SynLiTS: Phase Prompting-Driven Diffusion Synthesis and Context-Aware Fusion for Unaligned Liver Tumor Segmentation.

Cancer plays a major risk for public health worldwide. According to the survey made by the cancer society predicts approximate about 42,220 new cases will be diagnosed and around 32,220 people will die of this cancer that is around 71% of people will die in 2018 and Liver cancer rate is increased by 3% for every year since 2000 and achieved second leading place for the cause of death. There is a con-tinuous in the development with regard to prevent and different options for treating the cancer. Detection of cancer at its initial stages is very difficult with the help of pathological information’s, so as any added support CAD systems using CT scan images are being designed from few decades in order to find out cancer in its early stage. In this paper discussed various segmentation techniques and liver tumor detection techniques to initial segment out the liver region from the abdominal and then to extract the efficient characteristics. Based on the characteristics presences of tumour is identified and separated out from the liver and finally analyse the stage of the cancer. Therefore the process is divided into three parts; 1.Region segmentation, 2.Liver Tumour segmentation and 3.Detection of Cancer stage. In this paper, study is done on different methods of liver region and tumour segmentation of abdominal CT scan to analyze liver tumor and detection of early stage of the tumor

Liver cancer is a life threatening illness and one of the fastest growing cancer in the world.Segmenting liver tumors from computed tomography(CT) images is crucial for the diagnosis and management of liver cancer.The variety of appearances, hazy borders, variable densities, forms and sizes of lesions of tumors makes it challenging .This work introduces a liver tumor segmentation method based on the Liver Tumor Segmentation (LITS) dataset.U-net model is used to segment the lesions in the tumor.A convolutional neural network architecture created especially for semantic segmentation problems is used to implement a Unet model for liver tumor segmentation.U-Net and Seg-Net models demonstrate better segmentation performance by utilizing the extensive annotated data present in the LITS dataset.To produce the segmentation output , the encoder down samples the input picture through convolutional and pooling layers, while the decoder up samples the feature map.The last layer is sigmoid activation function the task is likely binary segmentation.The outcomes of the experiment confirm the efficacy of the suggested method, demonstrating its potential to improve diagnostic precision in medical environments.Dice-coefficient, Dice-loss,F1 score are the metrics used to calculate the performance of the segmented images

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.

Accurate liver tumor segmentation is critical for the diagnosis and treatment of liver cancer, yet remains challenging due to image quality and tumor heterogeneity. Existing methods predominantly focus on single-phase and single-time-point CT scans, neglecting complementary multi-phase information and longitudinal correlations. In this paper, we propose the first multi-modal multi-temporal network, which integrates diverse information from arterial phase and longitudinal data to facilitate liver tumor segmentation of venous phase. Our approach mainly comprises three novel blocks: (1) A polynomial residual block that captures abundant tumor characteristics via nonlinear hierarchical feature learning; (2) A longitudinal phase attention block that refines input features from channel and spatial dimensions, and recalibrates the target feature based on inter-phase and inter-temporal dependencies; (3) A progressive up-sampling block that preserves structural details during resolution restoration, and forms an effective decoder architecture. Experiments on a large-scale CT dataset of longitudinal multi-phase liver tumors demonstrate that the proposed network achieves promising liver tumor segmentation and outperforms state-of-the-art methods. In addition, an ablation study validates the functions of the proposed blocks.

Objective: Segmentation of liver and liver tumors from 3D medical images is a challenging and computationally expensive task. Organs that are in close proximity may have similar shape, texture, and intensity, which makes it difficult for accurate segmentation. Accurate segmentation of liver tumors is important for diagnosis and treatment planning of liver cancer. Methods: A hybrid model with a U-Net based structure and the Whale Optimization Algorithm (WOA) was proposed. WOA was used to optimize the hyperparameters of the conventional LiTS-Res-UNet to obtain the best segmentation performance of the deep learning model. Results: The LiTS-Res-Unet + WOA hybrid model achieved a performance of 99.54% for accuracy, with a Dice coefficient of 92.38% and a Jaccard index of 86.73% on the benchmark dataset, outperforming state-of-the-art methods. Conclusions: The WOA-based adaptive search space was able to obtain an optimal set of hyperparameters for deep learning model convergence while increasing the accuracy of the model in the proposed hybrid model. The robust performance and clinical applicability of the model in liver tumor segmentation were demonstrated.

