Liver Vessel Segmentation Research Articles

Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review

The segmentation of liver blood vessels is of major importance as it is essential for formulating diagnoses, planning and delivering treatments, as well as evaluating the results of clinical procedures. Different imaging techniques are available for application in clinical practice, so the segmentation methods should take into account the characteristics of the imaging technique. Based on the literature, this review paper presents the most advanced and effective methods of liver vessel segmentation, as well as their performance according to the metrics used. This paper includes results available for four imaging methods, namely: computed tomography (CT), computed tomography angiography (CTA), magnetic resonance (MR), and ultrasonography (USG). The publicly available datasets used in research are also presented. This paper may help researchers gain better insight into the available materials and methods, making it easier to develop new, more effective solutions, as well as to improve existing approaches. This article analyzes in detail various segmentation methods, which can be divided into three groups: active contours, tracking-based, and machine learning techniques. For each group of methods, their theoretical and practical characteristics are discussed, and the pros and cons are highlighted. The most advanced and promising approaches are also suggested. However, we conclude that liver vasculature segmentation is still an open problem, because of the various deficiencies and constraints researchers need to address and try to eliminate from the solutions used.

Modified GAN-CAED to Minimize Risk of Unintentional Liver Major Vessels Cutting by Controlled Segmentation Using CTA/SPET-CT

This article substantially advances upon state-of-the-art to enhance liver vessels segmentation accuracy by leveraging advantages of synthetic PET-CT (SPET-CT) images in addition to computed tomography angiography (CTA) volumes. Our setup makes a hybrid solution of modified generative adversarial network-convolutional autoencoder (GAN-cAED) combining synthetic ability of GAN to deliver SPET-CT images with generative ability of cAED network in terms of latent learning to more refined segmentation of major liver vessels. We improve time complexity through a novel concept of controlled segmentation by introducing a threshold metric to stop segmentation up to a desired level. The innovative concept of controlled vessel segmentation with a stopping criterion via variant threshold levels will help surgeons to avoid unintentional major blood vessels cutting, reducing the risk of excessive blood loss. Clinically, such solutions offer computer-aided liver surgeries and drug treatment evaluation in a CTA-only environment, shorten the requirement of radioactive and expensive fused PET-CT images.

Liver vessel segmentation based on inter-scale V-Net.

Segmentation and visualization of liver vessel is a key task in preoperative planning and computer-aided diagnosis of liver diseases. Due to the irregular structure of liver vessel, accurate liver vessel segmentation is difficult. This paper proposes a method of liver vessel segmentation based on an improved V-Net network. Firstly, a dilated convolution is introduced into the network to make the network can still enlarge the receptive field without reducing down-sampling and save detailed spatial information. Secondly, a 3D deep supervision mechanism is introduced into the network to speed up the convergence of the network and help the network learn semantic features better. Finally, inter-scale dense connections are designed in the decoder of the network to prevent the loss of high-level semantic information during the decoding process and effectively integrate multi-scale feature information. The public datasets 3Dircadb were used to perform liver vessel segmentation experiments. The average dice and sensitivity of the proposed method reached 71.6 and 75.4%, respectively, which are higher than those of the original network. The experimental results show that the improved V-Net network can automatically and accurately segment labeled or even other unlabeled liver vessels from the CT images.

VesselNet: A deep convolutional neural network with multi pathways for robust hepatic vessel segmentation

Extraction or segmentation of organ vessels is an important task for surgical planning and computer-aided diagnoses. This is a challenging task due to the extremely small size of the vessel structure, low SNR, and varying contrast in medical image data. We propose an automatic and robust vessel segmentation approach that uses a multi-pathways deep learning network. The proposed method trains a deep network for binary classification based on extracted training patches on three planes (sagittal, coronal, and transverse planes) centered on the focused voxels. Thus, it is expected to provide a more reliable recognition performance by exploring the 3D structure. Furthermore, due to the large variety of medical data device values, we transform a raw medical image into a probability map as input to the network. Then, we extract vessels based on the proposed network, which is robust and sufficiently general to handle images with different contrast obtained by various imaging systems. The proposed deep network provides a vessel probability map for voxels in the target medical data, which is used in a post-process to generate the final segmentation result. To validate the effectiveness and efficiency of the proposed method, we conducted experiments with 20 data (public datasets) with different contrast levels and different device value ranges. The results demonstrate impressive performance in comparison with the state-of-the-art methods. We propose the first 3D liver vessel segmentation network using deep learning. Using a multi-pathways network, segmentation results can be improved, and the probability map as input is robust against intensity changes in clinical data.

