Three-Dimensional Liver Image Segmentation Using Generative Adversarial Networks Based on Feature Restoration

Medical imaging provides a powerful tool for medical diagnosis. In the process of computer-aided diagnosis and treatment of liver cancer based on medical imaging, accurate segmentation of liver region from abdominal CT images is an important step. However, due to defects of liver tissue and limitations of CT imaging procession, the gray level of liver region in CT image is heterogeneous, and the boundary between the liver and those of adjacent tissues and organs is blurred, which makes the liver segmentation an extremely difficult task. In this study, aiming at solving the problem of low segmentation accuracy of the original 3D U-Net network, an improved network based on the three-dimensional (3D) U-Net, is proposed. Moreover, in order to solve the problem of insufficient training data caused by the difficulty of acquiring labeled 3D data, an improved 3D U-Net network is embedded into the framework of generative adversarial networks (GAN), which establishes a semi-supervised 3D liver segmentation optimization algorithm. Finally, considering the problem of poor quality of 3D abdominal fake images generated by utilizing random noise as input, deep convolutional neural networks (DCNN) based on feature restoration method is designed to generate more realistic fake images. By testing the proposed algorithm on the LiTS-2017 and KiTS19 dataset, experimental results show that the proposed semi-supervised 3D liver segmentation method can greatly improve the segmentation performance of liver, with a Dice score of 0.9424 outperforming other methods.

Radiation therapy with neutrons and hydrons of the liver region using the MCNP code: المعالجة الاشعاعية بالنترونات والهدرونات لمنطقة الكبد باستخدام الكود MCNP

CT images were read and a 3D model of the tumor was created in the liver area, Then the values ​​of the radiation dose in terms of the depth resulting from (photons, neutrons and protons) were estimated and studied using the code (MCNP) after entering the data into it. The value of the radiation dose in terms of depth and curvature in photons, neutrons and protons radiation therapy was studied, from our findings in the research we note that protons are the best option for radiation therapy for high-depth liver cancer of photons and neutrons due to the lower dose at entry compared to the dose absorbed in the tumor area and its ability to deliver a greater amount of dose of neutrons and photons to the tumor area. We note that the values reached are acceptable for the treatment of tumors at a depth close to the surface. As for a large-depth tumor, it is necessary to increase high-energy radiation doses deep in the tumor area by accelerating proton therapy to protect natural organs from high-energy radiation doses.

U-Net combined with multi-scale attention mechanism for liver segmentation in CT images

Background The liver is an important organ that undertakes the metabolic function of the human body. Liver cancer has become one of the cancers with the highest mortality. In clinic, it is an important work to extract the liver region accurately before the diagnosis and treatment of liver lesions. However, manual liver segmentation is a time-consuming and boring process. Not only that, but the segmentation results usually varies from person to person due to different work experience. In order to assist in clinical automatic liver segmentation, this paper proposes a U-shaped network with multi-scale attention mechanism for liver organ segmentation in CT images, which is called MSA-UNet. Our method makes a new design of U-Net encoder, decoder, skip connection, and context transition structure. These structures greatly enhance the feature extraction ability of encoder and the efficiency of decoder to recover spatial location information. We have designed many experiments on publicly available datasets to show the effectiveness of MSA-UNet. Compared with some other advanced segmentation methods, MSA-UNet finally achieved the best segmentation effect, reaching 98.00% dice similarity coefficient (DSC) and 96.08% intersection over union (IOU).

An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data

Background To estimate median liver iron concentration (LIC) calculated from magnetic resonance imaging, excluded vessels of the liver parenchyma region were defined manually. Previous works proposed the automated method for excluding vessels from the liver region. However, only user-defined liver region remained a manual process. Therefore, this work aimed to develop an automated liver region segmentation technique to automate the whole process of median LIC calculation. Methods 553 MR examinations from 471 thalassemia major patients were used in this study. LIC maps (in mg/g dry weight) were calculated and used as the input of segmentation procedures. Anatomical landmark data were detected and used to restrict ROI. After that, the liver region was segmented using fuzzy c-means clustering and reduced segmentation errors by morphological processes. According to the clinical application, erosion with a suitable size of the structuring element was applied to reduce the segmented liver region to avoid uncertainty around the edge of the liver. The segmentation results were evaluated by comparing with manual segmentation performed by a board-certified radiologist. Results The proposed method was able to produce a good grade output in approximately 81% of all data. Approximately 11% of all data required an easy modification step. The rest of the output, approximately 8%, was an unsuccessful grade and required manual intervention by a user. For the evaluation matrices, percent dice similarity coefficient (%DSC) was in the range 86–92, percent Jaccard index (%JC) was 78–86, and Hausdorff distance (H) was 14–28 mm, respectively. In this study, percent false positive (%FP) and percent false negative (%FN) were applied to evaluate under- and over-segmentation that other evaluation matrices could not handle. The average of operation times could be reduced from 10 s per case using traditional method, to 1.5 s per case using our proposed method. Conclusion The experimental results showed that the proposed method provided an effective automated liver segmentation technique, which can be applied clinically for automated median LIC calculation in thalassemia major patients.

