Embedded U-Net Liver Segmentation Model based on Multi-scale Semantic Information-MSN-Net Model

Segmentation of livers and liver tumors is one of the most important steps in radiation therapy of hepatocellular carcinoma. The segmentation task is often done manually, making it tedious, labor intensive, and subject to intra-/inter- operator variations. While various algorithms for delineating organ-at-risks (OARs) and tumor targets have been proposed, automatic segmentation of livers and liver tumors remains intractable due to their low tissue contrast with respect to the surrounding organs and their deformable shape in CT images. The U-Net has gained increasing popularity recently for image analysis tasks and has shown promising results. Conventional U-Net architectures, however, suffer from three major drawbacks. First, skip connections allow for the duplicated transfer of low resolution information in feature maps to improve efficiency in learning, but this often leads to blurring of extracted image features. Secondly, high level features extracted by the network often do not contain enough high resolution edge information of the input, leading to greater uncertainty where high resolution edge dominantly affects the network's decisions such as liver and liver-tumor segmentation. Thirdly, it is generally difficult to optimize the number of pooling operations in order to extract high level global features, since the number of pooling operations used depends on the object size. To cope with these problems, we added a residual path with deconvolution and activation operations to the skip connection of the U-Net to avoid duplication of low resolution information of features. In the case of small object inputs, features in the skip connection are not incorporated with features in the residual path. Furthermore, the proposed architecture has additional convolution layers in the skip connection in order to extract high level global features of small object inputs as well as high level features of high resolution edge information of large object inputs. Efficacy of the modified U-Net (mU-Net) was demonstrated using the public dataset of Liver tumor segmentation (LiTS) challenge 2017. For liver-tumor segmentation, Dice similarity coefficient (DSC) of 89.72 %, volume of error (VOE) of 21.93 %, and relative volume difference (RVD) of - 0.49 % were obtained. For liver segmentation, DSC of 98.51 %, VOE of 3.07 %, and RVD of 0.26 % were calculated. For the public 3D Image Reconstruction for Comparison of Algorithm Database (3Dircadb), DSCs were 96.01 % for the liver and 68.14 % for liver-tumor segmentations, respectively. The proposed mU-Net outperformed existing state-of-art networks.

Accurate liver segmentation is essential for radiation therapy planning of hepatocellular carcinoma and absorbed dose calculation. However, liver segmentation is a challenging task due to the anatomical variability in both shape and size and the low contrast between liver and its surrounding organs. Thus we propose a convolutional neural network (CNN) for automated liver segmentation. In our method, fractional differential enhancement is firstly applied for preprocessing. Subsequently, an initial liver segmentation is obtained by using a CNN. Finally, accurate liver segmentation is achieved by the evolution of an active contour model. Experimental results show that the proposed method outperforms existing methods. One hundred fifty CT scans are evaluated for the experiment. For liver segmentation, Dice of 95.8%, true positive rate of 95.1%, positive predictive value of 93.2%, and volume difference of 7% are calculated. In addition, the values of these evaluation measures show that the proposed method is able to provide a precise and robust segmentation estimate, which can also assist the manual liver segmentation task.

Medical image classification: Knowledge transfer via residual U-Net and vision transformer-based teacher-student model with knowledge distillation

Due to low contrast and the blurred boundary between liver tissue and neighboring organs sharing similar intensity values, the problem of liver segmentation from CT images has not yet achieved satisfactory performance and remains a challenge. To alleviate these problems, we introduce deep supervision (DS) and atrous inception (AI) technologies with conditional random field (CRF) and propose three major improvements that are experimentally shown to have substantive and practical value. First, we replace the encoder's standard convolution with the residual block. Residual blocks can increase the depth of the network. Second, we provide an AI module to connect the encoder and decoder. AI allows us to obtain multi-scale features. Third, we incorporate the DS mechanism into the decoder. This helps to make full use of information of the shallow layers. In addition, we employ the Tversky loss function to balance the segmented and non-segmented regions and perform further refinement with a dense CRF. Finally, we extensively validate the proposed method on three public databases: LiTS17, 3DIRCADb, and SLiver07. Compared to the state-of-the-art methods, the proposed method achieved increased segmentation accuracy for the livers with low contrast and the fuzzy boundary between liver tissue and neighboring organs and is, therefore, more suited for automatic segmentation of these livers.

