An Efficient Semiautomatic Active Contour Model of Liver Tumor Segmentation from CT Images

2019 ◽  
pp. 75-85
Author(s):  
Ankur Biswas ◽  
Paritosh Bhattacharya ◽  
Santi P. Maity
Keyword(s):  
Liver Tumor ◽  
Active Contour ◽  
Active Contour Model ◽  
Ct Images ◽  
Tumor Segmentation ◽  
Contour Model ◽  
Liver Tumor Segmentation

Hybrid active contour model and deep belief network based approach for brain tumor segmentation and classification

Sensor Review ◽  
2019 ◽  
Vol 39 (4) ◽  
pp. 473-487 ◽  
Author(s):  
Ayalapogu Ratna Raju ◽  
Suresh Pabboju ◽  
Ramisetty Rajeswara Rao
Keyword(s):  
Brain Tumor ◽  
Sensitivity And Specificity ◽  
Active Contour ◽  
Active Contour Model ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Content Type ◽  
Contour Model ◽  
The Brain ◽  
Mri Image

Purpose Brain tumor segmentation and classification is the interesting area for differentiating the tumorous and the non-tumorous cells in the brain and classifies the tumorous cells for identifying its level. The methods developed so far lack the automatic classification, consuming considerable time for the classification. In this work, a novel brain tumor classification approach, namely, harmony cuckoo search-based deep belief network (HCS-DBN) has been proposed. Here, the images present in the database are segmented based on the newly developed hybrid active contour (HAC) segmentation model, which is the integration of the Bayesian fuzzy clustering (BFC) and the active contour model. The proposed HCS-DBN algorithm is trained with the features obtained from the segmented images. Finally, the classifier provides the information about the tumor class in each slice available in the database. Experimentation of the proposed HAC and the HCS-DBN algorithm is done using the MRI image available in the BRATS database, and results are observed. The simulation results prove that the proposed HAC and the HCS-DBN algorithm have an overall better performance with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively. Design/methodology/approach The proposed HAC segmentation approach integrates the properties of the AC model and BFC. Initially, the brain image with different modalities is subjected to segmentation with the BFC and AC models. Then, the Laplacian correction is applied to fuse the segmented outputs from each model. Finally, the proposed HAC segmentation provides the error-free segments of the brain tumor regions prevailing in the MRI image. The next step is to extract the useful features, based on scattering transform, wavelet transform and local Gabor binary pattern, from the segmented brain image. Finally, the extracted features from each segment are provided to the DBN for the training, and the HCS algorithm chooses the optimal weights for DBN training. Findings The experimentation of the proposed HAC with the HCS-DBN algorithm is analyzed with the standard BRATS database, and its performance is evaluated based on metrics such as accuracy, sensitivity and specificity. The simulation results of the proposed HAC with the HCS-DBN algorithm are compared against existing works such as k-NN, NN, multi-SVM and multi-SVNN. The results achieved by the proposed HAC with the HCS-DBN algorithm are eventually higher than the existing works with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively. Originality/value This work presents the brain tumor segmentation and the classification scheme by introducing the HAC-based segmentation model. The proposed HAC model combines the BFC and the active contour model through a fusion process, using the Laplacian correction probability for segmenting the slices in the database.

Spatial feature fusion convolutional network for liver and liver tumor segmentation from CT images

2020 ◽  
Author(s):  
Tianyu Liu ◽  
Junchi Liu ◽  
Yan Ma ◽  
Jiangping He ◽  
Jincang Han ◽  
...  
Keyword(s):  
Liver Tumor ◽  
Feature Fusion ◽  
Ct Images ◽  
Tumor Segmentation ◽  
Convolutional Network ◽  
Spatial Feature ◽  
Liver Tumor Segmentation

Automated Lung Nodule Segmentation Using an Active Contour Model Based on PET/CT Images

2015 ◽  
Vol 12 (8) ◽  
pp. 1972-1976 ◽  
Author(s):  
Yan Qiang ◽  
Xiaohui Zhang ◽  
Guohua Ji ◽  
Juanjuan Zhao
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Lung Nodule ◽  
Ct Images ◽  
Model Based ◽  
Contour Model ◽  
Pet Ct

scholarly journals 3D IFPN: Improved Feature Pyramid Network for Automatic Segmentation of Gastric Tumor

2021 ◽  
Vol 11 ◽  
Author(s):  
Haimei Li ◽  
Bing Liu ◽  
Yongtao Zhang ◽  
Chao Fu ◽  
Xiaowei Han ◽  
...  
Keyword(s):  
Liver Tumor ◽  
Feature Fusion ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Learning Ability ◽  
Gastric Tumor ◽  
Tumor Segmentation ◽  
Gastric Tumors ◽  
Multi Level ◽  
Liver Tumor Segmentation

Automatic segmentation of gastric tumor not only provides image-guided clinical diagnosis but also assists radiologists to read images and improve the diagnostic accuracy. However, due to the inhomogeneous intensity distribution of gastric tumors in CT scans, the ambiguous/missing boundaries, and the highly variable shapes of gastric tumors, it is quite challenging to develop an automatic solution. This study designs a novel 3D improved feature pyramidal network (3D IFPN) to automatically segment gastric tumors in computed tomography (CT) images. To meet the challenges of this extremely difficult task, the proposed 3D IFPN makes full use of the complementary information within the low and high layers of deep convolutional neural networks, which is equipped with three types of feature enhancement modules: 3D adaptive spatial feature fusion (ASFF) module, single-level feature refinement (SLFR) module, and multi-level feature refinement (MLFR) module. The 3D ASFF module adaptively suppresses the feature inconsistency in different levels and hence obtains the multi-level features with high feature invariance. Then, the SLFR module combines the adaptive features and previous multi-level features at each level to generate the multi-level refined features by skip connection and attention mechanism. The MLFR module adaptively recalibrates the channel-wise and spatial-wise responses by adding the attention operation, which improves the prediction capability of the network. Furthermore, a stage-wise deep supervision (SDS) mechanism and a hybrid loss function are also embedded to enhance the feature learning ability of the network. CT volumes dataset collected in three Chinese medical centers was used to evaluate the segmentation performance of the proposed 3D IFPN model. Experimental results indicate that our method outperforms state-of-the-art segmentation networks in gastric tumor segmentation. Moreover, to explore the generalization for other segmentation tasks, we also extend the proposed network to liver tumor segmentation in CT images of the MICCAI 2017 Liver Tumor Segmentation Challenge.

scholarly journals Adaptive Attention Convolutional Neural Network for Liver Tumor Segmentation

2021 ◽  
Vol 11 ◽  
Author(s):  
Shunyao Luan ◽  
Xudong Xue ◽  
Yi Ding ◽  
Wei Wei ◽  
Benpeng Zhu
Keyword(s):  
Neural Network ◽  
Liver Tumor ◽  
Semantic Information ◽  
Liver Tumors ◽  
Ct Images ◽  
Attention Mechanism ◽  
Tumor Segmentation ◽  
Expansion Path ◽  
Liver Tumor Segmentation ◽  
End To End

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.

scholarly journals Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images

2020 ◽  
Vol 39 (5) ◽  
pp. 1316-1325 ◽  
Author(s):  
Hyunseok Seo ◽  
Charles Huang ◽  
Maxime Bassenne ◽  
Ruoxiu Xiao ◽  
Lei Xing
Keyword(s):  
Liver Tumor ◽  
Ct Images ◽  
Tumor Segmentation ◽  
Liver Tumor Segmentation ◽  
High Level
Sign in / Sign up

Export Citation Format

Share Document