Liver Segmentation on CT and MR Using Laplacian Mesh Optimization

Chartrand, Gabriel; Cresson, Thierry; Chav, R.; Gotra, Akshat; Tang, An; Guise, Jacques A. de

doi:10.1109/tbme.2016.2631139

Cited by 60 publications

(45 citation statements)

References 46 publications

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“…Hepatic segmentation involves calculating volumetric data of the liver, both of the entire liver and subsections, most often for surgical or procedural planning. 110 Another example includes the application of deep learning radiomics of elastography (DLRE) which has been shown to have excellent performance in predicting liver fibrosis stages compared with 2D-shear wave elastography and serum biomarkers in patients with hepatitis B. 111 ML also has the potential to reduce imaging time, enhance resolution, and analyze vast amounts of data which may enable practitioners to detect disease earlier and provide improved prognostic information.…”

Section: Advanced Analytic Techniquesmentioning

confidence: 99%

Magnetic Resonance Imaging in Primary Sclerosing Cholangitis—Current State and Future Directions

et al. 2019

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Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease characterized by biliary inflammation and fibrosis leading to bile duct strictures, cirrhosis, and carries an increased risk of hepatobiliary malignancies. Magnetic resonance imaging (MRI) with magnetic resonance cholangiopancreatography (MRCP) is the imaging modality of choice in PSC. As an evolving technology, MRI has other potential applications in the care and study of those patients with PSC. In this review, the authors aim to provide a technical overview on MRI/MRCP and related technologies, summarize its contemporary use in PSC, and discuss its evolving role to predict outcomes and look ahead toward emerging MRI technologies relevant to PSC.

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Section: Advanced Analytic Techniquesmentioning

confidence: 99%

Magnetic Resonance Imaging in Primary Sclerosing Cholangitis—Current State and Future Directions

et al. 2019

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“…Established segmentation procedures for CT imaging have been translated or adjusted to MR imaging. However, due to major differences in the image morphology, these approaches may not be directly applicable to MR imaging with comparable segmentation performance [35].…”

Section: Discussionmentioning

confidence: 99%

“…Chartrand et al [35] proposed a semi-automated segmentation method for CT scans and MR images using Laplacian mesh optimization. This approach achieves an overlap of 92.4 ± 1.4 % for MR imaging.…”

Section: Discussionmentioning

confidence: 99%

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

Winther

Hundt

Ringe

et al. 2020

Rofo

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

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“…Zeng et al [ 13 ] presented modified graph cuts and feature detection based on vessel anatomic structure used for liver and liver vessel segmentation, respectively. In [ 14 ], Laplacian mesh optimization is used for liver segmentation from CT and MR images. An approximate 3D model of liver is initialized by a few manually generated contours, firstly.…”

Section: Introductionmentioning

confidence: 99%

Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation

Jiang

2018

BioMed Research International

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The automated segmentation of liver and tumor from CT images is of great importance in medical diagnoses and clinical treatment. However, accurate and automatic segmentation of liver and tumor is generally complicated due to the complex anatomical structures and low contrast. This paper proposes a registration-based organ positioning (ROP) and joint segmentation method for liver and tumor segmentation from CT images. First, a ROP method is developed to obtain liver's bounding box accurately and efficiently. Second, a joint segmentation method based on fuzzy c-means (FCM) and extreme learning machine (ELM) is designed to perform coarse liver segmentation. Third, the coarse segmentation is regarded as the initial contour of active contour model (ACM) to refine liver boundary by considering the topological information. Finally, tumor segmentation is performed using another ELM. Experiments on two datasets demonstrate the performance advantages of our proposed method compared with other related works.

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Liver Segmentation on CT and MR Using Laplacian Mesh Optimization

Abstract: The proposed approach can alleviate the cumbersome and tedious process of slice-wise segmentation required for precise hepatic volumetry, virtual surgery, and treatment planning.

Cited by 60 publications

References 46 publications

Magnetic Resonance Imaging in Primary Sclerosing Cholangitis—Current State and Future Directions

Magnetic Resonance Imaging in Primary Sclerosing Cholangitis—Current State and Future Directions

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

Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation

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