Dual-path Network for Liver and Tumor Segmentation in CT Images Using Swin Transformer Encoding Approach.

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 segmentation of liver and tumor regions in medical imaging is crucial for the diagnosis, treatment, and monitoring of hepatocellular carcinoma (HCC) patients. However, manual segmentation is time-consuming and subject to inter- and intra-rater variability. Therefore, automated methods are necessary but require rigorous validation of high-quality segmentations based on a consensus of raters. To address the need for reliable and comprehensive data in this domain, we present LiverHccSeg, a dataset that provides liver and tumor segmentations on multiphasic contrast-enhanced magnetic resonance imaging from two board-approved abdominal radiologists, along with an analysis of inter-rater agreement.LiverHccSeg provides a curated resource for liver and HCC tumor segmentation tasks. The dataset includes a scientific reading and co-registered contrast-enhanced multiphasic magnetic resonance imaging (MRI) scans with corresponding manual segmentations by two board-approved abdominal radiologists and relevant metadata and offers researchers a comprehensive foundation for external validation, and benchmarking of liver and tumor segmentation algorithms. The dataset also provides an analysis of the agreement between the two sets of liver and tumor segmentations. Through the calculation of appropriate segmentation metrics, we provide insights into the consistency and variability in liver and tumor segmentations among the radiologists. A total of 17 cases were included for liver segmentation and 14 cases for HCC tumor segmentation. Liver segmentations demonstrates high segmentation agreement (mean Dice, 0.95 ± 0.01 [standard deviation]) and HCC tumor segmentations showed higher variation (mean Dice, 0.85 ± 0.16 [standard deviation]).The applications of LiverHccSeg can be manifold, ranging from testing machine learning algorithms on public external data to radiomic feature analyses. Leveraging the inter-rater agreement analysis within the dataset, researchers can investigate the impact of variability on segmentation performance and explore methods to enhance the accuracy and robustness of liver and tumor segmentation algorithms in HCC patients. By making this dataset publicly available, LiverHccSeg aims to foster collaborations, facilitate innovative solutions, and ultimately improve patient outcomes in the diagnosis and treatment of HCC.

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.

BackgroundCholangiolocellular carcinoma (CoCC) is a relatively rare primary liver tumor. We present a literature review and case report of a patient who presented with a slow-growing CoCC that was completely resected after a 5-year follow-up period.Case presentationThe patient was a 66-year-old man with a history of inflammatory thoracic and intra-abdominal pseudo-tumors. He was regularly followed up at our hospital for partial dilation of the pancreatic duct branch located in the body of the pancreas. Five years earlier, computed tomography (CT) demonstrated a small tumor in liver segment 4. Radiological findings were suggestive of hemangioma. Tumor size gradually increased during the 5-year follow-up period. CT scans showed that the tumor had progressed in size from 10 to 20 mm. Positron emission tomography CT revealed an accumulation of fluorodeoxyglucose (standardized uptake value max 5.3) at the tumor site. The tumor exhibited high intensity on T2-weighted and diffusion-weighted images of ethoxybenzyl magnetic resonance imaging. The tumor showed high intensity during the early phase but low intensity during the hepatobiliary phase. Tumor markers were within their respective normal ranges. Suspecting intrahepatic cholangiocarcinoma, left hepatectomy was performed. The tumor was diagnosed as CoCC based on pathological findings. The patient’s post-operative course was uneventful. The patient survived for a year, without any recurrence.ConclusionsIn cases dealing with small tumor sizes, it is difficult to distinguish between CoCC and hemangioma due to their similar radiological findings. Thus, it is important to consider the diagnosis of CoCC in small benign hepatic tumors. As such, follow-up radiological examination is recommended.

Renal cancer is one of ten most common cancers in human beings. The laparoscopic partial nephrectomy (LPN) becomes the main therapeutic approach in treating renal cancer. Accurate kidney and tumor segmentation in CT images is a prerequisite step in the surgery planning. However, automatic and accurate kidney and renal tumor segmentation in CT images remains a challenge. In this paper, we propose a new method to perform a precise segmentation of kidney and renal tumor in CT angiography images. This method relies on a three-dimensional (3D) fully convolutional network (FCN) which combines a pyramid pooling module (PPM). The proposed network is implemented as an end-to-end learning system directly on 3D volumetric images. It can make use of the 3D spatial contextual information to improve the segmentation of the kidney as well as the tumor lesion. The experiments conducted on 140 patients show that these target structures can be segmented with a high accuracy. The resulting average dice coefficients obtained for kidney and renal tumor are equal to 0.931 and 0.802 respectively. These values are higher than those obtained from the other two neural networks.

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.

HDTN: hybrid duo-transformer network for liver and hepatic tumor segmentation in CT images

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.