Liver segmentation in CT images is a complex and challenging process. This is due to wide variability of liver sizes and shapes from one image to another, in addition to the inhomogeneity of the gray-level within the liver region and the low contrast to the background levels. In this paper, a fully automatic approach for liver segmentation is introduced. The approach consists of three main stages; pre-processing, segmentation and post processing. Watershed segmentation algorithm is used in the main processing stage to detect the borders and edges accurately between the liver regions and the background. However, because of the over-segmentation caused by the watershed algorithm, region merging algorithm is applied in the post processing stage. The merging criteria were proposed to maximize the disparity between the liver regions and the background and in the same time to keep the variance of the gray-level in the liver regions under certain threshold. The algorithm achieved 91% overall accuracy when evaluated using CT images from the MICCAI dataset.

Liver and tumor segmentation from CT images is a complex and crucial step in achieving full-course adaptive radiotherapy and also plays an essential role in computer-aided clinical diagnosis systems. Deep learning-based methods play an important role in achieving automatic segmentation. This research aims to improve liver tumor detection performance by proposing a dual path feature extracting strategy and employing Swin-Transformer. The hierarchical Swin-Transformer is embedded into the encoder and decoder and combined with CNN to form a dual coding path structure incorporating long-range dependencies and multi-scale contextual connections to capture coarse-tuned features at different semantic scales. The features of the two encoding paths and the upsampling path are fused, tested and validated with LITS and in-house datasets. The proposed method has a DG of 97.95% and a DC of 96.2% for liver segmentation; a DG of 80.6% and a DC of 68.1% for tumor segmentation; and a classification study of the tumor dataset shows a DG of 91.1% and a DC of 87.2% for large and continuous tumors and a DG of 71.7% and a DC of 66.4% for small and scattered tumors. Swin-Transformer can be used as a robust encoder for medical image segmentation networks and, combined with CNN networks, can better recover local spatial information and enhance feature representation. Accurate localization before segmentation can achieve better results for small and scattered tumors.

The analysis of CT images of the abdomen has become increasingly important as lesions occurring in the liver are not easily detectable. The development of medical imaging technology has led to an explosive growth in the number of pathological sections of CT images. In the field of medical imaging, the use of computer-aided techniques for liver segmentation of abdominal CT images is particularly important. To further improve the accuracy of liver segmentation in abdominal CT images, this paper proposes the U2-OANet liver segmentation network on the basis of U2Net. This paper combines the new octave convolution module to design the OCRSU module, which produces better results for segmentation of liver boundaries while reducing the video memory usage. At the same time, this paper adds a dual attention module including the bottom attention module and the mixed attention module, to ensure the liver edge details while alleviating the noise problem and the false-positive problem, and further optimizes the liver segmentation network. Compared with the U2Net network, the U2-OANet network proposed in this paper has effectively improved the liver segmentation accuracy on CHAOS and 3DIRCADB datasets.

Segmentation of liver in CT images is regarded as a challenge in image processing due to low-contrast of datasets, variety of liver shape, and its non-uniform texture; especially for abnormal cases. In this paper, we deal with normal and abnormal datasets as images containing two or more Gaussian components. We threshold a slice in a narrow band of each mode, find liver pixels based on a priori knowledge, prepare a probability map, and threshold the map to find initial liver border. Final boundary of liver is obtained through a few iterations of `Geodesic Active Contour'. The proposed method was tested on 30 normal and 17 abnormal datasets each containing 159-263 slices; acquired from different CT machines. The results for normal and abnormal datasets are completely acceptable, according to the evaluation done by a specialist. However, for severely abnormal datasets, the proposed method is regarded as a promising algorithm for liver segmentation.

The structure and morphology of hepatic vessels play an important role in the discovery and diagnosis of liver lesions. In this paper, a vascular segmentation method for liver ct image based on Hessian enhancement algorithm is proposed, and the hepatic vessels in CT image are segmented and extracted more accurately. Firstly, the improved Hessian filtering algorithm is used to enhance the structure of vascular branch. The other two classical algorithms are compared in the aspects of algorithm running efficiency, signal-to-noise ratio and image quality, and it is proved that the improved Hessian filtering algorithm in scripture is preprocessed. High image quality. Then the level set method based on threshold velocity function is used to remove the non-vascular region with low gray value by adding threshold constraints on the basis of the velocity function of the general level set method, which can effectively reduce the phenomenon of over-segmentation. The experimental results show that compared with the other three classical algorithms, the proposed method has better results in segmentation accuracy, over-segmentation rate, under-segmentation rate, sensitivity and specificity.

