Automatic Liver Tumor Segmentation Research Articles

Attention-guided context asymmetric fusion networks for the liver tumor segmentation of computed tomography images.

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.

Automated segmentation of liver tumors from computed tomographic scans

The precision of liver tumor segmentation heavily depends on the doctor's expertise, hence it is required to produce an algorithm for automatic liver tumor segmentation to reduce the manual intervention in assessing liver disease identification. We propose a CNN-based UNet architecture designed to segment liver tumors from CT images of size 128×128. In this model, modifications were made to the encoder, decoder, and bridge paths to enhance feature extraction efficiency. The performance of the modified UNet was evaluated against an existing segmentation method using the same CT image size. The comparison focused on the Dice similarity coefficient and accuracy. Our proposed method demonstrated a high Dice similarity coefficient of 75.37 % and an accuracy of 99.75 % on the 3Dircadb dataset. These results indicate that our modified UNet achieved superior segmentation metrics compared to state-of-the-art methods, showcasing its effectiveness in liver tumor segmentation.

Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT.

Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.

A three-path network with multi-scale selective feature fusion, edge-inspiring and edge-guiding for liver tumor segmentation

Automatic liver tumor segmentation is one of the most important tasks in computer-aided diagnosis and treatment. Deep learning techniques have gained increasing popularity for medical image segmentation in recent years. However, due to the various shapes, sizes, and obscure boundaries of tumors, it is still difficult to automatically extract tumor regions from CT images. Based on the complementarity of edge detection and region segmentation, a three-path structure with multi-scale selective feature fusion (MSFF) module, multi-channel feature fusion (MFF) module, edge-inspiring (EI) module, and edge-guiding (EG) module is proposed in this paper. The MSFF module includes the process of generation, fusion, and selection of multi-scale features, which can adaptively correct the response weights in multiple branches to filter redundant information. The MFF module integrates richer hierarchical features to capture targets at different scales. The EI module aggregates high-level semantic information at different levels to obtain fine edge semantics, which is injected into the EG module for representation learning of segmentation features. Experiments on the LiTs2017 dataset show that our proposed method achieves a Dice index of 85.55% and a Jaccard index of 81.11%, which are higher than what can be obtained by the current state-of-the-art methods. Cross-dataset validation experiments conducted on 3Dircadb and Clinical datasets show the generalization and robustness of the proposed method by achieving dice indices of 80.14% and 81.68%, respectively.

MANet: a multi-attention network for automatic liver tumor segmentation in computed tomography (CT) imaging

Automatic liver tumor segmentation is a paramount important application for liver tumor diagnosis and treatment planning. However, it has become a highly challenging task due to the heterogeneity of the tumor shape and intensity variation. Automatic liver tumor segmentation is capable to establish the diagnostic standard to provide relevant radiological information to all levels of expertise. Recently, deep convolutional neural networks have demonstrated superiority in feature extraction and learning in medical image segmentation. However, multi-layer dense feature stacks make the model quite inconsistent in imitating visual attention and awareness of radiological expertise for tumor recognition and segmentation task. To bridge that visual attention capability, attention mechanisms have developed for better feature selection. In this paper, we propose a novel network named Multi Attention Network (MANet) as a fusion of attention mechanisms to learn highlighting important features while suppressing irrelevant features for the tumor segmentation task. The proposed deep learning network has followed U-Net as the basic architecture. Moreover, residual mechanism is implemented in the encoder. Convolutional block attention module has split into channel attention and spatial attention modules to implement in encoder and decoder of the proposed architecture. The attention mechanism in Attention U-Net is integrated to extract low-level features to combine with high-level ones. The developed deep learning architecture is trained and evaluated on the publicly available MICCAI 2017 Liver Tumor Segmentation dataset and 3DIRCADb dataset under various evaluation metrics. MANet demonstrated promising results compared to state-of-the-art methods with comparatively small parameter overhead.

Densely Connected U-Net With Criss-Cross Attention for Automatic Liver Tumor Segmentation in CT Images.