An Improved Fuzzy Connectedness Method for Automatic Three-Dimensional Liver Vessel Segmentation in CT Images.

In this paper, an improved fuzzy connectedness (FC) method was proposed for automatic three-dimensional (3D) liver vessel segmentation in computed tomography (CT) images. The vessel-enhanced image (i.e., vesselness image) was incorporated into the fuzzy affinity function of FC, rather than the intensity image used by traditional FC. An improved vesselness filter was proposed by incorporating adaptive sigmoid filtering and a background-suppressing item. The fuzzy scene of FC was automatically initialized by using the Otsu segmentation algorithm and one single seed generated adaptively, while traditional FC required multiple seeds. The improved FC method was evaluated on 40 cases of clinical CT volumetric images from the 3Dircadb (n=20) and Sliver07 (n=20) datasets. Experimental results showed that the proposed liver vessel segmentation strategy could achieve better segmentation performance than traditional FC, region growing, and threshold level set. Average accuracy, sensitivity, specificity, and Dice coefficient of the improved FC method were, respectively, (96.4 ± 1.1)%, (73.7 ± 7.6)%, (97.4 ± 1.3)%, and (67.3 ± 5.7)% for the 3Dircadb dataset and (96.8 ± 0.6)%, (89.1 ± 6.8)%, (97.6 ± 1.1)%, and (71.4 ± 7.6)% for the Sliver07 dataset. It was concluded that the improved FC may be used as a new method for automatic 3D segmentation of liver vessel from CT images.

Accurate liver vessel segmentation via active contour model with dense vessel candidates

Background and objectiveThe purpose of this paper is to propose a fully automated liver vessel segmentation algorithm including portal vein and hepatic vein on contrast enhanced CTA images. MethodsFirst, points of a vessel candidate region are extracted from 3-dimensional (3D) CTA image. To generate accurate points, we reduce 3D segmentation problem to 2D problem by generating multiple maximum intensity (MI) images. After the segmentation of MI images, we back-project pixels to the original 3D domain. We call these voxels as vessel candidates (VCs). A large set of MI images can produce very dense and accurate VCs. Finally, for the accurate segmentation of a vessel region, we propose a newly designed active contour model (ACM) that uses the original image, vessel probability map from dense VCs, and the good prior of an initial contour. ResultsWe used 55 abdominal CTAs for a parameter study and a quantitative evaluation. We evaluated the performance of the proposed method comparing with other state-of-the-art ACMs for vascular images applied directly to the original data. The result showed that our method successfully segmented vascular structure 25%-122% more accurately than other methods without any extra false positive detection. ConclusionOur model can generate a smooth and accurate boundary of the vessel object and easily extract thin and weak peripheral branch vessels. The proposed approach can automatically segment a liver vessel without any manual interaction. The detailed result can aid further anatomical studies.

Robust liver vessel extraction using 3D U-Net with variant dice loss function

PurposeLiver vessel extraction from CT images is essential in liver surgical planning. Liver vessel segmentation is difficult due to the complex vessel structures, and even expert manual annotations contain unlabeled vessels. This paper presents an automatic liver vessel extraction method using deep convolutional network and studies the impact of incomplete data annotation on segmentation accuracy evaluation. MethodsWe select the 3D U-Net and use data augmentation for accurate liver vessel extraction with few training samples and incomplete labeling. To deal with high imbalance between foreground (liver vessel) and background (liver) classes but also increase segmentation accuracy, a loss function based on a variant of the dice coefficient is proposed to increase the penalties for misclassified voxels. We include unlabeled liver vessels extracted by our method in the expert manual annotations, with a specialist's visual inspection for refinement, and compare the evaluations before and after the procedure. ResultsExperiments were performed on the public datasets Sliver07 and 3Dircadb as well as local clinical datasets. The average dice and sensitivity for the 3Dircadb dataset were 67.5% and 74.3%, respectively, prior to annotation refinement, as compared with 75.3% and 76.7% after refinement. ConclusionsThe proposed method is automatic, accurate and robust for liver vessel extraction with high noise and varied vessel structures. It can be used for liver surgery planning and rough annotation of new datasets. The evaluation difference based on some benchmarks, and their refined results, showed that the quality of annotation should be further considered for supervised learning methods.

