who underwent resection of CRCLM; half received neoadjuvant chemotherapy for 6 months before resection (n = 40) and the other half did not (n = 40). The study group was compared with two control groups: a normal group composed of patients undergoing cholecystectomy for benign disease (n = 40) and a group of untreated, nonmetastatic colorectal cancer (CRC) patients (n = 40). Splenic volume was measured by CT/MRI volumetry. In the study group, the nontumoral liver was graded for steatosis and sinusoidal injury; operative and outcomes characteristics were also analyzed.

RESULTS

Before chemotherapy, CRCLM patients had normalized spleen volumes of 3.2 ± 1.1 mL/kg, significantly higher than normal (2.5 ± 0.8 mL/kg; p < 0.001) and nonmetastatic CRC (2.6 ± 1.3 mL/kg; p < 0.05) patients, with higher splenic volume after 6 months of chemotherapy (4.2 ± 1.7 mL/kg; p < 0.01). After chemotherapy, splenic volume increase was associated with any perioperative complication (p < 0.01) and major complications (p < 0.05). Patients with ≥39% splenic volume increase (maximal chi-square test) were significantly more likely to have major complications (p < 0.01). Spleen volume changes were not correlated with change in platelet count (R(2) = 0.03; p = 0.301).

CONCLUSIONS

In patients with CRCLM, the presence of liver metastases and chemotherapy are associated with higher splenic volume. Percent splenic volume increase after 6 months of chemotherapy can aid preoperative risk stratification, as it was an independent predictor of major postoperative complications.

Liver planning software accurately predicts postoperative liver volume and measures early regeneration

BACKGROUND

Postoperative or remnant liver volume (RLV) after hepatic resection is a critical predictor of perioperative outcomes. This study investigates whether the accuracy of liver surgical planning software for predicting postoperative RLV and assessing early regeneration.

STUDY DESIGN

Patients eligible for hepatic resection were approached for participation in the study from June 2008 to 2010. All patients underwent cross-sectional imaging (CT or MRI) before and early after resection. Planned remnant liver volume (pRLV) (based on the planned resection on the preoperative scan) and postoperative actual remnant liver volume (aRLV) (determined from early postoperative scan) were measured using Scout Liver software (Pathfinder Therapeutics Inc.). Differences between pRLV and aRLV were analyzed, controlling for timing of postoperative imaging. Measured total liver volume (TLV) was compared with standard equations for calculating volume.

RESULTS

Sixty-six patients were enrolled in the study from June 2008 to June 2010 at 3 treatment centers. Correlation was found between pRLV and aRLV (r = 0.941; p < 0.001), which improved when timing of postoperative imaging was considered (r = 0.953; p < 0.001). Relative volume deviation from pRLV to aRLV stratified cases according to timing of postoperative imaging showed evidence of measurable regeneration beginning 5 days after surgery, with stabilization at 8 days (p < 0.01). For patients at the upper and lower extremes of liver volumes, TLV was poorly estimated using standard equations (up to 50% in some cases).

CONCLUSIONS

Preoperative virtual planning of future liver remnant accurately predicts postoperative volume after hepatic resection. Early postoperative liver regeneration is measureable on imaging beginning at 5 days after surgery. Measuring TLV directly from CT scans rather than calculating based on equations accounts for extremes in TLV.

Computerized liver volumetry on MRI by using 3D geodesic active contour segmentation

OBJECTIVE

Our purpose was to develop an accurate automated 3D liver segmentation scheme for measuring liver volumes on MRI.

SUBJECTS AND METHODS

Our scheme for MRI liver volumetry consisted of three main stages. First, the preprocessing stage was applied to T1-weighted MRI of the liver in the portal venous phase to reduce noise and produce the boundary-enhanced image. This boundary-enhanced image was used as a speed function for a 3D fast-marching algorithm to generate an initial surface that roughly approximated the shape of the liver. A 3D geodesic-active-contour segmentation algorithm refined the initial surface to precisely determine the liver boundaries. The liver volumes determined by our scheme were compared with those manually traced by a radiologist, used as the reference standard.