Automatic liver tumor segmentation plays a key role in radiation therapy of hepatocellular carcinoma. In this paper, we propose a novel densely connected U-Net model with criss-cross attention (CC-DenseUNet) to segment liver tumors in computed tomography (CT) images. The dense interconnections in CC-DenseUNet ensure the maximum information flow between encoder layers when extracting intra-slice features of liver tumors. Moreover, the criss-cross attention is used in CC-DenseUNet to efficiently capture only the necessary and meaningful non-local contextual information of CT images containing liver tumors. We evaluated the proposed CC-DenseUNet on the LiTS dataset and the 3DIRCADb dataset. Experimental results show that the proposed method reaches the state-of-the-art performance for liver tumor segmentation. We further experimentally demonstrate the robustness of the proposed method on a clinical dataset comprising 20 CT volumes.

DHT-Net: Dynamic Hierarchical Transformer Network for Liver and Tumor Segmentation.

Automatic segmentation of liver tumors is crucial to assist radiologists in clinical diagnosis. While various deep learningbased algorithms have been proposed, such as U-Net and its variants, the inability to explicitly model long-range dependencies in CNN limits the extraction of complex tumor features. Some researchers have applied Transformer-based 3D networks to analyze medical images. However, the previous methods focus on modeling the local information (eg. edge) or global information (eg. morphology) with fixed network weights. To learn and extract complex tumor features of varied tumor size, location, and morphology for more accurate segmentation, we propose a Dynamic Hierarchical Transformer Network, named DHT-Net. The DHT-Net mainly contains a Dynamic Hierarchical Transformer (DHTrans) structure and an Edge Aggregation Block (EAB). The DHTrans first automatically senses the tumor location by Dynamic Adaptive Convolution, which employs hierarchical operations with the different receptive field sizes to learn the features of various tumors, thus enhancing the semantic representation ability of tumor features. Then, to adequately capture the irregular morphological features in the tumor region, DHTrans aggregates global and local texture information in a complementary manner. In addition, we introduce the EAB to extract detailed edge features in the shallow fine-grained details of the network, which provides sharp boundaries of liver and tumor regions. We evaluate DHT-Net on two challenging public datasets, LiTS and 3DIRCADb. The proposed method has shown superior liver and tumor segmentation performance compared to several state-of-the-art 2D, 3D, and 2.5D hybrid models.

Multimodality annotated hepatocellular carcinoma data set including pre- and post-TACE with imaging segmentation

Hepatocellular carcinoma (HCC) is the most common primary liver neoplasm, and its incidence has doubled over the past two decades owing to increasing risk factors. Despite surveillance, most HCC cases are diagnosed at advanced stages and can only be treated using transarterial chemo-embolization (TACE) or systemic therapy. TACE failure may occur with incidence reaching up to 60% of cases, leaving patients with a financial and emotional burden. Radiomics has emerged as a new tool capable of predicting tumor response to TACE from pre-procedural computed tomography (CT) studies. This data report defines the HCC-TACE data collection of confirmed HCC patients who underwent TACE and have pre- and post-procedure CT imaging studies and available treatment outcomes (time-to-progression and overall survival). Clinically curated segmentation of pre-procedural CT studies was done for the purpose of algorithm training for prediction and automatic liver tumor segmentation.

An Across Feature Map Attention-Based Deep Learning Method for Small Liver Tumor Segmentation in CT Scans

