Liver Segmentation Method Research Articles

Comprehensive evaluation of the ramification patterns of hepatic vascular anatomy based on three-dimensional visualization technology.

The liver segmentation method proposed by Couinaud is widely accepted by surgeons because of its convenience and practicality. However, this conventional eight-segment classification does not reflect realistic details of the liver and thus requires further adjustments to promote improvements in surgical strategies. This study aimed to explore the ramification patterns of the hepatic vasculature comprehensively. A total of 197 eligible patients meeting the study criteria were enrolled for three-dimensional reconstruction analysis. In the left hemiliver, the portal vein bifurcated into P2 and umbilical portion (UP) in 172 (98.3%) patients. The P4b of 103 patients (103/172, 59.9%) whose P4b branched from the right horn of the left portal vein (LPV) diverged from the main trunk of the UP. In the right paramedian sector (RPMS), the entire portal trunk directly bifurcates into P8vent and P8dor. Simple branching of P5 off the trunk of the RPMS was observed in 78 patients (78/130, 60%). The anterior fissure vein (AFV) was identified in 86 (86/148, 58.1%) patients. V8d entered the right hepatic vein (RHV) in all the patients. In 75.3% (113/150) of all the patients, V5d joined the RHV. In the right lateral sector (RLS), more than half (71/133, 53.4%) of the patients had an arch-like type. We summarize different patterns of liver vascular branches, providing a valuable reference for clinical surgery and liver transplantation. Cranio-caudal segmentation is more common than ventral-dorsal segmentation. The AFV can be regarded as a reliable anatomical landmark for subsegmentation in segment 8. In addition, the absence of AFV was associated with the P8 pattern.

Extracting organs of interest from medical images based on convolutional neural network with auxiliary and refined constraints

Accurately extracting organs from medical images provides radiologist with more comprehensive evidences to clinical diagnose, which offers up a higher accuracy and efficiency. However, the key to achieving accurate segmentation lies in abundant clues for contour distinction, which has a high demand for the network architecture design and its practical training status. To this end, we design auxiliary and refined constraints to optimize the energy function by supplying additional guidance in training procedure, thus promoting model’s ability to capture information. Specifically, for the auxiliary constraint, a set of convolutional structures are involved into a conventional network to act as a discriminator, then adversarial network is established. Based on the obtained architecture, we further build adversarial mechanism by introducing a second discriminator into segmentor for refinement. The involvement of refined constraint contributes to ameliorate training situation, optimize model performance, and boost its ability of collecting information for segmentation. We evaluate the proposed framework on two public databases (NIH Pancreas-CT and MICCAI Sliver07). Experimental results show that the proposed network achieves comparable performance to current pancreas segmentation algorithms and outperforms most state-of-the-art liver segmentation methods. The obtained results on public datasets sufficiently demonstrate the effectiveness of the proposed model for organ segmentation.

Lgma-net: liver and tumor segmentation methods based on local–global feature mergence and attention mechanisms

Lgma-net: liver and tumor segmentation methods based on local–global feature mergence and attention mechanisms

Real-time robust liver and gallbladder segmentation during laparoscopic cholecystectomy using convolutional neural networks: an analysis

Aim: Images in different laparoscopic cholecystectomy datasets are acquired using various camera models, parameters, and settings, with the annotation methods varying by institution. These factors result in inconsistent inference performance of the network model. This study aims to identify the optimal network model architecture for liver and gallbladder segmentation from several options. Then, the performance and robustness of the optimal network model are evaluated using an independent dataset that is not included in the training. Methods: The public dataset, CholecSeg8k, was utilized as the input for the network model training, validation, and testing. A local private dataset from KPJ Damansara Hospital, Selangor, Malaysia, was used for testing purposes only. For the implementation of liver and gallbladder segmentation, segmentation models, a public Python library was employed. Results: Among the experiments, highly accurate liver and gallbladder segmentation results were achieved using the feature pyramid network (FPN) architecture as the network model, with the Inception-ResNet-v2 architecture as the network backbone. The best-trained network model resulted in a loss of 0.070955, a mean intersection over union (IoU) score of 0.95896, and a mean F1-score of 0.9773 on the test set. However, visualized results for the private dataset contained considerable false-negative areas. Conclusion: The proposed automated technique has the potential to serve as an alternative to the conventional indocyanine green injection along with near-infrared fluorescence imaging (ICG-NIRF)-based method for liver and gallbladder segmentation during laparoscopic cholecystectomy. Future work will focus on enhancing the results of the private dataset. Additionally, a surgeon-assistant robotic arm that will use the liver and gallbladder segmentation results for camera steering will be analyzed.