Liver vessel segmentation based on centerline constraint and intensity model

Abstract Liver vessels provide lots of important information for liver-disease diagnosis and liver surgery. This paper presents an effective liver vessel segmentation method from abdominal computer tomography angiography (CTA) images. The proposed method applies two techniques including centerline constraint and intensity model for effective detection of liver vessels, in which the former aims at generating the position and distance restraints for the detection of thin vessels by the offset medialness filter and height ridge traversal algorithm, while the latter is mainly used to extract intensity feature for the detection of thick vessels based on Kernel Fuzzy C-Means (KFCM). And then, the centerline constraint and intensity model are integrated into graph cuts for ultimate liver vessel segmentation. The proposed method does not require any manual selection of the initial vessel regions, and is capable of dealing with complex liver vessel systems. The experimental results on clinical CTA data sets give an average accuracy, sensitivity, and specificity of 97.4%, 83.0%, and 98.1%, respectively, which show the efficiency of the proposed method on liver vessel segmentation.

Automatic liver vessel segmentation using 3D region growing and hybrid active contour model

This paper proposes a new automatic method for liver vessel segmentation by exploiting intensity and shape constraints of 3D vessels. The core of the proposed method is to apply two different strategies: 3D region growing facilitated by bi-Gaussian filter for thin vessel segmentation, and hybrid active contour model combined with K-means clustering for thick vessel segmentation. They are then integrated to generate final segmentation results. The proposed method is validated on abdominal computed tomography angiography (CTA) images, and obtains an average accuracy, sensitivity, specificity, Dice, Jaccard, and RMSD of 98.2%, 68.3%, 99.2%, 73.0%, 66.1%, and 2.56 mm, respectively. Experimental results show that our method is capable of segmenting complex liver vessels with more continuous and complete thin vessel details, and outperforms several existing 3D vessel segmentation algorithms.

Liver vessel segmentation and identification based on oriented flux symmetry and graph cuts

Background and objectiveAccurate segmentation of liver vessels from abdominal computer tomography angiography (CTA) volume is very important for liver-vessel analysis and living-related liver transplants. This paper presents a novel liver-vessel segmentation and identification method. MethodsFirstly, an anisotropic diffusion filter is used to smooth noise while preserving vessel boundaries. Then, based on the gradient symmetry and antisymmetry pattern of vessel structures, optimal oriented flux (OOF) and oriented flux antisymmetry (OFA) measures are respectively applied to detect liver vessels and their boundaries, and further to slenderize vessels. Next, according to vessel geometrical structure, a centerline extraction measure based on height ridge traversal and leaf node line-growing (LNLG) is proposed for the extraction of liver-vessel centerlines, and an intensity model based on fast marching is integrated into graph cuts (GCs) for effective segmentation of liver vessels. Finally, a distance voting mechanism is applied to separate the hepatic vein and portal vein. ResultsThe experiment results on abdominal CTA images show that the proposed method can effectively segment liver vessels, achieving an average accuracy, sensitivity, and specificity of 97.7%, 79.8%, and 98.6%, respectively, and has a good performance on thin-vessel extraction. ConclusionsThe proposed method does not require manual selection of the centerlines and vessel seeds, and can effectively segment liver vessels and identify hepatic vein and portal vein.

Liver vessel segmentation based on extreme learning machine

Liver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remove noise while preserving vessel boundaries from the original computer tomography (CT) images. Then, based on the knowledge of prior shapes and geometrical structures, three classical vessel filters including Sato, Frangi and offset medialness filters together with the strain energy filter are used to extract vessel structure features. Finally, the ELM is applied to segment liver vessels from background voxels. Experimental results show that the proposed method can effectively segment liver vessels from abdominal CT images, and achieves good accuracy, sensitivity and specificity.

