Segmentation and Diagnosis of Liver Carcinoma Based on Adaptive Scale-Kernel Fuzzy Clustering Model for CT Images

SADSNet: A robust 3D synchronous segmentation network for liver and liver tumors based on spatial attention mechanism and deep supervision

Highlights

• Introduce a data augmentation strategy to expand the required different morphological data during the training and learning phase, and improve the algorithm's feature learning ability for complex and diverse tumor morphology CT images.• Design attention mechanisms for encoding and decoding paths to extract fine pixel level features, improve feature extraction capabilities, and achieve efficient spatial channel feature fusion.• The deep supervision layer is used to correct and decode the final image data to provide high accuracy of results.• The effectiveness of this method has been affirmed through validation on the LITS, 3DIRCADb, and SLIVER datasets.

BACKGROUND

Accurately extracting liver and liver tumors from medical images is an important step in lesion localization and diagnosis, surgical planning, and postoperative monitoring. However, the limited number of radiation therapists and a great number of images make this work time-consuming.

OBJECTIVE

This study designs a spatial attention deep supervised network (SADSNet) for simultaneous automatic segmentation of liver and tumors.

METHOD

Firstly, self-designed spatial attention modules are introduced at each layer of the encoder and decoder to extract image features at different scales and resolutions, helping the model better capture liver tumors and fine structures. The designed spatial attention module is implemented through two gate signals related to liver and tumors, as well as changing the size of convolutional kernels; Secondly, deep supervision is added behind the three layers of the decoder to assist the backbone network in feature learning and improve gradient propagation, enhancing robustness.

RESULTS

The method was testing on LITS, 3DIRCADb, and SLIVER datasets. For the liver, it obtained dice similarity coefficients of 97.03%, 96.11%, and 97.40%, surface dice of 81.98%, 82.53%, and 86.29%, 95% hausdorff distances of 8.96 mm, 8.26 mm, and 3.79 mm, and average surface distances of 1.54 mm, 1.19 mm, and 0.81 mm. Additionally, it also achieved precise tumor segmentation, which with dice scores of 87.81% and 87.50%, surface dice of 89.63% and 84.26%, 95% hausdorff distance of 12.96 mm and 16.55 mm, and average surface distances of 1.11 mm and 3.04 mm on LITS and 3DIRCADb, respectively.

CONCLUSION

The experimental results show that the proposed method is effective and superior to some other methods. Therefore, this method can provide technical support for liver and liver tumor segmentation in clinical practice.

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

OBJECTIVES

Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging?

METHODS

A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography.

RESULTS

One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians' intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy.

CONCLUSION

Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this.

KEY POINTS

• ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.

Hybrid-attention densely connected U-Net with GAP for extracting livers from CT volumes

PURPOSE

Liver segmentation is an important step in the clinical treatment of liver cancer, and accurate and automatic liver segmentation methods are extremely important. U-Net has been used as the benchmark for many medical segmentation networks, but it cannot fully utilize low-resolution information and global contextual information. To solve these problems, we propose a new network architecture named the hybrid-attention densely connected U-Net (HDU-Net).

METHODS

The proposed HDU-Net has three main changes relative to U-Net, as follows: (1) It uses a densely connected structure and dilated convolution to achieve feature reuse and avoid information loss. (2) A global average pooling block is proposed to further augment the receptive field and improve the segmentation accuracy of the network for small or disconnected liver regions. (3) By combining the spatial attention and channel attention mechanisms, a hybrid attention structure is proposed to replace the skip connection component to filter and integrate low-resolution information.

RESULTS

Experiments conducted on the LITS2017, 3Dircadb and Sliver07 datasets show that the proposed model can segment the liver accurately and effectively. Dice scores reach 96.5%, 96.18%, and 97.57% on these datasets, respectively, constituting results that are superior to many previously proposed methods.

CONCLUSIONS

The experimental liver segmentation results have demonstrated that our proposed network provides improved segmentation performance in comparison with other networks. The experimental results without postprocessing confirmed that our network solves the oversegmentation and undersegmentation problems to some extent. The proposed model is effective, robust, and efficient in terms of liver segmentation without requiring extensive training time or a very large dataset.

