Approach For Liver Segmentation Research Articles

Transformer Skip‐Fusion Based SwinUNet for Liver Segmentation From CT Images

ABSTRACTLiver segmentation is a crucial step in medical image analysis and is essential for diagnosing and treating liver diseases. However, manual segmentation is time‐consuming and subject to variability among observers. To address these challenges, a novel liver segmentation approach, SwinUNet with transformer skip‐fusion is proposed. This method harnesses the Swin Transformer's capacity to model long‐range dependencies efficiently, the U‐Net's ability to preserve fine spatial details, and the transformer skip‐fusion's effectiveness in enabling the decoder to learn intricate features from encoder feature maps. In experiments using the 3DIRCADb and CHAOS datasets, this technique outperformed traditional CNN‐based methods, achieving a mean DICE coefficient of 0.988% and a mean Jaccard coefficient of 0.973% by aggregating the results obtained from each dataset, signifying outstanding agreement with ground truth. This remarkable accuracy in liver segmentation holds significant promise for improving liver disease diagnosis and enhancing healthcare outcomes for patients with liver conditions.

Sparse deep belief network coupled with extended local fuzzy active contour model-based liver cancer segmentation from abdomen CT images.

Liver cancer from abdominal CT images must be accurately segmented for the purpose of diagnosis with treatment planning. But, the similarity in gray values between the liver and the surrounding tissues poses a challenge. To address this, a novel sparse deep belief network coupled with extended local fuzzy active contour model-based liver cancer segmentation from abdomen CT images (SDBN-ELFAC-LCS-CT) is proposed. This method incorporates dynamic adaptive pooling and residual modules in SDBN to improve the feature selection and generalization ability. Additionally, the 3D reconstruction is performed to refine segmentation results. The proposed SDBN-ELFAC-LCS-CT approach is implemented in MATLAB. The performance of the proposed SDBN-ELFAC-LCS-CT achieves dice coefficients that were up to 96.16% higher and 75.88%, 88.75%, and 71.16% lower. Volumetric overlap error compared with existing models, like basic ensembles of vanilla-style deep learning modes, increases liver segmentation from CT imageries (BEVS-LCS-CT), an incorporated 3 dimensional sparse deep belief network along enriched seagull optimization approach for liver segmentation (3DBN-ESOA-LCS-CT) and iterative convolutional encoder-decoder network and multiple scale context learning for segmenting liver (ICEDN-LCS-CT), respectively.

Liver Segmentation in Ultrasound Images Using Self-Supervised Learning with Physics-inspired Augmentation and Global-Local Refinement

Shear Wave Elastography (SWE) is a non-invasive ultrasound method that evaluates changes in liver stiffness, serving as a useful biomarker for liver fibrosis. The proper placement of a region of interest (ROI) on the liver in the B-mode image is imperative for obtaining accurate and dependable results in SWE. In order to develop an automated system for liver fibrosis measurement utilizing SWE, the initial crucial phase involves the segmentation of the liver capsule. This paper presents a novel approach for liver segmentation in ultrasound images using a contrastive self-supervised learning approach. The proposed method leverages a large dataset of unannotated abdominal ultrasound images to learn the feature representations, which are then fine-tuned on the downstream task of liver segmentation. The algorithm is trained in two stages: in the first stage a SimCLR model is trained to learn the feature representations from non-labeled data, and in the second stage these representations are fine-tuned with a smaller annotated dataset of liver segmentation masks. Finally, this is followed by a refinement step using CascadePSP. The study also investigates the use of physics-inspired augmentations, such as sector angle and penetration to improve the performance of the deep learning model on ultrasound images. The proposed approach of SimCLR+ENet was compared against the state-of-the-art method U-Net. Evaluation of the average Dice similarity showed that SimCLR+ENet outperformed U-Net with a result of 90.58% compared to 89.77%. Similarly, the average Huasdorff distance evaluation demonstrated that SimCLR+ENet achieved superior performance with a value of 21.71 compared to U-Net’s 29.53. This highlights the effectiveness of the proposed approach, with performance improvements of 0.9% and 26.5% for the average Dice coefficient and average Hausdorff distance, respectively. The study provides insights into the use of physics-inspired augmentations in the medical ultrasound imaging field and highlights the potential for self-supervised learning in improving segmentation results.