<h3>Purpose/Objective(s)</h3> Liver cancer is the second most frequent cancer to cause death in men and sixth for women. Detection of liver cancer early using computed tomography (CT) could assist doctors in making accurate hepatocellular carcinoma evaluation and treatment planning, which prevent millions of patients' death every year. Traditionally, radiologists delineate the liver lesion by manually reading hundreds of CT scans slice by slice, which is an enormous burden and suffers from inter- or intra-operator variations. Therefore, automatic liver tumor segmentation methods are highly demanded in clinical practice. Although many deep convolutional neural network-based techniques for automatic segmentation have been proposed, the small tumor remains challenging to segment due to convolutional and pooling operations that result in the loss of small object information. This study aims to propose a deep learning method for accurate segmentation for small liver tumors. <h3>Materials/Methods</h3> This study included 131 patients with liver cancer. The in-plane resolution of the patients' CTs is from 0.55 mm to 1.0 mm and slice spacing from 0.45 mm to 6.0 mm. We randomly selected 100 CT scans as the training set and others as the testing set. Each CT slice of the testing set was separated into groups according to tumor size as follows: 0.1–2.0, 2.1–5.0, 5.1–10.0, and 10.1–20.0 cm. The CT slice without tumor or tumor size > 20 cm were excluded. This work presents a novel attention-based deep learning method for small liver tumor segmentation, which exploits the relation between small and large tumors by computing the original image of small tumors and the feature maps of large tumors. The relation of small-large tumors can compensate for the information loss of small tumors during the convolutional and pooling operations and improve the performance of small tumor segmentation. <h3>Results</h3> Among 20,693 CT slices of the 31 testing patients, 3.0% CT slices with tumors ≤2 cm, 6.7% ≤5 cm, 10.6% ≤10 cm, and 13.4%≤20 cm. We compared our method with six widely used segmentation models. The results show our model outperforms other methods on all sizes of liver tumors, especially for small size tumors: For the 0.1-2.0 cm liver tumor, it achieved 8.4%, 10.0%, 11.3%, 9.1%, 10.9%, and 9.6% improvement compared to Unet, PAN, DeepLabV3, FPN, LinkNet, and PSPNet, respectively. <h3>Conclusion</h3> The tumors of different sizes share similar imaging characteristics. The information of large tumors can compensate for the information loss of small tumors from the feature propagation processes of deep learning methods. The small-large tumors relation can significantly improve small liver tumor segmentation, which is beneficial for disease diagnosis and treatment planning and has the potential for other types of tumor detection and segmentation.

Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM.

Accurate segmentation of liver tumors, which could help physicians make appropriate treatment decisions and assess the effectiveness of surgical treatment, is crucial for the clinical diagnosis of liver cancer. In this study, we propose a 4-dimensional (4D) deep learning model based on 3D convolution and convolutional long short-term memory (C-LSTM) for hepatocellular carcinoma (HCC) lesion segmentation. The proposed deep learning model utilizes 4D information on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) images to assist liver tumor segmentation. Specifically, a shallow U-net based 3D CNN module was designed to extract 3D spatial domain features from each DCE phase, followed by a 4-layer C-LSTM network module for time domain information exploitation. The combined information of multi-phase DCE images and the manner by which tissue imaging features change on multi-contrast images allow the network to more effectively learn the characteristics of HCC, resulting in better segmentation performance. The proposed model achieved a Dice score of 0.825± 0.077, a Hausdorff distance of 12.84± 8.14 mm, and a volume similarity of 0.891± 0.080 for liver tumor segmentation, which outperformed the 3D U-net model, RA-UNet model and other models in the ablation study in both internal and external test sets. Moreover, the performance of the proposed model is comparable to the nnU-Net model, which showed state-of-the-art performance in many segmentation tasks, with significantly reduced prediction time. The proposed 3D convolution and C-LSTM based model can achieve accurate segmentation of HCC lesions.