Gabor Dictionary of Sparse Image Patches Selected in Prior Boundaries for 3D Liver Segmentation in CT Images

The gray contrast between the liver and other soft tissues is low, and the boundary is not obvious. As a result, it is still a challenging task to accurately segment the liver from CT images. In recent years, methods of machine learning have become a research hotspot in the field of medical image segmentation because they can effectively use the "gold standard" personalized features of the liver from different data. However, machine learning usually requires a large number of data samples to train the model and improve the accuracy of medical image segmentation. This paper proposed a method for liver segmentation based on the Gabor dictionary of sparse image blocks with prior boundaries. This method reduced the number of samples by selecting the test sample set within the initial boundary area of the liver. The Gabor feature was extracted and the query dictionary was created, and the sparse coefficient was calculated to obtain the boundary information of the liver. By optimizing the reconstruction error and filling holes, a smooth liver boundary was obtained. The proposed method was tested on the MICCAI 2007 dataset and ISBI2017 dataset, and five measures were used to evaluate the results. The proposed method was compared with methods for liver segmentation proposed in recent years. The experimental results show that this method can improve the accuracy of liver segmentation and effectively repair the discontinuity and local overlap of segmentation results.

Computer-aided diagnosis of liver lesions using CT images: A systematic review

BACKGROUND

Medical image processing has a strong footprint in radio diagnosis for the detection of diseases from the images. Several computer-aided systems were researched in the recent past to assist the radiologist in diagnosing liver diseases and reducing the interpretation time. The aim of this paper is to provide an overview of the state-of-the-art techniques in computer-assisted diagnosis systems to predict benign and malignant lesions using computed tomography images.

METHODS

The research articles published between 1998 and 2020 obtained from various standard databases were considered for preparing the review. The research papers include both conventional as well as deep learning-based systems for liver lesion diagnosis. The paper initially discusses the various hepatic lesions that are identifiable on computed tomography images, then the computer-aided diagnosis systems and their workflow. The conventional and deep learning-based systems are presented in stages wherein the various methods used for preprocessing, liver and lesion segmentation, radiological feature extraction and classification are discussed.

CONCLUSION

The review suggests the scope for future, work as efficient and effective segmentation methods that work well with diverse images have not been developed. Furthermore, unsupervised and semi-supervised deep learning models were not investigated for liver disease diagnosis in the reviewed papers. Other areas to be explored include image fusion and inclusion of essential clinical features along with the radiological features for better classification accuracy.

Contrast-enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two-center study

Background

The present study constructed and validated the use of contrast‐enhanced computed tomography (CT)‐based radiomics to preoperatively predict microvascular invasion (MVI) status (positive vs negative) and risk (low vs high) in patients with hepatocellular carcinoma (HCC).

Methods

We enrolled 637 patients from two independent institutions. Patients from Institution I were randomly divided into a training cohort of 451 patients and a test cohort of 111 patients. Patients from Institution II served as an independent validation set. The LASSO algorithm was used for the selection of 798 radiomics features. Two classifiers for predicting MVI status and MVI risk were developed using multivariable logistic regression. We also performed a survival analysis to investigate the potentially prognostic value of the proposed MVI classifiers.

Results

The developed radiomics signature predicted MVI status with an area under the receiver operating characteristic curve (AUC) of .780, .776, and .743 in the training, test, and independent validation cohorts, respectively. The final MVI status classifier that integrated two clinical factors (age and α‐fetoprotein level) achieved AUC of .806, .803, and .796 in the training, test, and independent validation cohorts, respectively. For MVI risk stratification, the AUCs of the radiomics signature were .746, .664, and .700 in the training, test, and independent validation cohorts, respectively, and the AUCs of the final MVI risk classifier‐integrated clinical stage were .783, .778, and .740, respectively. Survival analysis showed that our MVI status classifier significantly stratified patients for short overall survival or early tumor recurrence.

Conclusions

Our CT radiomics‐based models were able to predict MVI status and MVI risk of HCC and might serve as a reliable preoperative evaluation tool.