New Technique for Automatic Segmentation of Blood Vessels in CT Scan Images of Liver Based on Optimized Fuzzy C-Means Method.

Automatic segmentation of medical CT scan images is one of the most challenging fields in digital image processing. The goal of this paper is to discuss the automatic segmentation of CT scan images to detect and separate vessels in the liver. The segmentation of liver vessels is very important in the liver surgery planning and identifying the structure of vessels and their relationship to tumors. Fuzzy C-means (FCM) method has already been proposed for segmentation of liver vessels. Due to classical optimization process, this method suffers lack of sensitivity to the initial values of class centers and segmentation of local minima. In this article, a method based on FCM in conjunction with genetic algorithms (GA) is applied for segmentation of liver's blood vessels. This method was simulated and validated using 20 CT scan images of the liver. The results showed that the accuracy, sensitivity, specificity, and CPU time of new method in comparison with FCM algorithm reaching up to 91%, 83.62, 94.11%, and 27.17 were achieved, respectively. Moreover, selection of optimal and robust parameters in the initial step led to rapid convergence of the proposed method. The outcome of this research assists medical teams in estimating disease progress and selecting proper treatments.

Quantitative evaluation of noise reduction and vesselness filters for liver vessel segmentation on abdominal CTA images

Liver vessel segmentation in CTA images is a challenging task, especially in the case of noisy images. This paper investigates whether pre-filtering improves liver vessel segmentation in 3D CTA images. We introduce a quantitative evaluation of several well-known filters based on a proposed liver vessel segmentation method on CTA images. We compare the effect of different diffusion techniques i.e. Regularized Perona–Malik, Hybrid Diffusion with Continuous Switch and Vessel Enhancing Diffusion as well as the vesselness approaches proposed by Sato, Frangi and Erdt. Liver vessel segmentation of the pre-processed images is performed using a histogram-based region grown with local maxima as seed points. Quantitative measurements (sensitivity, specificity and accuracy) are determined based on manual landmarks inside and outside the vessels, followed by T-tests for statistic comparisons on 51 clinical CTA images. The evaluation demonstrates that all the filters make liver vessel segmentation have a significantly higher accuracy than without using a filter (p < 0.05); Hybrid Diffusion with Continuous Switch achieves the best performance. Compared to the diffusion filters, vesselness filters have a greater sensitivity but less specificity. In addition, the proposed liver vessel segmentation method with pre-filtering is shown to perform robustly on a clinical dataset having a low contrast-to-noise of up to 3 (dB). The results indicate that the pre-filtering step significantly improves liver vessel segmentation on 3D CTA images.

Fully automated liver segmentation from SPIR image series

Accurate liver segmentation is an important component of surgery planning for liver transplantation, which enables patients with liver disease a chance to survive. Spectral pre-saturation inversion recovery (SPIR) image sequences are useful for liver vessel segmentation because vascular structures in the liver are clearly visible in these sequences. Although level-set based segmentation techniques are frequently used in liver segmentation due to their flexibility to adapt to different problems by incorporating prior knowledge, the need to initialize the contours on each slice is a common drawback of such techniques. In this paper, we present a fully automated variational level set approach for liver segmentation from SPIR image sequences. Our approach is designed to be efficient while achieving high accuracy. The efficiency is achieved by (1) automatically defining an initial contour for each slice, and (2) automatically computing weight values of each term in the applied energy functional at each iteration during evolution. Automated detection and exclusion of spurious structures (e.g. cysts and other bright white regions on the skin) in the pre-processing stage increases the accuracy and robustness. We also present a novel approach to reduce computational cost by employing binary regularization of level set function. A signed pressure force function controls the evolution of the active contour. The method was applied to ten data sets. In each image, the performance of the algorithm was measured using the receiver operating characteristics method in terms of accuracy, sensitivity and specificity. The accuracy of the proposed method was 96%. Quantitative analyses of results indicate that the proposed method can accurately, efficiently and consistently segment liver images.

