A semantic segmentation framework for liver and liver tumour segmentation

ObjectiveLiver cancer is one of the most commonly diagnosed cancer, and energy-based tumor ablation is a widely accepted treatment. Automatic and robust segmentation of liver tumors and ablation zones would facilitate the evaluation of treatment success. The purpose of this study was to develop and evaluate an automatic deep learning based method for (1) segmentation of liver and liver tumors in both arterial and portal venous phase for pre-treatment CT, and (2) segmentation of liver and ablation zones in both arterial and portal venous phase for after ablation treatment.Materials and Methods252 CT images from 63 patients undergoing liver tumor ablation at a large University Hospital were retrospectively included; each patient had pre-treatment and post-treatment multi-phase CT images. 3D voxel-wise manual segmentation of the liver, tumors and ablation region by the radiologist provided reference standard. Deep learning models for liver and lesion segmentation were initially trained on the public Liver Tumor Segmentation Challenge (LiTS) dataset to obtain base models. Then, transfer learning was applied to adapt the base models on the clinical training-set, to obtain tumor and ablation segmentation models both for arterial and portal venous phase images. For modeling, 2D residual-attention Unet (RA-Unet) was employed for liver segmentation and a multi-scale patch-based 3D RA-Unet for tumor and ablation segmentation.ResultsOn the independent test-set, the proposed method achieved a dice similarity coefficient (DSC) of 0.96 and 0.95 for liver segmentation on arterial and portal venous phase, respectively. For liver tumors, the model on arterial phase achieved detection sensitivity of 71%, DSC of 0.64, and on portal venous phase sensitivity of 82%, DSC of 0.73. For liver tumors >0.5cm3 performance improved to sensitivity 79%, DSC 0.65 on arterial phase and, sensitivity 86%, DSC 0.72 on portal venous phase. For ablation zone, the model on arterial phase achieved detection sensitivity of 90%, DSC of 0.83, and on portal venous phase sensitivity of 90%, DSC of 0.89.ConclusionThe proposed deep learning approach can provide automated segmentation of liver tumors and ablation zones on multi-phase (arterial and portal venous) and multi-time-point (before and after treatment) CT enabling quantitative evaluation of treatment success.

PurposeAccurate segmentation of liver and liver tumors is critical for radiotherapy. Liver tumor segmentation, however, remains a difficult and relevant problem in the field of medical image processing because of the various factors like complex and variable location, size, and shape of liver tumors, low contrast between tumors and normal tissues, and blurred or difficult-to-define lesion boundaries. In this paper, we proposed a neural network (S-Net) that can incorporate attention mechanisms to end-to-end segmentation of liver tumors from CT images.MethodsFirst, this study adopted a classical coding-decoding structure to realize end-to-end segmentation. Next, we introduced an attention mechanism between the contraction path and the expansion path so that the network could encode a longer range of semantic information in the local features and find the corresponding relationship between different channels. Then, we introduced long-hop connections between the layers of the contraction path and the expansion path, so that the semantic information extracted in both paths could be fused. Finally, the application of closed operation was used to dissipate the narrow interruptions and long, thin divide. This eliminated small cavities and produced a noise reduction effect.ResultsIn this paper, we used the MICCAI 2017 liver tumor segmentation (LiTS) challenge dataset, 3DIRCADb dataset and doctors’ manual contours of Hubei Cancer Hospital dataset to test the network architecture. We calculated the Dice Global (DG) score, Dice per Case (DC) score, volumetric overlap error (VOE), average symmetric surface distance (ASSD), and root mean square error (RMSE) to evaluate the accuracy of the architecture for liver tumor segmentation. The segmentation DG for tumor was found to be 0.7555, DC was 0.613, VOE was 0.413, ASSD was 1.186 and RMSE was 1.804. For a small tumor, DG was 0.3246 and DC was 0.3082. For a large tumor, DG was 0.7819 and DC was 0.7632.ConclusionS-Net obtained more semantic information with the introduction of an attention mechanism and long jump connection. Experimental results showed that this method effectively improved the effect of tumor recognition in CT images and could be applied to assist doctors in clinical treatment.