RESULTS

The two volumetric methods reached excellent agreement (intraclass correlation coefficient, 0.98) without statistical significance (p = 0.42). The average (± SD) accuracy was 99.4% ± 0.14%, and the average Dice overlap coefficient was 93.6% ± 1.7%. The mean processing time for our automated scheme was 1.03 ± 0.13 minutes, whereas that for manual volumetry was 24.0 ± 4.4 minutes (p < 0.001).

CONCLUSION

The MRI liver volumetry based on our automated scheme agreed excellently with reference-standard volumetry, and it required substantially less completion time.

Multi-institutional quantitative evaluation and clinical validation of Smart Probabilistic Image Contouring Engine (SPICE) autosegmentation of target structures and normal tissues on computer tomography images in the head and neck, thorax, liver, and male pelvis areas

PURPOSE

Clinical validation and quantitative evaluation of computed tomography (CT) image autosegmentation using Smart Probabilistic Image Contouring Engine (SPICE).

METHODS AND MATERIALS

CT images of 125 treated patients (32 head and neck [HN], 40 thorax, 23 liver, and 30 prostate) in 7 independent institutions were autosegmented using SPICE and computational times were recorded. The number of structures autocontoured were 25 for the HN, 7 for the thorax, 3 for the liver, and 6 for the male pelvis regions. Using the clinical contours as reference, autocontours of 22 selected structures were quantitatively evaluated using Dice Similarity Coefficient (DSC) and Mean Slice-wise Hausdorff Distance (MSHD). All 40 autocontours were evaluated by a radiation oncologist from the institution that treated the patients.

RESULTS

The mean computational times to autosegment all the structures using SPICE were 3.1 to 11.1 minutes per patient. For the HN region, the mean DSC was >0.70 for all evaluated structures, and the MSHD ranged from 3.2 to 10.0 mm. For the thorax region, the mean DSC was 0.95 for the lungs and 0.90 for the heart, and the MSHD ranged from 2.8 to 12.8 mm. For the liver region, the mean DSC was >0.92 for all structures, and the MSHD ranged from 5.2 to 15.9 mm. For the male pelvis region, the mean DSC was >0.76 for all structures, and the MSHD ranged from 4.8 to 10.5 mm. Out of the 40 autocontoured structures reviews by experts, 25 were scored useful as autocontoured or with minor edits for at least 90% of the patients and 33 were scored useful autocontoured or with minor edits for at least 80% of the patients.

CONCLUSIONS

Compared with manual contouring, autosegmentation using SPICE for the HN, thorax, liver, and male pelvis regions is efficient and shows significant promise for clinical utility.

Feasibility of semiautomated MR volumetry using gadoxetic acid-enhanced MRI at hepatobiliary phase for living liver donors

PURPOSE

To assess the feasibility of semiautomated MR volumetry using gadoxetic acid-enhanced MRI at the hepatobiliary phase compared with manual CT volumetry.

METHODS

Forty potential live liver donor candidates who underwent MR and CT on the same day, were included in our study. Semiautomated MR volumetry was performed using gadoxetic acid-enhanced MRI at the hepatobiliary phase. We performed the quadratic MR image division for correction of the bias field inhomogeneity. With manual CT volumetry as the reference standard, we calculated the average volume measurement error of the semiautomated MR volumetry. We also calculated the mean of the number and time of the manual editing, edited volume, and total processing time.

RESULTS

The average volume measurement errors of the semiautomated MR volumetry were 2.35% ± 1.22%. The average values of the numbers of editing, operation times of manual editing, edited volumes, and total processing time for the semiautomated MR volumetry were 1.9 ± 0.6, 8.1 ± 2.7 s, 12.4 ± 8.8 mL, and 11.7 ± 2.9 s, respectively.

CONCLUSION

Semiautomated liver MR volumetry using hepatobiliary phase gadoxetic acid-enhanced MRI with the quadratic MR image division is a reliable, easy, and fast tool to measure liver volume in potential living liver donors.