Accurate segmentation of the liver and liver tumour (LT) is challenging due to its hazy boundaries and large shape variability. Although using U-Net for liver and LT segmentation achieves better results than manual segmentation, it loses spatial and channel features during segmentation, leading to inaccurate liver and LT segmentation. A residual deformable split depth-wise separable U-Net (RDSDSU-Net) is proposed to increase the accuracy of liver and LT segmentation. The residual deformable convolution layer (DCL) with deformable pooling (DP) is used in the encoder as an attention mechanism to adaptively extract liver and LT shape and position characteristics. Afterward, a convolutional spatial and channel features split graph network (CSCFSG-Net) is introduced in the middle processing layer to improve the expression capability of the liver and LT features by capturing spatial and channel features separately and to extract global contextual liver and LT information from spatial and channel features. Sub-pixel convolutions (SPC) are used in the decoder section to prevent the segmentation results from having a chequerboard artefact effect. Also, the residual deformable encoder features are combined with the decoder through summation to avoid increasing the number of feature maps (FM). Finally, the efficiency of the RDSDSU-Net is evaluated on the 3DIRCADb and LiTS datasets. The DICE score of the proposed RDSDSU-Net achieved 98.21% for liver segmentation and 93.25% for LT segmentation on 3DIRCADb. The experimental outcomes illustrate that the proposed RDSDSU-Net model achieved better segmentation results than the existing techniques.

Liver tumor segmentation on computed tomography slices is a very difficult task because the medical images are often corrupted by noise and sampling artifacts. Besides, liver tumors are often surrounded by other abdominal structures with similar densities. Therefore, they often show the phenomenon of intensity inhomogeneity. These restrict the liver tumor segmentation. People tried to use traditional level set methods to segment the liver tumor, but the results were not satisfying due to the noise and the low gradient response on the liver tumor boundary. In this paper, we propose a multidistribution level set method which can overcome the insufficient segmentation and over-segmentation problems. We have done many experiments and compared our approach with the CV model and LSACM model. We also use the proposed method to segment the public data set from the "3D Liver Tumor Segmentation Challenge". All results reveal that our method is better even for liver tumors with low contrast and blurred boundaries.

Weakly-Supervised teacher-Student network for liver tumor segmentation from non-enhanced images.

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.

The incidence of liver tumors is among the top three in China. The treatments of benign and malignant tumors are different. Accurate diagnosis plays an important role in guiding the treatment of tumors. The aim of this study is to solve the following: (1) blurred boundary between the liver tumor and other organs causes incorrect segmentation of liver tumor boundaries; (2) large difference in tumor size and the diversity in texture and grayscale are major challenges in liver tumor classification tasks. Firstly, the liver tumor is segmented from the original CT images by a tumor segmentation network, UNet++ with fusion loss and atrous spatial pyramid pooling (FLAS-UNet++). The proposed segmentation method can solve the problem of tumor edge segmentation error by learning the tumor edge information. Secondly they are adaptively cropped according to the tumor volume to reduce the over-fitting and over-sensitivity of the deep network. Thirdly an improved Dense Block is designed to pay more attention to the changes in grayscale and texture between benign and malignant tumors. Finally, the features extracted from the network combined with tumor volume, patient's sex and age, are sent to a classifier for diagnosis. Liver tumor segmentation results show that the dice, HD95 reached 71.9%, 12.1 mm, respectively. The classification results show that the accuracy, specificity, sensitivity and area under curve reached 82.4%, 79.8%, 84.4%, 87.5%, respectively. The segmentation and classification results are both better than other's methods and mainstream networks. In order to solve existing problems of liver tumor CT image classification methods, our method realizes the accurate segmentation and classification of liver tumors in CT images and has important clinical application value.

Background: Automatic segmentation of liver regions as well as liver lesions such as hepatocellular carcinoma (HCC) from computed tomography (CT) images is critical for accurate diagnosis and therapy planning. With the advent of deep learning techniques such as transformers, computer-aided diagnostic tools (CADs) have the potential to increase the accuracy of liver tumor diagnosis, progression, and treatment planning. However, two major challenges remain: 1) existing models struggle to extract robust spatial features for accurate liver and liver lesion segmentation, and 2) publicly available liver datasets with HCC annotations are limited. Methods: We first present a new liver dataset acquired from Chongqing University Cancer Hospital (CCH-LHCC-CT) with HCC annotations. Second, we developed a novel deep learning architecture (ICT-Net), which is constructed based on a pretrained transformer encoder in conjunction with an advanced feature upscaling and enhanced convolution-transformer decoder formation. Results: We performed liver and liver tumor segmentation on the CCH-LHCC-CT and three public CT liver datasets. The proposed ICT-Net architecture achieves superior accuracy (higher ACC/DSC/IoU, lower HD95) across all datasets. Conclusions: We construct a novel deep-learning architecture that produces robust information for liver and liver tumor segmentation. The statistical and visual results demonstrate that the proposed ICT-Net outperforms other existing approaches investigated in this study in terms of ACC, DSC, and IoU. Clinical Translation Statement: ICT-Net enhances surgical planning accuracy through precise tumor margin delineation and improves therapy response assessment reliability, which holds meaningful promise to support more precise and effective clinical therapeutic strategies for patients with HCC.