4113 Background: Hepatocellular carcinoma (HCC) is the most common primary liver tumor, and the liver is a frequent site of metastasis of other cancers. High tumor burden, proximity to hepatic vessels, and other comorbidities render only 30% of patients as candidates for curative treatment: transplantation, resection, or ablation. Surgical resection requires a 20 - 40% future liver remanent (FLR) to avoid post-operative complications. Delineation of the Couinaud segments is essential for volumetric analysis of FLR and targeted localization of tumors during pre-surgical treatment planning. Currently, manual annotation of the Couinaud segments in one CT volume can take two or more hours, which makes it cumbersome, and can benefit from automation. The purpose of this work is to develop an automated Couinaud segmentation tool for fast and accurate FLR estimation. Methods: Three CT datasets were used: 1) 161 patients from the public Medical Segmentation Decathlon (MSD) Hepatic Vessels dataset, 2) 43 patients with cirrhosis and metabolic diseases having ascites and splenomegaly imaged at the National Institutes of Health (NIH), and 3) 197 patients in the public TCIA Colorectal Liver Metastasis (CRLM) dataset. FLR annotation in the CRLM dataset was done by an expert radiologist. The Couinaud segments in the MSD and NIH datasets were manually annotated by two physicians using ITK-SNAP. The MSD and NIH datasets were used for training, while the CRLM dataset was reserved for testing. A 3D nnU-Net model was trained with default hyperparameters to outline the Couinaud segments. On the test dataset, the predicted Couinaud segments were overlaid on the FLR annotation, and metrics, such as Dice Similarity Coefficient (DSC), Hausdorff Distance (HD) error (in mm), and volume error (in cc) were calculated. The performance was compared to a previously described 3D U-Net model developed to quantify liver segmental volume ratio (LSVR) in patients with cirrhosis. Results: The 3D nnU-Net obtained a DSC of 0.99 ± 0.01 (IQR: 0.991, 0.998), HD error of 0.87 ± 1.83 mm (IQR: 0, 1.02), and volume error of 13.7 ± 28.1 cc (IQR: 3.4, 15.3). In contrast, the LSVR U-Net model attained a DSC of 0.97 ± 0.01 (IQR: 0.972, 0.984), HD error of 9.56 ± 3.61 mm (IQR: 7.14, 11.93), and volume error of 47.9 ± 51.8 cc (IQR: 24.5, 55.4). The 3D nnU-Net model achieved significantly different results for DSC (p < 0.001, large effect size 0.86), HD error (p < 0.001, large effect size 0.87), and volume error (p < 0.001, large effect size 0.91). Conclusions: The model showed acceptable generalizability to the external TCIA CRLM dataset. Future work may be directed towards accurate volumetric analysis on patients undergoing portal vein embolization to increase the FLR, and automatic tumor localization on specific Couinaud segments.

Background: The accurate segmentation of the appendix with well-defined boundaries is critical for diagnosing conditions such as acute appendicitis. The manual identification of the appendix is time-consuming and highly dependent on the expertise of the radiologist. Method: In this study, we propose a fully automated approach to the detection of the appendix using deep learning architecture based on the U-Net with specific training parameters in CT scans. The proposed U-Net architecture is trained on an annotated original dataset of abdominal CT scans to segment the appendix efficiently and with high performance. In addition, to extend the training set, data augmentation techniques are applied for the created dataset. Results: In experimental studies, the proposed U-Net model is implemented using hyperparameter optimization and the performance of the model is evaluated using key metrics to measure diagnostic reliability. The trained U-Net model achieved the segmentation performance for the detection of the appendix in CT slices with a Dice Similarity Coefficient (DSC), Volumetric Overlap Error (VOE), Average Symmetric Surface Distance (ASSD), Hausdorff Distance 95 (HD95), Precision (PRE) and Recall (REC) of 85.94%, 23.29%, 1.24 mm, 5.43 mm, 86.83% and 86.62%, respectively. Moreover, our model outperforms other methods by leveraging the U-Net’s ability to capture spatial context through encoder–decoder structures and skip connections, providing a correct segmentation output. Conclusions: The proposed U-Net model showed reliable performance in segmenting the appendix region, with some limitations in cases where the appendix was close to other structures. These improvements highlight the potential of deep learning to significantly improve clinical outcomes in appendix detection.