A Lightweight Deep Learning Approach for Liver Segmentation

Liver segmentation is a prerequisite for various hepatic interventions and is a time-consuming manual task performed by radiology experts. Recently, various computationally expensive deep learning architectures tackled this aspect without considering the resource limitations of a real-life clinical setup. In this paper, we investigated the capabilities of a lightweight model, UNeXt, in comparison with the U-Net model. Moreover, we conduct a broad analysis at the micro and macro levels of these architectures by using two training loss functions: soft dice loss and unified focal loss, and by substituting the commonly used ReLU activation function, with the novel Funnel activation function. An automatic post-processing step that increases the overall performance of the models is also proposed. Model training and evaluation were performed on a public database—LiTS. The results show that the UNeXt model (Funnel activation, soft dice loss, post-processing step) achieved a 0.9902 dice similarity coefficient on the whole CT volumes in the test set, with 15× fewer parameters in nearly 4× less inference time, compared to its counterpart, U-Net. Thus, lightweight models can become the new standard in medical segmentation, and when implemented thoroughly can alleviate the computational burden while preserving the capabilities of a parameter-heavy architecture.

Adaptable volumetric liver segmentation model for CT images using region-based features and convolutional neural network

Liver plays an important role in metabolic processes, therefore fast diagnosis and potential surgical planning is essential in case of any disease. The automatic liver segmentation approach has been studied during the past years and different segmentation techniques have been proposed, but this task remains a challenge and improvements are still required to further increase segmentation accuracy. In this work, an automatic, deep learning based approach is introduced, which is adaptable and it is able to handle smaller databases, including heterogeneous data. The method starts with a preprocessing to highlight the liver area using probability density function based estimation and supervoxel segmentation. Then, a modification of the 3D U-Net is introduced, which is called 3D RP-UNet and applies the ResPath in the 3D network. Finally, with liver-heart separation and morphological steps, the segmentation results are further refined. Segmentation results on three public databases showed that the proposed method performs robustly and achieves good segmentation performance compared to other state-of-the-art approaches in the majority of the evaluation metrics.

An Enhanced Approach for Segmentation of Liver from Computed Tomography Images

An accurate segmentation of liver from Computed Tomography (CT) scans is essential for liver tumor research as it offers valuable information for clinical diagnosis and treatment. However, it is challenging to achieve an accurate segmentation of the liver because of the blurred edges, low contrast and similar intensity of the organs in the CT scan. In this paper, an automated model which will segment the liver from CT images using a hybrid algorithm has been used. The segmentation of liver from CT scan is done with the help of Particle Swarm Optimization (PSO) followed by level set algorithm. The ultimate aim of using this hybrid algorithm is to improve the accuracy of liver segmentation. Computer aided classification of liver CT into healthy and tumorous images aids in diagnosis of liver diseases. It can help a great deal in diagnosis of liver disorders. In order to achieve better classification results, it is of high importance to segment the liver accurately without an error of over or under segmentation. The results obtained indicate that the approach used in this work is faster and has 98.62% accuracy, 99.2% specificity, 97.1% sensitivity, 97.8% F-measure, 96.6% Matthews Coefficient Constant (MCC), 99.08% precision, 97.8% dice coefficient and 95.7% jaccard coefficient in segmenting the liver.

A novel approach for automatic and robust segmentation of the 3D liver in computed tomography images

Liver segmentation is important for the computer-assisted diagnosis of liver disease and liver volume measurement. Because of the complexity of liver computed tomography (CT) images, multiple image segmentation techniques are required to robustly segment the liver. Many liver segmentation approaches have been proposed in the past few decades, however the liver segmentation problem remains. More robust approaches are needed and new possibilities should be investigated. In this paper, we propose a novel 3D liver segmentation approach that contains four major parts: (1) image pre-processing, (2) edge-constrained liver segmentation by 3D region growing, (3) separation of 3D liver and other organs, and (4) post-processing. In the first step, an anisotropic diffusion filter removes noise effectively while keeping the image edges almost unchanged. In the second step, both 3D and 2D canny edges are combined to constrain the region growing process. The seeds of region growing are generated by slope difference distribution threshold selection of the 3D CT image and morphological erosion. In the third step, 3D random walk and slope difference distribution threshold selection are combined to separate the adhered organs. In the fourth step, a novel 2D vascular region correction method is proposed for post-processing. Two open accessible CT liver datasets are used to evaluate the proposed approach and the achieved average DICE measure accuracy is 96.83%.