Hepatocellular carcinoma currently causes over 800 000 fatalities per year worldwide – and the number of cases is increasing. An early diagnosis and treatment play a crucial role in saving patients’ lives. The purpose of this study is the exploration of a robust and precise computer-aided diagnosis (CAD) method using deep learning algorithms for liver tumor localization and segmentation. The difficulty of liver tumor segmentation lies within the recognition of the contrast between healthy and malignant tissues. This study proposes an implementation of a two-phased multi-scale and multi-resolution training pipeline to perform high accuracy in medical imaging segmentation tasks. For the experiments, the Liver Tumor Segmentation challenge (LiTS) public dataset was used. It contains 131 computed tomography (CT) images, out of which 82% show liver tumors with various shapes of lesion distribution. The final results show a dice per case score of 96.3% for liver segmentation and 72.5% for tumor segmentation when compared to the top LiTS results.

Liver tumor segmentation in computed tomography images is considered a difficult task, especially for highly diverse datasets. This is demonstrated by top-ranking results of the Liver Tumor Segmentation Challenge (LiTS) achieving ~70% dice. To improve upon these results, it is important to identify sources of limitations. In this work, we developed a tumor segmentation method following automatic liver segmentation and conducted a detailed limitation analysis study. Using LiTS dataset, tumor segmentation results performed comparable to state-of-the-art literature, achieving overall dice of 71%. Tumor detection accuracy reached ~83%. We have found that segmentation’s upper limit dice can reach ~77% if all false-positives were removed. Grouping by tumor sizes, larger tumors tend to have better segmentation, reaching a maximum approximated dice limit of 82.29% for tumors greater than 20,000 voxels. Medium and small tumor groups had an upper dice limit of 78.75% and 63.52% respectively. The tumor dice for true-positives was comparable for ideal (manual) vs. automatically segmented liver, reflecting a well-trained organ segmentation. We conclude that the segmentation of very small tumors with size values < 100 voxels is especially challenging where the system can be hyper-sensitive to consider local noise artifacts as possible tumors. The results of this work provide better insight about segmentation system limitations to enable for better false-positive removal development strategies. Removing suspected tumor regions less than 100 voxels eliminates ~80% of the total false-positives and therefore, may be an important step for clinical application of automated liver tumor detection and segmentation.

Cancer plays a major risk for public health worldwide. According to the survey made by the cancer society predicts approximate about 42,220 new cases will be diagnosed and around 32,220 people will die of this cancer that is around 71% of people will die in 2018 and Liver cancer rate is increased by 3% for every year since 2000 and achieved second leading place for the cause of death. There is a con-tinuous in the development with regard to prevent and different options for treating the cancer. Detection of cancer at its initial stages is very difficult with the help of pathological information’s, so as any added support CAD systems using CT scan images are being designed from few decades in order to find out cancer in its early stage. In this paper discussed various segmentation techniques and liver tumor detection techniques to initial segment out the liver region from the abdominal and then to extract the efficient characteristics. Based on the characteristics presences of tumour is identified and separated out from the liver and finally analyse the stage of the cancer. Therefore the process is divided into three parts; 1.Region segmentation, 2.Liver Tumour segmentation and 3.Detection of Cancer stage. In this paper, study is done on different methods of liver region and tumour segmentation of abdominal CT scan to analyze liver tumor and detection of early stage of the tumor

