SEMI-automated liver CT segmentation using Laplacian meshes

Chartrand, Gabriel; Cresson, Thierry; Chav, R.; Gotra, Akshat; Tang, An; Deguise, J.-C

doi:10.1109/isbi.2014.6867952

Cited by 23 publications

(14 citation statements)

References 11 publications

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“…Manual segmentation is thus time consuming and may take up to 90 minutes per patient. 16 It is also prone to intra-and interobserver variability given its inherent subjectivity. 15 As a result, for a high volume center, manual segmentation is not ideally suited to guide patient selection and treatment planning.…”

Section: Discussionmentioning

confidence: 99%

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

Kulkarni

Shetty

Gala

et al. 2020

Journal of Clinical Interventional Radiology ISVIR

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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.

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Section: Discussionmentioning

confidence: 99%

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

Kulkarni

Shetty

Gala

et al. 2020

Journal of Clinical Interventional Radiology ISVIR

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“…Variability is also be introduced by the sharpness of liver boundaries, window level settings, and computer monitor settings [ 34 ]. Manual segmentation is also time-consuming and may take up to 90 minutes for one patient [ 35 ]. As a result, manual segmentation is not suited for a busy clinical practice in high volume settings.…”

Section: Manual Segmentationmentioning

confidence: 99%

Liver segmentation: indications, techniques and future directions

et al. 2017

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ObjectivesLiver 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.MethodsUsing 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.ResultsLiver 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.ConclusionsLiver 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. Electronic supplementary materialThe online version of this article (doi:10.1007/s13244-017-0558-1) contains supplementary material, which is available to authorised users.

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“…Thus, segmenting the liver in a consistent manner is particularly important. Manual contouring of the liver can take up to 30 minutes, 3 , 4 and in the case of biomechanical models, inter/intraoperator contour variations may adversely affect the deformable image registration.…”

Section: Introductionmentioning

confidence: 99%

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

Anderson

Lin

Cárdenas

et al. 2021

Advances in Radiation Oncology

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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: μ Contrast(N = 17) : 0.99 mm (0.47-2.2), μ Non_Contrast(N = 19)l : 1.12 mm (0.41-2.87), and μ Miccai(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.

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SEMI-automated liver CT segmentation using Laplacian meshes

Cited by 23 publications

References 11 publications

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

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

Liver segmentation: indications, techniques and future directions

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

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