The Assessment of Regional Liver Function Before Major Hepatectomy Using Magnetic Resonance Imaging

Background The liver-to-spleen signal intensity ratio (LSR) on magnetic resonance imaging with gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid has been used as a parameter to assess liver function. LSR of the future remnant liver region (FR-LSR) is included in preoperative assessment of regional liver function. The aim of this study was to investigate the predictability of post-hepatectomy liver failure (PHLF) by FR-LSR. Methods Between May 2013 and May 2019, 127 patients underwent standardized EOB-MRI for diagnosis of liver tumor before major hepatectomy. The FR-LSR on EOB-MRI was calculated by a semiautomated three-dimensional volumetric analysis system. The cutoff value of FR-LSR in association with clinically relevant PHLF was determined according to the areas under the receiver operating characteristic curves. Then, FR-LSR and clinical variables were analyzed to assess the risk of clinically relevant PHLF. Results In patients with preoperative biliary drainage, metastatic liver tumor, estimated future remnant liver volume <50%, biliary reconstruction, operation time ≥ 480 min, estimated blood loss ≥ 1000 g, blood transfusion and a FR-LSR < 2.00 were associated with clinically relevant PHLF ( P < .05 for all) in univariable analysis. The liver-to-spleen signal intensity ratio of the future remnant liver region < 2.00 was the only independent risk factor for clinically relevant PHLF in multivariable risk analysis (OR, 27.90; 95% CI: 7.99-136.40; P < .05). Discussion The present study revealed that FR-LSR calculated using a 3-dimensional volumetric analysis system was an independent risk factor for clinically relevant PHLF. The liver-to-spleen signal intensity ratio of the future remnant liver region might be a reliable preoperative parameter in liver functional assessment, enabling safe performance of major hepatectomy.

Accurate Liver Border Identification Model in CT Scan Images

The liver is an important organ in human body with certain variations in its edges, color, shape and pixel intensity distribution. These uncertainties may be because of various liver pathologies, hereditary or both. Along with it, liver has close proximity to its nearby organs. Hence, identifying liver in scanned images is a challenging step in image processing. This task becomes more imprecise when liver diseases are present at the edges. The liver segmentation is prerequisite for liver volumetry, computer-based surgery planning, liver surgery modelling, surgery training, 3D view generation, etc. The proposed hybrid segmentation method overcomes the problems and identifies liver boundary in Computed-Tomography (CT) scan images accurately. In this paper, the first step is to study statistics of pixel intensity distribution within liver image, and novel methodology is designed to obtain thresholds. Then, threshold-based segmentation is applied which separates the liver from abdominal CT scan images. In the second step, liver edge is corrected using improved chain code and Bresenham pixel interconnection methods. This provides a precise liver image. The initial points are located inside the liver region without user interventions. These initial points evolve outwardly using Fast Marching Method (FMM), identifying the liver boundary accurately in CT abdominal scan images.

RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans

Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, few studies investigate 3D networks for liver tumor segmentation. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, i.e., RA-UNet, to precisely extract the liver region and segment tumors from the liver. The proposed network has a basic architecture as U-Net which extracts contextual information combining low-level feature maps with high-level ones. Attention residual modules are integrated so that the attention-aware features change adaptively. This is the first work that an attention residual mechanism is used to segment tumors from 3D medical volumetric images. We evaluated our framework on the public MICCAI 2017 Liver Tumor Segmentation dataset and tested the generalization on the 3DIRCADb dataset. The experiments show that our architecture obtains competitive results.