A Review of Advancements and Challenges in Liver Segmentation.

Liver segmentation technologies play vital roles in clinical diagnosis, disease monitoring, and surgical planning due to the complex anatomical structure and physiological functions of the liver. This paper provides a comprehensive review of the developments, challenges, and future directions in liver segmentation technology. We systematically analyzed high-quality research published between 2014 and 2024, focusing on liver segmentation methods, public datasets, and evaluation metrics. This review highlights the transition from manual to semi-automatic and fully automatic segmentation methods, describes the capabilities and limitations of available technologies, and provides future outlooks.

Automatic liver segmentation from CT volumes based on multi-view information fusion and condition random fields

Liver segmentation from CT images is a fundamental and essential step for many clinical applications, such as disease diagnosis, surgery navigator, and radiation therapy. Due to the limited computing resources, automatic and accurate liver segmentation from 3D CT volume is still a challenge task. In this paper, we propose an automatic liver segmentation method by fusing multi-view information. First, a 2D network combined with Dual Self-Attention (DSA) is proposed and applied on multi-sectional 2D slices reconstructed from axial, coronal, and sagittal directions. Then, to generate 3D segmentation results, a lightweight 3D network is designed to fuse the segmentation results from different viewing. Finally, a full connection condition random field is used to refine the 3D results. The proposed method is evaluated on four public datasets, including 3DIRCADb, CHAOS, Sliver07, and LiTS. The experimental results show that the proposed method can accurately segment the liver region for the datasets with large or small cases, as well as healthy or lesion livers. The dice values obtained by the proposed method on four datasets are 0.952, 0.969, 0.958, and 0.964, respectively, which are better than those obtained by many existing ones.

CoProLITE: Constrained Proxy Learning for lIver and hepaTic lesion sEgmentation

Liver and hepatic lesion segmentation is an important task in medical image analysis, which plays a crucial role in diagnosis, treatment planning and monitoring of liver diseases. We observed an ordinal layout of the feature space that aligns with CT image characteristics will improve performance on liver and hepatic lesion segmentation task. In order to enforce the samples to conform to a specific layout of the feature space, we propose a novel liver and hepatic lesion segmentation method called CoProLITE, which learns a constrained proxy for each classes. Specifically, We replace the traditional FCN-based segmentation head by a proxy learning-based head to learn feature representations of the images, and introduces constraints during the training process to guide the learning of the proxies. We extensively evaluate CoProLITE on three public datasets and compare it to state-of-the-art methods. The experimental results demonstrate the effectiveness of the proposed method.

Literature survey on deep learning methods for liver segmentation from CT images: a comprehensive review

Literature survey on deep learning methods for liver segmentation from CT images: a comprehensive review

Abdominal organ segmentation via deep diffeomorphic mesh deformations

Abdominal organ segmentation from CT and MRI is an essential prerequisite for surgical planning and computer-aided navigation systems. It is challenging due to the high variability in the shape, size, and position of abdominal organs. Three-dimensional numeric representations of abdominal shapes with point-wise correspondence to a template are further important for quantitative and statistical analyses thereof. Recently, template-based surface extraction methods have shown promising advances for direct mesh reconstruction from volumetric scans. However, the generalization of these deep learning-based approaches to different organs and datasets, a crucial property for deployment in clinical environments, has not yet been assessed. We close this gap and employ template-based mesh reconstruction methods for joint liver, kidney, pancreas, and spleen segmentation. Our experiments on manually annotated CT and MRI data reveal limited generalization capabilities of previous methods to organs of different geometry and weak performance on small datasets. We alleviate these issues with a novel deep diffeomorphic mesh-deformation architecture and an improved training scheme. The resulting method, UNetFlow, generalizes well to all four organs and can be easily fine-tuned on new data. Moreover, we propose a simple registration-based post-processing that aligns voxel and mesh outputs to boost segmentation accuracy.