Automatic segmentation of organs at risk and tumors in CT images of lung cancer from partially labelled datasets with a semi-supervised conditional nnU-Net

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 cancer is a major global health challenge, significantly contributing to mortality rates. The accurate segmentation of liver and tumors from clinical CT images plays a crucial role in selecting therapeutic strategies for liver disease and treatment monitoring but remains challenging due to liver shape variability, proximity to other organs, low contrast between tumors and healthy tissues, and unclear lesion boundaries. To address these challenges, we propose the Deep Residual Dual-Attention Network (DRDA-Net), a novel model for end-to-end liver and tumor segmentation. DRDA-Net integrates a Residual UNet architecture with dual-attention mechanisms, multi-scale tile and patch extraction, and an Ensemble method. The dual-attention mechanisms enhance focus on key regions, addressing variations in size, shape, and intensity, while the multi-scale approach captures fine details and broader contexts. Additionally, we introduce a unique pre-processing pipeline employing a two-channel denoising technique using convolutional neural networks (CNNs) and stationary wavelet transforms (SWTs) to reduce noise while preserving structural details. Evaluated on the 3DIRCADb dataset, DRDA-Net achieved Dice scores of 97.03% and 75.4% for liver and tumor segmentation, respectively, outperforming state-of-the-art methods. These results demonstrate the model’s effectiveness in overcoming segmentation challenges and highlight its potential to improve liver cancer diagnostics and treatment planning.

Automated segmentation of liver and lesion in contrast-enhanced abdominal computed tomography (CT) scans is a crucial step for computer-assisted diagnosis and surgical planning of liver diseases. Although deep convolutional neural networks (DCNNs) have contributed many breakthroughs in liver and lesion segmentation task from CT images, the problem remains challenging due to low segmentation accuracy and high complexity. To address these issues, we proposed a light-weight convolutional neural networks for liver and lesion segmentation from CT images, i.e., LW-Mnet. Firstly, we improve the basic module of MobileNetv3 as the backbone of LW-Mnet. The second is that we employ LWASPP to capture multi-scale feature information for enhance representation power of network. The proposed network is evaluated on the public LiTS dataset, and the results show that LW-Mnet achieved the better performance than other networks with fewer parameters and faster inference speed.

Background: Lung cancer is among a top cause of cancer-related fatalities worldwide, and timely, accurate detection is essential to improving patient survival rates. Examining CT scans manually takes significant time and is susceptible to observer variation, creating the need for automated detection systems. Methods Used: This study presents an unsupervised segmentation framework for lung tumours in CT images using K-means and Hierarchical Clustering. The process involves grayscale conversion, noise filtering, contrast enhancement, clustering-based segmentation, and morphological post-processing. Results Achieved: Performance indicators such as the Dice Similarity Coefficient (DSC) and the Jaccard Index, tumor area, perimeter, and circularity show that hierarchical clustering provides more accurate and morphologically consistent results, while K-means is computationally faster but less precise. Concluding Remarks: The findings support the use of unsupervised clustering as an effective annotation-free approach for tumor segmentation and pave the way for future research into hybrid and deep learning-based models that can enhance segmentation accuracy and clinical applicability.

Accurate liver segmentation from computed tomography (CT) scans is essential for liver cancer diagnosis and liver surgery planning. Convolutional neural network (CNN)-based models have limited segmentation performance due to their localized receptive fields. Hybrid models incorporating CNNs and transformers that can capture long-range dependencies have shown promising performance in liver segmentation with the cost of high model complexity. Therefore, a new network architecture named G-UNETR++ is proposed to improve accuracy in liver segmentation with moderate model complexity. Two gradient-based encoders that take the second-order partial derivatives (the first two elements from the last column of the Hessian matrix of a CT scan) as inputs are proposed to learn the 3D geometric features such as the boundaries between different organs and tissues. In addition, a hybrid loss function that combines dice loss, cross-entropy loss, and Hausdorff distance loss is designed to address class imbalance and improve segmentation performance in challenging cases. The proposed method was evaluated on three public datasets, the Liver Tumor Segmentation (LiTS) dataset, the 3D Image Reconstruction for Comparison of Algorithms Database (3D-IRCADb), and the Segmentation of the Liver Competition 2007 (Sliver07) dataset, and achieved 97.38%, 97.50%, and 97.32% in terms of the dice similarity coefficient for liver segmentation on the three datasets, respectively. The proposed method outperformed the other state-of-the-art models on the three datasets, which demonstrated the strong effectiveness, robustness, and generalizability of the proposed method in liver segmentation.

Liver segmentation plays a crucial role mainly in tumor segmentation, 3D volume reconstruction and treatment planning of liver disease. In the present work, a comparative evaluation of two segmentation methods named as active contour and level set is presented. Comparative evaluation of both methods has been done individually by applying on Computed Tomography (CT) abdomen images. Comparative study is done by following steps (i) Preprocessing using morphological functions (ii) Segmentation of liver using active contour/level set algorithms (iii) 3-Dimensional volume reconstruction. Results of applied liver segmentation algorithms are compared with manually performed delineations by radiologist. Volume overlapping of above methods is assessed using Dice similarity coefficient (DSC) which gives mean DSC of 93.52%±.62 and 89.48%±.76 in case of active contour and level set method respectively. Finally it can be demonstrated that active contour is superior to level set in terms of accuracy, processing time and 3D volume reconstruction.