A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points

The present study developed a hybrid semi-automatic method to extract the liver from abdominal computerized tomography (CT) images. The proposed hybrid method consists of a customized fast-marching level-set method for detection of an optimal initial liver region from multiple seed points selected by the user and a threshold-based level-set method for extraction of the actual liver region based on the initial liver region. The performance of the hybrid method was compared with those of the 2D region growing method implemented in OsiriX using abdominal CT datasets of 15 patients. The hybrid method showed a significantly higher accuracy in liver extraction (similarity index, SI=97.6 ± 0.5%; false positive error, FPE = 2.2 ± 0.7%; false negative error, FNE=2.5 ± 0.8%; average symmetric surface distance, ASD=1.4 ± 0.5mm) than the 2D (SI=94.0 ± 1.9%; FPE = 5.3 ± 1.1%; FNE=6.5 ± 3.7%; ASD=6.7 ± 3.8mm) region growing method. The total liver extraction time per CT dataset of the hybrid method (77 ± 10 s) is significantly less than the 2D region growing method (575 ± 136 s). The interaction time per CT dataset between the user and a computer of the hybrid method (28 ± 4 s) is significantly shorter than the 2D region growing method (484 ± 126 s). The proposed hybrid method was found preferred for liver segmentation in preoperative virtual liver surgery planning.

Preoperative hepatobiliary imaging: what does the radiologist need to know?

Accurate preoperative reporting is essential in guiding the surgeon in deciding when and how to operate safely and effectively. Critically, this relies on an understanding of the operative issues faced by the surgeon, which is not always appreciated by the radiologist. This paper therefore aims to address this, first focusing on relevant anatomical variants, and then issues specific to laparoscopic cholecystectomy, hepatic transplantation, and finally hepatic resection (including cholangiocarcinoma resection). Throughout the paper, there is an emphasis on associated surgical techniques to add context to the discussion.

A simple method for accurate liver volume estimation by use of curve-fitting: a pilot study

In this paper, we describe the effectiveness of our curve-fitting method by comparing liver volumes estimated by our new technique to volumes obtained with the standard manual contour-tracing method. Hepatic parenchymal-phase images of 13 patients were obtained with multi-detector CT scanners after intravenous bolus administration of 120-150 mL of contrast material (300 mgI/mL). The liver contours of all sections were traced manually by an abdominal radiologist, and the liver volume was computed by summing of the volumes inside the contours. The section number between the first and last slice was then divided into 100 equal parts, and each volume was re-sampled by use of linear interpolation. We generated 13 model profile curves by averaging 12 cases, leaving out one case, and we estimated the profile curve for each patient by fitting the volume values at 4 points using a scale and translation transform. Finally, we determined the liver volume by integrating the sampling points of the profile curve. We used Bland-Altman analysis to evaluate the agreement between the volumes estimated with our curve-fitting method and the volumes measured by the manual contour-tracing method. The correlation between the volume measured by manual tracing and that estimated with our curve-fitting method was relatively high (r = 0.98; slope 0.97; p < 0.001). The mean difference between the manual tracing and our method was -22.9 cm(3) (SD of the difference, 46.2 cm(3)). Our volume-estimating technique that requires the tracing of only 4 images exhibited a relatively high linear correlation with the manual tracing technique.

Preliminary study of a novel method for conveying corrected image volumes in surgical navigation

BACKGROUND

Commercial image-guided surgery systems rely on the fundamental assumption that preoperative medical images represent the physical state of the patient in the operating room. The guidance display typically consists of a three-dimensional (3D) model derived from medical images and three orthogonal views of the imaging data. A challenging question in image-guided surgery is: what happens when the images used in the guidance display no longer correspond to the current geometric state of the anatomy and guidance information is still desirable?

METHODS

We modify the conventional display with two techniques for incorporating a displacement field from a finite-element model into the guidance display and present a preliminary study of the effect of our method on performance with a simple surgical task. The topic of this paper is methods for conveying the computational model solution, not the model itself. To address the integration of the computational model solution into the display, a novel method of applying the deformation to the tool tip was developed, which quickly corrects for deformation but also maintains the pristine nature of the preoperative images. We compare the proposed technique to an existing method that applies the deformation field to the image volume.

RESULTS

A pilot study compared mean performance with our method of applying the deformation to the tool tip and the conventional technique. These methods were statistically similar with respect to accuracy of localization (p < 0.05) and amount of time (p < 0.05) required for localization of the target.

CONCLUSIONS

These results suggest that our new technique can be used in place of the computationally expensive task of deforming the image volume, without affecting the time or accuracy of the surgical task. Most notably, our work addresses the problem of incorporating deformation correction into the guidance display and offers a first step toward understanding its effect on surgical performance.