CPAD-Net: Contextual parallel attention and dilated network for liver tumor segmentation

Liver cancer is one of the leading causes of cancer death. Accurate and automatic liver tumor segmentation methods are urgent needs in clinical practice. Currently, Fully Convolutional Network and U-Net framework have achieved good results in medical image segmentation tasks, but there is still room for improvement. The traditional U-Net extracted a large number of low-level features, and the detailed features cannot be transmitted to deeper layers, resulting in poor segmentation ability. Therefore, this paper proposed a novel liver tumor segmentation network with contextual parallel attention and dilated convolution, called CPAD-Net. The proposed network applies a subsampled module, which has the same dimensionality reduction function as max-pooling without losing detailed features. CPAD-Net employs a contextual parallel attention module at skip connection. The module fuses contextual multi-scale features and extracts channel-spatial features in parallel. These features are concatenated with deep features to narrow the semantic gap and increase detailed information. Hybrid dilated convolution and double-dilated convolution are used in the encoding and decoding stages to expand the network receptive field. Dropout is added after each hybrid dilated convolution block to prevent overfitting. The efficacy of the proposed network is proved by widespread experimentation on two public datasets (LiTS2017 and 3Dircadb-01) and a clinical dataset from the Affiliated Hospital of Hebei University. The proposed network achieved Dice scores of 74.2%, 73.7% and 73.26%. The experimental results show that the proposed network outperforms most segmentation networks.

Learning From Synthetic CT Images via Test-Time Training for Liver Tumor Segmentation.

Automatic liver tumor segmentation could offer assistance to radiologists in liver tumor diagnosis, and its performance has been significantly improved by recent deep learning based methods. These methods rely on large-scale well-annotated training datasets, but collecting such datasets is time-consuming and labor-intensive, which could hinder their performance in practical situations. Learning from synthetic data is an encouraging solution to address this problem. In our task, synthetic tumors can be injected to healthy images to form training pairs. However, directly applying the model trained using the synthetic tumor images on real test images performs poorly due to the domain shift problem. In this paper, we propose a novel approach, namely Synthetic-to-Real Test-Time Training (SR-TTT), to reduce the domain gap between synthetic training images and real test images. Specifically, we add a self-supervised auxiliary task, i.e., two-step reconstruction, which takes the output of the main segmentation task as its input to build an explicit connection between these two tasks. Moreover, we design a scheduled mixture strategy to avoid error accumulation and bias explosion in the training process. During test time, we adapt the segmentation model to each test image with self-supervision from the auxiliary task so as to improve the inference performance. The proposed method is extensively evaluated on two public datasets for liver tumor segmentation. The experimental results demonstrate that our proposed SR-TTT can effectively mitigate the synthetic-to-real domain shift problem in the liver tumor segmentation task, and is superior to existing state-of-the-art approaches.

Automatic liver tumor segmentation and identification using fully connected convolutional neural network from CT images

SummaryIn recent years, one of the largest causes of death in human beings is liver tumor and cancer. In the current scenario, identifying the cancer tumor manually is very difficult and takes a lot of time as the world is battling with COVID‐19. The doctors and physicians are busy in serving and curing them. To predict the stage of liver tumor and plan the treatment, the segmentation is done from CT scanned images. In this research, two‐stage automatic liver segmentation and tumor identification framework using a fully connected convolutional neural network (FC‐CNN) model is proposed. During the first stage, the liver region is segmented using level set method from the preprocessed (OTSU thresholding) CT images. The features are extracted and utilized to detect the liver tumor in the second stage. The developed FC‐CNN is trained and tested using the extracted second order statistical textural features to classify the tumor affected and normal image. The proposed FC‐CNN model is trained and tested with 3D‐IRCADb‐01 and Kaggle datasets. The tested results prove that the proposed FC‐CNN model outperforms other reported methods. From the performance analysis, it is observed that it achieves a good accuracy and sensitivity rate of 99.11% and 98.10%, respectively.