Automatic and efficient liver tumor detection in multi-phase CT images is essential in computer-aided diagnosis of liver tumors. Nowadays, deep learning has been widely used in medical applications. Normally, deep learning-based AI systems need a large quantity of training data, but in the medical field, acquiring sufficient training data with high-quality annotations is a significant challenge. To solve the lack of training data issue, domain adaptation-based methods have recently been developed as a technique to bridge the domain gap across datasets with different feature characteristics and data distributions. This paper presents a domain adaptation-based method for detecting liver tumors in multi-phase CT images. We adopt knowledge for model learning from PV phase images to ART and NC phase images. Clinical Relevance- To minimize the domain gap we employ an adversarial learning scheme with the maximum square loss for mid-level output feature maps using an anchorless detector. Experiments show that our proposed method performs much better for various CT-phase images than normal training.

Precise liver tumors and associated organ segmentation hold immense value for surgical and radiological intervention, enabling anatomical localization for pre-operative planning and intra-operative guidance. Modern deep learning models for medical image segmentation have evolved from convolution neural networks to transformer architectures, significantly boosting global context understanding. However, accurate delineation especially of hepatic lesions remains an enduring challenge due to models’ predominant focus solely on spatial feature extraction failing to adequately characterize complex medical anatomies. Moreover, the relative paucity of expertly annotated medical imaging data restricts model exposure to diverse pathological presentations. In this paper, we present a three-phrased cascaded segmentation framework featuring an X-Fuse model that synergistically integrates spatial and frequency domain’s complementary information in dual encoders to enrich latent feature representation. To enhance model generalizability, building upon X-Fuse topology and taking advantage of additional unlabeled pathological data, our proposed integration of curriculum pseudo-labeling with Jensen–Shannon variance-based uncertainty rectification promotes optimized pseudo supervision in the context of semi-supervised learning. We further introduce a tumor-focus augmentation technique including training-free copy-paste and knowledge-based synthesis that show efficacy in simplicity, contributing to the substantial elevation of model adaptability on diverse lesional morphologies. Extensive experiments and modular evaluations on a holdout test set demonstrate that our methods significantly outperform existing state-of-the-art segmentation models in both supervised and semi-supervised settings, as measured by the Dice similarity coefficient, achieving superior delineation of bones (95.42%), liver (96.26%), and liver tumors (89.53%) with 16.41% increase comparing to V-Net on supervised-only and augmented-absent scenario. Our method marks a significant step toward the realization of more reliable and robust AI-assisted diagnostic tools for liver tumor intervention. We have made the codes publicly available [https://github.com/lyupengju/X-Fuse].

In this paper, we propose an approach to detection and segmentation of liver tumors in noisy computed topography (CT) images. Image segmentation plays crucial role in medical image processing applications. Noise is quite common in medical images. It occurs while acquisition and transmission of images. The essential goal of this framework is to identify liver cancer by segmenting liver tumors from noisy CT scan images. Liver segmentation is difficult task in medical applications because inter-patient variability in size, shape and disease. In general CT scanning is used to inspect liver cancer. In this research work, the liver tumors are detected by the medical images in three stages, pre-processing stage, processing stage and detection stage. First in pre-processing stage, median filter is used to remove the noise from CT image, and then the denoised image is segmented by fuzzy c-means clustering (FCM) algorithm. Finally in the detection stage distance regularized level set evolution (DRLSE) is used to extract tumor boundaries. This algorithm is very much useful for identifying hepatocellular carcinoma (liver cancer). Experimental results on various noisy CT scan images show that the proposed method is efficient for extracting hepatic tumors from liver.

Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, we propose an effective and efficient method for tumor segmentation in liver CT images using encoder-decoder based octave convolution networks. Compared with other convolution networks utilizing standard convolution for feature extraction, the proposed method utilizes octave convolutions for learning multiple-spatial-frequency features, thus can better capture tumors with varying sizes and shapes. The proposed network takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference. More importantly, we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties, and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. Finally, we integrate octave convolutions into the encoder-decoder architecture of UNet, which can generate high resolution tumor segmentation in one single forward feeding without post-processing steps. Both architectures are trained on a subset of the LiTS (Liver Tumor Segmentation) Challenge. The proposed approach is shown to significantly outperform other networks in terms of various accuracy measures and processing speed.