Computational resources, which presents a significant challenge in resourceconstrained environments, particularly in developing countries. Consequently, the development of decoding mechanisms that are both computationally efficient and lightweight is imperative. However, the performance of medical image segmentation is frequently limited by the simplicity of decoder designs. Balancing the optimization of decoder architectures with the reduction of computational demands while maintaining high model accuracy remains a formidable challenge. In this context, we introduce a novel decoder that integrates line deformable convolution and multi-scale self-attention (LDMSD). The multi-scale self-attention enhancement module within LDMSD leverages two distinct multi-scale self-attention mechanisms, thereby substantially improving the representational capacity of the feature maps. Furthermore, the decoder incorporates a linear deformable convolution attention-guided mechanism to augment the feature maps derived from skip connections. This mechanism effectively mitigates the inherent limitations of conventional convolution and enhances the model's ability to capture complex semantic relationships within the feature maps. Through this collaborative mechanism, LDMSD is able to capture target information from both global and multiscale perspectives, accurately locate the target's boundaries and structures, while maintaining its lightweight nature. Experimental results demonstrate that LDMSD outperforms the state-of-the-art decoders in terms of performance metrics, achieving a reduction in Floating Point Operations (FLOPs) by 77.36% and in parameter count by 81.66% when compared to the Cascaded Attention Decoder (CASCADE). To substantiate the efficacy of the proposed method, extensive experiments are conducted on six publicly available datasets. The results validate that the proposed method surpasses existing approaches in medical image segmentation tasks, both in terms of accuracy and computational efficiency.

Liver tumour segmentation from abdominal CT image is a challenging task in biomedical image processing. Complex segmentation methods may provide good segmentation results but are difficult for implementing in clinical environment. Hence, in this paper, we propose a simple thresholding technique based on fuzzy logic and Shannon's entropy function to segment the tumour from liver CT images. Tumour dimensions are then measured subsequently. The proposed method provides better results than the classical segmentation methods.

The purpose of this study is to evaluate and compare the performance of modern deep learning techniques for automatically recognizing and segmenting multiple organ regions on 3D CT images. CT image segmentation is one of the important task in medical image analysis and is still very challenging. Deep learning approaches have demonstrated the capability of scene recognition and semantic segmentation on nature images and have been used to address segmentation problems of medical images. Although several works showed promising results of CT image segmentation by using deep learning approaches, there is no comprehensive evaluation of segmentation performance of the deep learning on segmenting multiple organs on different portions of CT scans. In this paper, we evaluated and compared the segmentation performance of two different deep learning approaches that used 2D- and 3D deep convolutional neural networks (CNN) without- and with a pre-processing step. A conventional approach that presents the state-of-the-art performance of CT image segmentation without deep learning was also used for comparison. A dataset that includes 240 CT images scanned on different portions of human bodies was used for performance evaluation. The maximum number of 17 types of organ regions in each CT scan were segmented automatically and compared to the human annotations by using ratio of intersection over union (IU) as the criterion. The experimental results demonstrated the IUs of the segmentation results had a mean value of 79% and 67% by averaging 17 types of organs that segmented by a 3D- and 2D deep CNN, respectively. All the results of the deep learning approaches showed a better accuracy and robustness than the conventional segmentation method that used probabilistic atlas and graph-cut methods. The effectiveness and the usefulness of deep learning approaches were demonstrated for solving multiple organs segmentation problem on 3D CT images.

For proper diagnosis and treatment of various retinal conditions, retinal blood vessel segmentation is crucial. Delineation of vessels with varying thicknesses is critical for detecting disease symptoms. However, this task is challenging due to inadequate contextual information, complex vessel morphology, and lesion confusion. Many recent works employed several variations of CNNs with U-Net as baseline model for segmenting blood vessels from the fundus images. However, the existing methods still lack in generalizing the vessels well enough, indicating scope for improvement in this challenging problem of vessel segmentation. We introduce a novel Multi-Scale Residual (MSR) U-Net model in this study replacing convolution block and skip connections with an improved Multi-Scale Residual (MSR) convolution block and Bottleneck residual paths (B-Res paths) respectively. Specifically, STARE, DRIVE, and CHASE_DB1 datasets of fundus images are used to validate the proposed segmentation method. Our experimental results consistently showcase better/comparable performances when compared with current approaches, achieving higher area under receiver operator characteristic (AUC), accuracy, and F1 score. in segmenting blood vessels of varying thicknesses, even in scenarios with diverse contextual information, the presence of coexisting lesions, and complex vessel morphologies.