Improving automatic liver tumor segmentation in late-phase MRI using multi-model training and 3D convolutional neural networks

Automatic liver tumor segmentation can facilitate the planning of liver interventions. For diagnosis of hepatocellular carcinoma, dynamic contrast-enhanced MRI (DCE-MRI) can yield a higher sensitivity than contrast-enhanced CT. However, most studies on automatic liver lesion segmentation have focused on CT. In this study, we present a deep learning-based approach for liver tumor segmentation in the late hepatocellular phase of DCE-MRI, using an anisotropic 3D U-Net architecture and a multi-model training strategy. The 3D architecture improves the segmentation performance compared to a previous study using a 2D U-Net (mean Dice 0.70 vs. 0.65). A further significant improvement is achieved by a multi-model training approach (0.74), which is close to the inter-rater agreement (0.78). A qualitative expert rating of the automatically generated contours confirms the benefit of the multi-model training strategy, with 66 % of contours rated as good or very good, compared to only 43 % when performing a single training. The lesion detection performance with a mean F1-score of 0.59 is inferior to human raters (0.76). Overall, this study shows that correctly detected liver lesions in late-phase DCE-MRI data can be automatically segmented with high accuracy, but the detection, in particular of smaller lesions, can still be improved.

Automatic liver tumor segmentation used the cascade multi-scale attention architecture method based on 3D U-Net

Due to the complex structure of liver tumors and the low contrast with normal tissues make it still a challenging task to accurately segment liver tumors from CT images. To address these problems, we propose an end-to-end segmentation method for liver tumors. The method uses a cascade structure to improve the network's extraction of information. First, the Side-output Feature Fusion Attention block is used to fuse features at different levels and combine with attention mechanism to focus on important information. Then, the Atrous Spatial Pyramid Pooling Attention block is used to extract multi-scale semantic features. Finally, the Multi-scale Prediction Fusion block is used to fully fused the features captured at each layer of the network. To verify the performance of the proposed model and the effectiveness of each module, we evaluate it on LiTS and 3DIRCADb datasets and obtained Dice per Case of 0.665 and 0.719, respectively, and Dice Global of 0.812 and 0.784, respectively. The proposed method is compared with the basic model 3D U-Net, as well as some mainstream methods based on U-Net variants, and our method achieves better performance on the liver tumor segmentation task and is superior to most segmentation algorithms.

Weakly Supervised Liver Tumor Segmentation Using Couinaud Segment Annotation.

Automatic liver tumor segmentation is of great importance for assisting doctors in liver cancer diagnosis and treatment planning. Recently, deep learning approaches trained with pixel-level annotations have contributed many breakthroughs in image segmentation. However, acquiring such accurate dense annotations is time-consuming and labor-intensive, which limits the performance of deep neural networks for medical image segmentation. We note that Couinaud segment is widely used by radiologists when recording liver cancer-related findings in the reports, since it is well-suited for describing the localization of tumors. In this paper, we propose a novel approach to train convolutional networks for liver tumor segmentation using Couinaud segment annotations. Couinaud segment annotations are image-level labels with values ranging from 1 to 8, indicating a specific region of the liver. Our proposed model, namely CouinaudNet, can estimate pseudo tumor masks from the Couinaud segment annotations as pixel-wise supervision for training a fully supervised tumor segmentation model, and it is composed of two components: 1) an inpainting network with Couinaud segment masks which can effectively remove tumors for pathological images by filling the tumor regions with plausible healthy-looking intensities; 2) a difference spotting network for segmenting the tumors, which is trained with healthy-pathological pairs generated by an effective tumor synthesis strategy. The proposed method is extensively evaluated on two liver tumor segmentation datasets. The experimental results demonstrate that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods while requiring significantly less annotation effort.

Residual based attention-Unet combing DAC and RMP modules for automatic liver tumor segmentation in CT.

Purpose: Due to the complex distribution of liver tumors in the abdomen, the accuracy of liver tumor segmentation cannot meet the needs of clinical assistance yet. This paper aims to propose a new end-to-end network to improve the segmentation accuracy of liver tumors from CT. Method: We proposed a hybrid network, leveraging the residual block, the context encoder (CE), and the Attention-Unet, called ResCEAttUnet. The CE comprises a dense atrous convolution (DAC) module and a residual multi-kernel pooling (RMP) module. The DAC module ensures the network derives high-level semantic information and minimizes detailed information loss. The RMP module improves the ability of the network to extract multi-scale features. Moreover, a hybrid loss function based on cross-entropy and Tversky loss function is employed to distribute the weights of the two-loss parts through training iterations. Results: We evaluated the proposed method in LiTS17 and 3DIRCADb databases. It significantly improved the segmentation accuracy compared to state-of-the-art methods. Conclusions: Experimental results demonstrate the satisfying effects of the proposed method through both quantitative and qualitative analyses, thus proving a promising tool in liver tumor segmentation.