Deep-Learning Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy

In resin Yttrium-90 (Y-90) selective internal radiation therapy (SIRT), liver volume sizes are needed for Y-90 activity calculations using the body-surface-area method, which are obtained from contours that are manually delineated in 3D images. The aim was to apply a deep-learning based auto-segmentation method for liver delineation for Y-90 SIRT. A deep-learning-based liver segmentation method was applied using the U-Net3D architecture, which is a 3D convolutional neural network (CNN) extended from the original 2D U-Net architecture for 3D objects in medical imaging. The segmentation model was trained on the Liver Tumor Segmentation (LiTS) dataset. The training data set contained 130 CT scans, and the test data set contained 70 CT scans. The model was deployed in the clinic using DICOM communication. Auto-segmentation of liver in the CT images of 18 SIRT patients was studied. The CT images were exported from clinical database to the segmentation model's DICOM location, where a monitoring software detected the incoming data and automatically ran the liver segmentation. The results were then returned to the original DICOM location where the CT images were stored. Auto-segmented liver contours were compared with physician manually-delineated contours. Dice similarity coefficient (DSC), mean distance to agreement (MDA), ratio of volume (RV), and ratio of activity (RA, ratio of activity calculated using an auto-segmented liver contour to the accurate activity calculated using a manually-delineated contour), were assessed. DSC, MDA, and RV are 0.942±0.014 (range: 0.908-0.959), 1.902±0.503 mm (range: 1.043-2.956 mm), and 0.988±0.039 (range: 0.901-1.045), respectively. RA is 1.001±0.003 (range: 0.993-1.007), which indicates that the activities calculated using the auto-segmented liver contours are close to the accurate activities. The segmentation model was able to successfully identify and segment livers in the CT images, and provide accurate and reliable results. The proposed method is beneficial for clinical use as it can process large amounts of data quickly and efficiently, and can be easily deployed in a clinical environment using DICOM communication.

Swine Partial Liver Transplantation Model for Practicing Living Donor Liver Transplantation Based on a New Liver Segmentation Method.

Living donor liver transplantation (LDLT) is one of the most technically demanding and complicated procedures. However, unlike deceased donor liver transplantation, there is no suitable animal model for practicing LDLT. Herein, we propose a new liver segmentation method and a feasible pig LDLT model for practicing for LDLT in humans. Four Landrace pigs weighing 25, 25, 27, and 28 kg were used as donors and recipients to establish a partial liver transplantation model. Partial liver transplantation was performed using a right liver and a left liver, respectively, based on a new segmentation system compatible with that of humans. We established a new segmentation system for porcine liver transplantation and a partial liver transplantation model. For right liver transplantation, 91 and 142 min were required to operate on the donor and recipient, respectively; for left liver transplantation, 57 and 104 min were required to operate on the donor and recipient, respectively. All pigs that underwent partial liver transplantation remained alive until the operation was completed. It is expected that this new pig model based on the new segmentation system will be suitable as an educational tool for LDLT training and will replace the existing animal models for partial liver transplantation.

Quantification of liver-Lung shunt fraction on 3D SPECT/CT images for selective internal radiation therapy of liver cancer using CNN-based segmentations and non-rigid registration