To assist physicians in the diagnosis and treatment planning of tumor, a robust and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, numerous researchers have improved the segmentation accuracy of liver and tumor by introducing multiscale contextual information and attention mechanism. However, this tends to introduce more training parameters and suffer from a heavier computational burden. In addition, the tumor has various sizes, shapes, locations, and numbers, which is the main reason for the poor accuracy of automatic segmentation. Although current loss functions can improve the learning ability of the model for hard samples to a certain extent, these loss functions are difficult to optimize the segmentation effect of small tumor regions when the large tumor regions in the sample are in themajority. We propose a Liver and Tumor Segmentation Network (LiTS-Net) framework. First, the Shift-Channel Attention Module (S-CAM) is designed to model the feature interdependencies in adjacent channels and does not require additional training parameters. Second, the Weighted-Region (WR) loss function is proposed to emphasize the weight of small tumors in dense tumor regions and reduce the weight of easily segmented samples. Moreover, the Multiple 3D Inception Encoder Units (MEU) is adopted to capture the multiscale contextual information for better segmentation of liver andtumor. Efficacy of the LiTS-Net is demonstrated through the public dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) challenge, with Dice per case of 96.9 and 75.1 , respectively. For the 3D Image Reconstruction for Comparison of Algorithm and DataBase (3Dircadb), Dices are 96.47 for the liver and 74.54 for tumor segmentation. The proposed LiTS-Net outperforms existing state-of-the-artnetworks. We demonstrated the effectiveness of LiTS-Net and its core components for liver and tumor segmentation. The S-CAM is designed to model the feature interdependencies in the adjacent channels, which is characterized by no need to add additional training parameters. Meanwhile, we conduct an in-depth study of the feature shift proportion of adjacent channels to determine the optimal shift proportion. In addition, the WR loss function can implicitly learn the weights among regions without the need to manually specify the weights. In dense tumor segmentation tasks, WR aims to enhance the weights of small tumor regions and alleviate the problem that small tumor segmentation is difficult to optimize further when large tumor regions occupy the majority. Last but not least, our proposed method outperforms other state-of-the-art methods on both the LiTS dataset and the 3Dircadbdataset.

A 49-year-old man with chronic hepatitis B receiving treatment with entecavir visited a hospital with a complaint of abdominal pain. Computed tomography (CT) showed 2 liver tumors, each measuring 1cm in diameter, 1 in segment 7 and 1 in segment 4. Magnetic resonance imaging (MRI) showed a hypervascular tumor in segment 7 that appeared in a site different from that seen on CT. The liver tumor in segment 4 was not detected by MRI. Two months later, MRI showed a new liver tumor in segment 7/6 and that the liver tumor in segment 7 had increased to 2cm in diameter;blood tests showed eosinophilia. Enzyme-linked immunosorbent assay showed a high serum Toxocara antibody. The patient was diagnosed as having hepatic toxocariasis and was treated with albendazole for 8 weeks. After treatment, MRI showed that the liver tumors disappeared. Eosinophilia, multiple lesions, and the disappearance of the tumors were characteristic findings of visceral larva migrans.

PurposeLiver is one of the organs with a high incidence of tumors in the human body. Malignant liver tumors seriously threaten human life and health. The difficulties of liver tumor segmentation from computed tomography (CT) image are: (a) The contrast between the liver tumors and healthy tissues in CT images is low and the boundary is blurred; (b) The image of liver tumor is complex and diversified in size, shape, and location.MethodsTo solve the above problems, this paper focused on the human liver and liver tumor segmentation algorithm based on convolutional neural network (CNN), and specially designed a three‐dimensional dual path multiscale convolutional neural network (TDP‐CNN). To balance the performance of segmentation and requirement of computational resources, the dual path was used in the network, then the feature maps from both paths were fused at the end of the paths. To refine the segmentation results, we used conditional random fields (CRF) to eliminate the false segmentation points in the segmentation results to improve the accuracy.ResultsIn the experiment, we used the public dataset liver tumor segmentation (LiTS) to analyze the segmentation results qualitatively and quantitatively. Ground truth segmentation of liver and liver tumor was manually labeled by an experienced radiologist. Quantitative metrics were Dice, Hausdorff distance, and average distance. For the segmentation results of liver tumor, Dice was 0.689, Hausdorff distance was 7.69, and the average distance was 1.07; for the segmentation results of the liver, Dice was 0.965, Hausdorff distance was 29.162, and the average distance was 0.197. Compared with other liver and liver tumor segmentation algorithms in Medical Image Computing and Intervention (MICCAI) 2017 competition, our method of liver segmentation ranked first, and liver tumor segmentation ranked second.ConclusionsThe experimental results showed that the proposed algorithm had good performance in both liver and liver tumor segmentation.