Tumor attention networks: Better feature selection, better tumor segmentation

Compared with the traditional analysis of computed tomography scans, automatic liver tumor segmentation can supply precise tumor volumes and reduce the inter-observer variability in estimating the tumor size and the tumor burden, which could further assist physicians to make better therapeutic choices for hepatic diseases and monitoring treatment. Among current mainstream segmentation approaches, multi-layer and multi-kernel convolutional neural networks (CNNs) have attracted much attention in diverse biomedical/medical image segmentation tasks with remarkable performance. However, an arbitrary stacking of feature maps makes CNNs quite inconsistent in imitating the cognition and the visual attention of human beings for a specific visual task. To mitigate the lack of a reasonable feature selection mechanism in CNNs, we exploit a novel and effective network architecture, called Tumor Attention Networks (TA-Net), for mining adaptive features by embedding Tumor Attention layers with multi-functional modules to assist the liver tumor segmentation task. In particular, each tumor attention layer can adaptively highlight valuable tumor features and suppress unrelated ones among feature maps from 3D and 2D perspectives. Moreover, an analysis of visualization results illustrates the effectiveness of our tumor attention modules and the interpretability of CNNs for liver tumor segmentation. Furthermore, we explore different arrangements of skip connections in information fusion. A deep ablation study is also conducted to illustrate the effects of different attention strategies for hepatic tumors. The results of extensive experiments demonstrate that the proposed TA-Net increases the liver tumor segmentation performance with a lower computation cost and a small parameter overhead over the state-of-the-art methods, under various evaluation metrics on clinical benchmark data. In addition, two additional medical image datasets are used to evaluate generalization capability of TA-Net, including the comparison with general semantic segmentation methods and a non-tumor segmentation task. All the program codes have been released at https://github.com/shuchao1212/TA-Net.

Automatic Liver Tumor Segmentation based on Multi-level Deep Convolutional Networks and Fractal Residual Network

ABSTRACT The liver is a very important and complex organ in our body. Efficient liver segmentation is a very important interesting and challenging task in the field of medical image processing. The abdomen consists of many organs among which the liver consists of very important parts, such as right lobule, left lobule and hepatic duct. In this research work, for computed tomography (CT) images we designed a feasible upstream approach to segment a liver region from abdomen using multi-level deep convolutional network and Fractal Residual Network (FRN). Our convolutional neural network is designed in such a way that it learns to allot different probabilities for each super pixel based on different regions of the liver by these indirectly different classes are created based on different intensities of super pixels. Further FRN is used to identify the tumor region and finally refinement of tumor segmentation is done with active contour model method. The proposed model (MDCN + FRN) is evaluated on CT images 125 patients from TCI dataset and achieved a dice similarity in an average of 0.89 in training and dice similarity in an average of 0.86 in testing. Compared to other segmentation methods, the experiment conducted shows better performance from proposed method.

Enhancing Segmentation Approaches From Gaussian Mixture Model and Expected Maximization to Super Pixel Division Algorithm

Automatic liver tumor segmentation would greatly influence liver treatment organizing strategy and follow-up assessment, as a result of organization and joining of full picture information. Right now, develop a totally programmed technique for liver tumor division in CT picture. Introductory liver division comprises of applying a functioning form strategy. In the wake of separating liver applying Super pixel division Algorithm for portioning liver tumor proficiently. In the proposed work, we will investigate these procedures so as to improve division of various segments of the CT pictures. The exploratory outcomes indicated that the proposed strategy was exact for liver tumor division.