Purpose: Selective internal radiation therapy (SIRT) has been proven to be an effective treatment for hepatocellular carcinoma (HCC) patients. In clinical practice, the treatment planning for SIRT using 90Y microspheres requires estimation of the liver-lung shunt fraction (LSF) to avoid radiation pneumonitis. Currently, the manual segmentation method to draw a region of interest (ROI) of the liver and lung in 2D planar imaging of 99mTc-MAA and 3D SPECT/CT images is inconvenient, time-consuming and observer-dependent. In this study, we propose and evaluate a nearly automatic method for LSF quantification using 3D SPECT/CT images, offering improved performance compared with the current manual segmentation method.Methods: We retrospectively acquired 3D SPECT with non-contrast-enhanced CT images (nCECT) of 60 HCC patients from a SPECT/CT scanning machine, along with the corresponding diagnostic contrast-enhanced CT images (CECT). Our approach for LSF quantification is to use CNN-based methods for liver and lung segmentations in the nCECT image. We first apply 3D ResUnet to coarsely segment the liver. If the liver segmentation contains a large error, we dilate the coarse liver segmentation into the liver mask as a ROI in the nCECT image. Subsequently, non-rigid registration is applied to deform the liver in the CECT image to fit that obtained in the nCECT image. The final liver segmentation is obtained by segmenting the liver in the deformed CECT image using nnU-Net. In addition, the lung segmentations are obtained using 2D ResUnet. Finally, LSF quantitation is performed based on the number of counts in the SPECT image inside the segmentations.Evaluations and Results: To evaluate the liver segmentation accuracy, we used Dice similarity coefficient (DSC), asymmetric surface distance (ASSD), and max surface distance (MSD) and compared the proposed method to five well-known CNN-based methods for liver segmentation. Furthermore, the LSF error obtained by the proposed method was compared to a state-of-the-art method, modified Deepmedic, and the LSF quantifications obtained by manual segmentation. The results show that the proposed method achieved a DSC score for the liver segmentation that is comparable to other state-of-the-art methods, with an average of 0.93, and the highest consistency in segmentation accuracy, yielding a standard deviation of the DSC score of 0.01. The proposed method also obtains the lowest ASSD and MSD scores on average (2.6 mm and 31.5 mm, respectively). Moreover, for the proposed method, a median LSF error of 0.14% is obtained, which is a statically significant improvement to the state-of-the-art-method (p=0.004), and is much smaller than the median error in LSF manual determination by the medical experts using 2D planar image (1.74% and p<0.001).Conclusions: A method for LSF quantification using 3D SPECT/CT images based on CNNs and non-rigid registration was proposed, evaluated and compared to state-of-the-art techniques. The proposed method can quantitatively determine the LSF with high accuracy and has the potential to be applied in clinical practice.

Morph-Rec: A Novel Computer-Aided Liver Segmentation Model based on Morphological Reconstruction Operation

An abdominal Computed Tomography (CT) scan gives more information about diseases of the liver, gallbladder, and biliary tract. In the image processing approach, liver segmentation is an essential step to be done before liver lesion detection. Liver segmentation removes the unwanted regions from the CT image and makes the task of lesion detection easier. In this paper, a novel Morph-Rec model based on morphological reconstruction operation is proposed for liver segmentation from CT images. The proposed work is focused on segmenting the liver region from the CT slices irrespective of the size and shape of the liver region. The proposed model is validated on 2650 CT slices of 120 and 20 CT scans from the LITS and 3DIRCADb datasets, respectively. The proposed Morph-Rec method is evaluated using metrics such as dice score, accuracy, F1 score, Jaccard index and Matthew’s correlation coefficient. To justify the adaptability and efficiency of the proposed model, it is also validated on 50 CT slices of nine CT scans provided by a local scan centre. The proposed method has produced excellent results on all metrics and the obtained results are better than the state-of-the-art conventional methods for liver segmentation. Hence, the proposed technique is an automatic and dataset-generic model that can perform liver segmentation precisely on any CT acquisition of the liver.

An Efficient Framework for Accurate Liver Segmentation in Abdominal CT Images with Low Knowledge Requirement

Liver segmentation from abdominal computed tomography (CT) images is a primary step in the diagnosis and treatment of liver cancer, but previous liver segmentation methods have the problems of excessive demand for prior knowledge, under- and oversegmentation, and boundary leakage. To solve these problems, this paper proposes a new method of liver segmentation to assist doctors in medical judgment. Firstly, a liver reconstruction algorithm is proposed to obtain the approximate initial region of the liver, which reduces the requirement of prior knowledge and can reconstruct the liver region closer to the liver boundary. Then, we refine the edge of the liver region based on the reaction diffusion level set (RD level set). This edge refinement method can effectively deal with the weak boundary problem, prevent under- and oversegmentation, and obtain a more accurate liver region. Our method is verified on the clinical and public datasets, respectively. The segmentation results in terms of mean VOE, RVD, ASD, RMSD, and MSD on dataset Sliver07 are 5.1%, −0.1%, 1.0 mm, 2.0 mm, and 18.2 mm, and on dataset 3Dircadb are 8.1%, −0.2%, 1.5 mm, 2.4 mm, and 20.8 mm, respectively. Compared with the previous algorithms, the experiment results show that this method has a great improvement in accuracy with less prior knowledge. The liver reconstruction algorithm proposed in this paper can obtain a more accurate initial liver region, reduce the requirement for prior knowledge, and reduce time costs compared with the level set algorithm.