DCSegNet: Deep Learning Framework Based on Divide-and-Conquer Method for Liver Segmentation

Image segmentation plays a vital role in the medical diagnosis and intervention field. The segmentation methods can be classified as fully automated, semiautomated or manual. Among them, manual segmentation can best improve the quality of the results, but it is time-consuming and tedious, and it may lead to operator bias. A continuity-aware probabilistic network based on the divide-and-conquer method was proposed in the current work. The proposed network comprised backbone network, local segmentation and a weight network. The backbone network extracts the features from image. The local segmentation divides the data space, whereas the weight network provides the continuity-aware weights. Therefore, combining those results of the weighted segments can eventually yield precise estimations. In this study, the proposed model was evaluated against several recent methods on the three datasets, and a several performance indexes of segmentation were evaluated for liver segmentation, the results showing that it is the most advanced liver segmentation approach. The source code of this work is publicly shared at https://github.com/licongsheng/DCSegNet for others to easily reproduce the work and build their own models with the introduced mechanisms.

Liver segmentation in MRI images based on whale optimization algorithm

This paper proposes an approach for liver segmentation in MRI images based on Whale optimization algorithm (WOA). It is used to extract the different clusters in the abdominal image to support the segmentation process. A statistical image is prepared to define the potential liver position in the abdominal image. Then, WOA divides the image into a predefined number of clusters. The prepared statistical image is converted into a binary image and multiplied by the image clustered by WOA. This multiplication process removes a great part of other organs from the image. It is followed by some points, picked up by user interaction, representing the required clusters which reside in the area of liver. The morphological operations enhance the initial segmented liver and produces the final image. The proposed approach is tested using a set of 70 MRI images, annotated and approved by radiology specialists. The resulting image is validated using structural similarity index measure (SSIM), similarity index (SI) and other five measures. The overall accuracy of the experimental result showed accuracy of 96.75% using SSIM and 97.5 using SI%.

Automatic liver segmentation based on appearance and context information

BackgroundAutomated image segmentation has benefits for reducing clinicians’ workload, quicker diagnosis, and a standardization of the diagnosis.MethodsThis study proposes an automatic liver segmentation approach based on appearance and context information. The relationship between neighboring pixels in blocks is utilized to estimate appearance information, which is used for training the first classifier and obtaining the probability distribution map. The map is used for extracting context information, along with appearance features, to train the next classifier. The prior probability distribution map is achieved after iterations and refined through an improved random walk for liver segmentation without user interaction.ResultsThe proposed approach is evaluated using CT images with eight contemporary approaches, and it achieves the highest VOE, RVD, ASD, RMSD and MSD. It also achieves a high average score of 76 using the MICCAI-2007 Grand Challenge scoring system.ConclusionsExperimental results show that the proposed method is superior to eight other state of the art methods.

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.

A low-interaction automatic 3D liver segmentation method using computed tomography for selective internal radiation therapy.

This study introduces a novel liver segmentation approach for estimating anatomic liver volumes towards selective internal radiation treatment (SIRT). The algorithm requires minimal human interaction since the initialization process to segment the entire liver in 3D relied on a single computed tomography (CT) slice. The algorithm integrates a localized contouring algorithm with a modified k-means method. The modified k-means segments each slice into five distinct regions belonging to different structures. The liver region is further segmented using localized contouring. The novelty of the algorithm is in the design of the initialization masks for region contouring to minimize human intervention. Intensity based region growing together with novel volume of interest (VOI) based corrections is used to accomplish the single slice initialization. The performance of the algorithm is evaluated using 34 liver CT scans. Statistical experiments were performed to determine consistency of segmentation and to assess user dependency on the initialization process. Volume estimations are compared to the manual gold standard. Results show an average accuracy of 97.22% for volumetric calculation with an average Dice coefficient of 0.92. Statistical tests show that the algorithm is highly consistent (P = 0.55) and independent of user initialization (P = 0.20 and Fleiss' Kappa = 0.77 ± 0.06).

The domain knowledge based graph-cut model for liver CT segmentation

In this paper, we present a semi-supervised approach for liver segmentation from computed tomography (CT) scans, which is based on the graph cut model integrated with domain knowledge. Firstly, some hard constraints are obtained according to the knowledge of liver characteristic appearance and anatomical location. Secondly, the energy function is constructed via knowledge based similarity measure. A path-based spatial connectivity measure is applied for robust regional properties. Finally, the image is interpreted as a graph, afterwards the segmentation problem is casted as an optimal cut on it, which can be computed through the existing max-flow algorithm. The model is evaluated on MICCAI 2007 liver segmentation challenge datasets and some other CT volumes from the hospital. The experimental results show its effectiveness and efficiency.

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.