This paper presents a new approach to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast learning speed and good generalization ability, it is chosen to be the base classifier in the ensemble. Besides, majority voting is incorporated for fusion of classification results from the ensemble of base classifiers. In order to further increase testing accuracy, ELM autoencoder is implemented as a pre-training step. In automatic liver tumor detection, ELM is trained as a one-class classifier with only healthy liver samples, and the performance is compared with two-class ELM. In liver tumor segmentation, a semi-automatic approach is adopted by selecting samples in 3D space to train the classifier. The proposed method is tested and evaluated on a group of patients' CT data and experiment show promising results.

Liver tumor segmentation based on medical imaging is playing an increasingly important role in liver tumor research and individualized therapeutic decision-making. However, it remains a challenging in terms of the accuracy of automatic segmentation of liver tumors. Therefore, we aimed to develop a novel deep neural network for improving the results from the automatic segmentation of liver tumors. This paper proposes the attention-guided context asymmetric fusion network (AGCAF-Net), combining attention guidance and fusion context modules on the basis of a residual neural network for the automatic segmentation of liver tumors. According to the attention-guided context block (AGCB), the feature map is first divided into multiple small blocks, the local correlation between features is calculated, and then the global nonlocal fusion module (GNFM) is used to obtain the global information between pixels. Additionally, the context pyramid module (CPM) and asymmetric semantic fusion module (AFM) are used to obtain multiscale features and resolve the feature mismatch during feature fusion, respectively. Finally, we used the liver tumor segmentation benchmark (LiTS) dataset to verify the efficiency of our designed network. Our results showed that AGCAF-Net with AFM and CPM is effective in improving the accuracy of liver tumor segmentation, with the Dice coefficient increasing from 82.5% to 84.1%. The segmentation results of liver tumors by AGCAF-Net were superior to those of several state-of-the-art U-net methods, with a Dice coefficient of 84.1%, a sensitivity of 91.7%, and an average symmetric surface distance of 3.52. AGCAF-Net can obtain better matched and accurate segmentation in liver tumor segmentation, thus effectively improving the accuracy of liver tumor segmentation.

BackgroundLiver cancer is still one of the most common causes of death from cancer globally. The accurate segmentation of liver tumors from CT images is critical for diagnosis, treatment planning, and tracking. Conventional segmentation techniques frequently struggle to handle the intricacy of medical images, requiring the usage of sophisticated artificial intelligence (AI) methods to enhance accuracy and effectiveness.ObjectiveThe main objective of this study is to create and test an improved U-Net model (AM-UNet) that incorporates an attention mechanism to enhance the segmentation and classification accuracy of liver tumors in CT images. This method seeks to surpass previous techniques in terms of accuracy, precision, recall, and F1 score.MethodsThe dataset used includes 194 liver tumor CT scans obtained from 131 individuals for training and 70 for testing. The open-source 3DIRCAD-B dataset, which is incorporated into LiTS, contains images of both normal and pathological conditions. Preprocessing methods such as Median Filtering (MF) and Histogram Equalization (HE) were used to reduce noise and improve contrast. The AM-UNet model was then used to segment the tumors before classifying them as malignant or benign. The efficiency was assessed utilizing metrics like accuracy, precision, recall, F1-score, and ROC (Receiver Operating Characteristic).ResultsThe suggested AM-UNet model produced excellent outcomes, with a recall of 95%, accuracy of 92%, precision of 94%, and an F1-score of 93%. These metrics show that the model outperforms conventional techniques in correctly segmenting and classifying liver tumors in CT images.ConclusionThe AM-UNet model improves the segmentation and classification of liver tumors, providing substantial performance metrics over traditional methods. Its utilization can transform liver cancer diagnosis by assisting physicians in accurate tumor identification and treatment planning, resulting in improved patient results.

Weakly-Supervised teacher-Student network for liver tumor segmentation from non-enhanced images.