A deep residual attention-based U-Net with a biplane joint method for liver segmentation from CT scans

Liver tumours are diseases with high morbidity and high deterioration probabilities, and accurate liver area segmentation from computed tomography (CT) scans is a prerequisite for quick tumour diagnosis. While 2D network segmentation methods can perform segmentation with lower device performance requirements, they often discard the rich 3D spatial information contained in CT scans, limiting their segmentation accuracy. Hence, a deep residual attention-based U-shaped network (DRAUNet) with a biplane joint method for liver segmentation is proposed in this paper, where the biplane joint method introduces coronal CT slices to assist the transverse slices with segmentation, incorporating more 3D spatial information into the segmentation results to improve the segmentation performance of the network. Additionally, a novel deep residual block (DR block) and dual-effect attention module (DAM) are introduced in DRAUNet, where the DR block has deeper layers and two shortcut paths. The DAM efficiently combines the correlations of feature channels and the spatial locations of feature maps. The DRAUNet with the biplane joint method is tested on three datasets, Liver Tumour Segmentation (LiTS), 3D Image Reconstruction for Comparison of Algorithms Database (3DIRCADb), and Segmentation of the Liver Competition 2007 (Sliver07), and it achieves 97.3%, 97.4%, and 96.9% Dice similarity coefficients (DSCs) for liver segmentation, respectively, outperforming most state-of-the-art networks; this strongly demonstrates the segmentation performance of DRAUNet and the ability of the biplane joint method to obtain 3D spatial information from 3D images.

Automatic 3D CT liver segmentation based on fast global minimization of probabilistic active contour.

Automatic liver segmentation from computed tomography (CT) images is an essential preprocessing step for computer-aided diagnosis of liver diseases. However, due to the large differences in liver shapes, low-contrast to adjacent tissues, and existence of tumors or other abnormalities, liver segmentation has been very challenging. This study presents an accurate and fast liver segmentation method based on a novel probabilistic active contour (PAC) model and its fast global minimization scheme (3D-FGMPAC), which is explainable as compared with deep learningmethods. The proposed method first constructs a slice-indexed-histogram to localize the volume of interest (VOI) and estimate the probability that a voxel belongs to the liver according its intensity. The probabilistic image would be used to initialize the 3D PAC model. Secondly, a new contour indicator function, which is a component of the model, is produced by combining the gradient-based edge detection and Hessian-matrix-based surface detection. Then, a fast numerical scheme derived for the 3D PAC model is performed to evolve the initial probabilistic image into the global minimizer of the model, which is a smoothed probabilistic image showing a distinctly highlighted liver. Next, a simple region-growing strategy is applied to extract the whole liver mask from the smoothed probabilistic image. Finally, a B-spline surface is constructed to fit the patch of the rib cage to prevent possible leakage into adjacent intercostaltissues. The proposed method is evaluated on two public datasets. The average Dice score, volume overlap error, volume difference, symmetric surface distance and volume processing time are 0.96, 7.35%, 0.02%, 1.17 mm and 19.8 s for the Sliver07 dataset, and 0.95, 8.89%, , 1.45 mm and 23.08 s for the 3Dircadb dataset,respectively. The proposed fully-automatic approach can effectively segment the liver from low-contrast and complex backgrounds. The quantitative and qualitative results demonstrate that the proposed segmentation method outperforms state-of-the-art traditional automatic liver segmentation algorithms and achieves very competitive performance compared with recent deep leaning-basedmethods.

Swine partial liver transplantation model for practicing living donor liver transplantation based on a new liver segmentation method

Jaewon Lee, Jae-Hyung Cho, Kwang-Woong Lee, Nam-Joon Yi, YoungRok Choi, Suk Kyun Hong, Jeong-Moo Lee, Su Young Hong, Sola Lee, Kyung-Suk Suh. Korean J Transplant 2022;36:292. https://doi.org/10.4285/ATW2022.F-4305

Topological approach of liver segmentation based on 3D visualization technology in surgical planning for split liver transplantation.