Segmentation of liver vessels for surgical purposes

In this paper we describe a new approach for segmentation of liver from CT images and further the segmentation of liver vessels to create a visualization model for surgical purposes. Since usual approaches, based on density models or edge detection, don't work well for liver, we investigate the texture of the liver to classify each pixel, whether it lies on the liver-background boundary or outside it. The classifier outputs the boundaries of the liver in each slice, which are used then to create the organ volume. Vessels are segmented then inside the liver volume using a single automatically selected threshold. The result is morphologically closed and smoothed by a Gaussian kernel then.

Segmentation and 3D modeling of liver vessel system

In this paper we describe a new approach for segmentation of liver from CT images and further the segmentation of liver vessels to create a visualization model for surgical and modeling purposes. Since usual approaches, based on density models or edge detection, don't work well for liver, we investigate the local density distribution of the liver texture to classify each pixel, whether it lies on the liver-background boundary or outside it. The classifier outputs the boundaries of the liver in each slice, which are used then to create the organ volume. Vessels are segmented then inside the liver volume using a single automatically selected threshold. The result is morphologically closed and smoothed by a Gaussian kernel then. In the last step we produce a 3D skeleton of the vessel system to investigate its topology.

Liver Vessels Segmentation Using a Hybrid Geometrical Moments/Graph Cuts Method

This paper describes a fast and fully automatic method for liver vessel segmentation on computerized tomography scan preoperative images. The basis of this method is the introduction of a 3-D geometrical moment-based detector of cylindrical shapes within the minimum-cut/maximum-flow energy minimization framework. This method represents an original way to introduce a data term as a constraint into the widely used Boykov's graph cuts algorithm, and hence, to automate the segmentation. The method is evaluated and compared with others on a synthetic dataset. Finally, the relevancy of our method regarding the planning of a necessarily accurate percutaneous high-intensity focused ultrasound surgical operation is demonstrated with some examples.

An iterative Bayesian approach for liver analysis: tumors validation study

We present a new method for the simultaneous, nearly automatic segmentation of liver contours, vessels, and tumors from abdominal CTA scans. The method repeatedly applies multi-resolution, multi-class smoothed Bayesian classification followed by morphological adjustment and active contours refinement. It uses multi-class and voxel neighborhood information to compute an accurate intensity distribution function for each class. Only one user-defined voxel seed for the liver and additional seeds according to the number of tumors inside the liver are required for initialization. The algorithm do not require manual adjustment of internal parameters. In this work, a retrospective study on a validated clinical dataset totaling 20 tumors from 9 patients CTAs� was performed. An aggregated competition score of 61 was obtained on the test set of this database. In addition we measured the robustness of our algorithm to different seeds initializations. These results suggest that our method is clinically applicable, accurate, efficient, and robust to seed selection compared to manually generated ground truth segmentation and to other semi-automatic segmentation methods.

An efficient and generic extension to ITK to process arbitrary shaped regions of interest

The paper describes a software method to extend ITK (Insight ToolKit, supported by the National Library of Medicine), leading to ITK++. This method, which is based on the extension of the iterator design pattern, allows the processing of regions of interest with arbitrary shapes, without modifying the existing ITK code. We experimentally evaluate this work by considering the practical case of the liver vessel segmentation from CT-scan images, where it is pertinent to constrain processings to the liver area. Experimental results clearly prove the interest of this work: for instance, the anisotropic filtering of this area is performed in only 16 s with our proposed solution, while it takes 52 s using the native ITK framework. A major advantage of this method is that only add-ons are performed: this facilitates the further evaluation of ITK++ while preserving the native ITK framework.

Segmentation of liver vessels as seen in MR and CT images

Deriving liver vessel structure from CT and MR scans manually is time consuming and error prone. An automatic procedure that could help the radiologist in her analysis is therefore needed. We use matched filters to emphasise blood vessels and entropy-based thresholding to segment the vessels. Vessel interconnections are next extracted and exported to a graph structure. At the end, genetic algorithms are used to search globally for the most likely graph based on a set of fitness functions. Results show that the methods presented are promising and can eventually be used clinically.