Computed tomography (CT) imaging remains the most utilized modality for liver-related cancer screening and treatment monitoring purposes. Liver, liver tumor and liver vasculature segmentation from CT data is a prerequisite for treatment planning and computer assisted detection/diagnosis systems. In this paper, we present a survey on liver, liver tumor and liver vasculature segmentation methods that are using CT images, recent methods presented in the literature are viewed and discussed along with positives, negatives and statistical performance of these methods. Liver computer assisted detection/diagnosis systems will also be discussed along with their limitations and possible ways of improvement. In this paper, we concluded that although there is still room for improvement, automatic liver segmentation methods have become comparable to human segmentation. However, the performance of liver tumor segmentation methods can be considered lower than expected in both automatic and semi-automatic methods. Furthermore, it can be seen that most computer assisted detection/diagnosis systems require manual segmentation of liver and liver tumors, limiting clinical applicability of these systems. Liver, liver tumor and liver vasculature segmentation is still an open problem since various weaknesses and drawbacks of these methods can still be addressed and improved especially in tumor and vasculature segmentation along with computer assisted detection/diagnosis systems.

The accurate segmentation of liver and liver tumors from CT images can assist radiologists in decision-making and treatment planning. The contours of liver and liver tumors are currently obtained by manual labeling, which is time-consuming and subjective. Computer-aided segmentation methods have been widely used in the segmentation of liver and liver tumors. However, due to the diversity of shape, volume, and image intensity, the segmentation is still a difficult task. In this study, we present a Spatial Feature Fusion Convolutional Network (SFF-Net) to automatically segment liver and liver tumors from CT images. First, we extract side-outputs at each convolutional block in SFF-Net to make full use of multiscale features. Second, skip-connections are added in the down-sampling phase, therefore, the spatial information can be efficiently transferred to later layers. Third, we present feature fusion blocks (FFBs) to merge spatial features and high-level semantic features from early layers and later layers, respectively. Finally, a fully connected 3D conditional random fields (CRFs) is applied to refine the liver and liver tumor segmentation results. We test our method on the MICCAI 2017 Liver Tumor Segmentation (LiTS) challenge dataset. The Dice Global (DG) score, Dice per case (DC) score, Volume Overlap Error (VOE), Average Symmetric Surface Distance (ASSD), and tumor precision score are calculated to evaluate the liver and liver tumor segmentation accuracies. For the liver segmentation, DG is 0.955; DC is 0.937; VOE is 0.106; and ASSD is 3.678. For the tumor segmentation, DG is 0.746; DC is 0.592; VOE is 0.416; ASSD is 1.585 and the tumor precision score is 0.369. The SFF-Net learns more spatial information by adding skip-connections and feature fusion blocks. The experiments validate that our method can accurately segment liver and liver tumors from CT images.

Accurate segmentation of the liver and liver tumour (LT) is challenging due to its hazy boundaries and large shape variability. Although using U-Net for liver and LT segmentation achieves better results than manual segmentation, it loses spatial and channel features during segmentation, leading to inaccurate liver and LT segmentation. A residual deformable split depth-wise separable U-Net (RDSDSU-Net) is proposed to increase the accuracy of liver and LT segmentation. The residual deformable convolution layer (DCL) with deformable pooling (DP) is used in the encoder as an attention mechanism to adaptively extract liver and LT shape and position characteristics. Afterward, a convolutional spatial and channel features split graph network (CSCFSG-Net) is introduced in the middle processing layer to improve the expression capability of the liver and LT features by capturing spatial and channel features separately and to extract global contextual liver and LT information from spatial and channel features. Sub-pixel convolutions (SPC) are used in the decoder section to prevent the segmentation results from having a chequerboard artefact effect. Also, the residual deformable encoder features are combined with the decoder through summation to avoid increasing the number of feature maps (FM). Finally, the efficiency of the RDSDSU-Net is evaluated on the 3DIRCADb and LiTS datasets. The DICE score of the proposed RDSDSU-Net achieved 98.21% for liver segmentation and 93.25% for LT segmentation on 3DIRCADb. The experimental outcomes illustrate that the proposed RDSDSU-Net model achieved better segmentation results than the existing techniques.