Split liver transplantation (SLT) is a complex procedure. The left-lateral and right tri-segment splits are the most common surgical approaches and are based on the Couinaud liver segmentation theory. Notably, the liver surface following right tri-segment splits may exhibit different degrees of ischemic changes related to the destruction of the local portal vein blood flow topology. There is currently no consensus on preoperative evaluation and predictive strategy for hepatic segmental necrosis after SLT. To investigate the application of the topological approach in liver segmentation based on 3D visualization technology in the surgical planning of SLT. Clinical data of 10 recipients and 5 donors who underwent SLT at Shenzhen Third People's Hospital from January 2020 to January 2021 were retrospectively analyzed. Before surgery, all the donors were subjected to 3D modeling and evaluation. Based on the 3D-reconstructed models, the liver splitting procedure was simulated using the liver segmentation system described by Couinaud and a blood flow topology liver segmentation (BFTLS) method. In addition, the volume of the liver was also quantified. Statistical indexes mainly included the hepatic vasculature and expected volume of split grafts evaluated by 3D models, the actual liver volume, and the ischemia state of the hepatic segments during the actual surgery. Among the 5 cases of split liver surgery, the liver was split into a left-lateral segment and right tri-segment in 4 cases, while 1 case was split using the left and right half liver splitting. All operations were successfully implemented according to the preoperative plan. According to Couinaud liver segmentation system and BFTLS methods, the volume of the left lateral segment was 359.00 ± 101.57 mL and 367.75 ± 99.73 mL, respectively, while that measured during the actual surgery was 397.50 ± 37.97 mL. The volume of segment IV (the portion of ischemic liver lobes) allocated to the right tri-segment was 136.31 ± 86.10 mL, as determined using the topological approach to liver segmentation. However, during the actual surgical intervention, ischemia of the right tri-segment section was observed in 4 cases, including 1 case of necrosis and bile leakage, with an ischemic liver volume of 238.7 mL. 3D visualization technology can guide the preoperative planning of SLT and improve accuracy during the intervention. The simulated operation based on 3D visualization of blood flow topology may be useful to predict the degree of ischemia in the liver segment and provide a reference for determining whether the ischemic liver tissue should be removed during the surgery.

Automatic liver tumor segmentation on multiphase computed tomography volume using SegNet deep neural network and K‐means clustering

AbstractLiver and liver tumor segmentations are essential in computer‐aided systems for diagnosing liver tumors. These systems must operate on multiphase computed tomography (CT) images instead of a single phase for accurate diagnosis for clinical applications. We have proposed a framework that can perform segmentation from quadriphasic CT data. The liver was segmented using a fine‐tuned SegNet model and the liver tumor was segmented using the K‐means clustering method coupled with a power‐law transformation‐based image enhancement technique. The best values for liver segmentation achieved were: Dice Coefficient = 96.46 ± 0.48%, Jaccard Index = 93.16 ± 0.89%, volumetric overlap error = 6.84 ± 0.89% and average symmetric surface distance = 0.59 ± 0.3 mm and the results for liver tumor delineation were Dice Coefficient = 85.07 ± 4.5%, Jaccard Index = 74.29 ± 6.8%, volumetric overlap error = 25.71 ± 6.8% and average symmetric surface distance = 1.14 ± 1.3 mm. The proposed liver segmentation method based on deep learning is fully automatic, robust, and effective for all phases. The image enhancement technique has shown promising results and aided in better liver tumor segmentation. The liver tumors were segmented satisfactorily; however, improvements concerning false positive reduction can further increase the accuracy.

Efficient two-step liver and tumour segmentation on abdominal CT via deep learning and a conditional random field

Segmentation of the liver and tumours from computed tomography (CT) scans is an important task in hepatic surgical planning. Manual segmentation of the liver and tumours is a time-consuming and labour-intensive task; therefore, a fully automated method for performing this segmentation is particularly desired. An automatic two-step liver and tumour segmentation method is presented in this paper. A cascade framework is used during the segmentation process, and a fully connected conditional random field (CRF) method is used to refine the tumour segmentation result. First, the proposed fractal residual U-Net (FRA-UNet) is used to locate and initially segment the liver. Then, FRA-UNet is further used to predict liver tumours from the liver region of interest (ROI). Finally, a three-dimensional (3D) CRF is used to refine the tumour segmentation results. The improved fractal residual (FR) structure effectively retains more effective features for improving the segmentation performance of deeper networks, the improved deep residual block can utilise the feature information more effectively, and the 3D CRF method smooths the contours and avoids the tumour oversegmentation problem. FRA-UNet is tested on the Liver Tumour Segmentation challenge dataset (LiTS) and the 3D Image Reconstruction for Comparison of Algorithm Database dataset (3DIRCADb), achieving 97.13% and 97.18% Dice similarity coefficients (DSCs) for liver segmentation and 71.78% and 68.97% DSCs for tumour segmentation, respectively, outperforming most state-of-the-art networks.