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Segmentation of organs and lesions could be employed for the express purpose of dosimetry in nuclear medicine, assisted image interpretations, and mass image processing studies. Deep leaning created liver and liver lesion segmentation on clinical 3D MRI data has not been fully addressed in previous experiments. To this end, the required data were collected from 128 patients, including their T1w and T2w MRI images, and ground truth labels of the liver and liver lesions were generated. The collection of 110 T1w-T2w MRI image sets was divided, with 94 designated for training and 16 for validation. Furthermore, 18 more datasets were separately allocated for use as hold-out test datasets. The T1w and T2w MRI images were preprocessed into a two-channel format so that they were used as inputs to the deep learning model based on the Isensee 2017 network. To calculate the final Dice coefficient of the network performance on test datasets, the binary average of T1w and T2w predicted images was used. The deep learning model could segment all 18 test cases, with an average Dice coefficient of 88% for the liver and 53% for the liver tumor. Liver segmentation was carried out with rather a high accuracy; this could be achieved for liver dosimetry during systemic or selective radiation therapies as well as for attenuation correction in PET/MRI scanners. Nevertheless, the delineation of liver lesions was not optimal; therefore, tumor detection was not practical by the proposed method on clinical data., (© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)

The present study aimed to analyse the integrated histopathological lesions (IHLs) of the gill, liver and intestine of Catla catla exposed to the different doses of UV-B radiation. Gill exhibited the lesions like hypertrophy, hyperplasia, vacuolation, fusion of the gill filaments, rupture in the gill lamellae, epithelial cell lifting and necrosis. The UV-B-exposed liver of Catla showed the lesions like the degeneration of nucleus, the disarrangement of hepatocytes, sinusoidal vacuolation, epitheliod histiocyst, hepatocellular adenoma, exocrine adenoma, cyst formation and diffused epithelial necrosis (DEN). UV-B-exposed intestine showed the lesions like the distortion of columnar epithelial cells (CECs), distortion in lamina propria (LP), disruption in brush border (BB), vacuolation in LP, the presence of submucosal mass (SM), the degeneration of nucleus, the presence of tactoid bodies, the presence of aschoff nodules and metatypical cell carcinoma. These histopathological alterations can be considered as the main blocking alterations of the growth and absorption as well as the final production of fish which can cause a serious loss in total yield to fish farmers which can interrupt the profitable economical production of fish., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Liver morphology, intensity of apoptosis, and activity of xenobiotic metabolism enzymes were studied in a chronic model experiment in rats receiving a mixture of 6 pesticides against the background of life-long diets with adequate and insufficient supply of water-soluble vitamins. The dose of each pesticide in the mixture did not exceed the acceptable daily intake (1 ADI). It was found that chronic exposure to low doses of anthropogenic toxicants in combination with permanent vitamin deficiency provokes a number of liver changes, such as increased apoptosis activity, cytochrome P450 system depletion, steatosis, and inflammatory infiltration, which is a potential health risk factor., (© 2023. Springer Science+Business Media, LLC, part of Springer Nature.)

The use of quantitative MRI methods for assessment of the abdomen in children has become commonplace over the past decade. Increasingly employed methods include MR elastography, chemical shift encoded (CSE) MR imaging for determination of proton density fat fraction, diffusion-weighted imaging, and a variety of relaxometry techniques, such as T1 and T2* mapping. These techniques can be used in a variety of settings to distinguish normal from abnormal tissue as well as determine the severity of disease. The performance of quantitative MRI methods in the pediatric population presents unique challenges as compared to adult populations. These challenges relate to multiple factors, including patient size, pediatric physiology, inability to breath hold, and greater physical motion during the examination. The purpose of this review article is to review quantitative MRI methods that may be used in clinical practice to assess the pediatric abdomen and to discuss special considerations when performing these techniques in children., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Robotic liver surgery is experiencing a period of great development, but some hurdles still need to be overcome. Parenchymal transection remains one of the most technically challenging steps. The lack of dedicated instruments and the flourishing of several techniques didn't allow surgeons to reach a standard technique so far. The aim of the present paper is to provide an overview of the different robotic liver transection techniques described to date, highlighting the strengths and weaknesses of each one. We conducted an extensive search on PubMed, Scopus and Web of Science, inserting the following keywords: "robotic liver transection, robotic hepatic transection" and focusing particularly on technical reports and paper regarding new surgical methods. This search resulted in a total of 13 different surgical techniques. All the methods described can be classified into two categories the "hybrid techniques" and the "fully robotic techniques" which are based, respectively, on the combined use of laparoscopic and robotic instrumentation or exclusively robotic devices. Another fundamental difference is the division between "one-surgeon" and "two-surgeon techniques", which depends on the level of expertise required of the assistant surgeon at the operating table. This is the first comprehensive review on this topic. Although the existing literature does not allow one technique to be established as superior to the others, the adoption of a standardized method of robotic hepatic transection is highly desirable to optimize surgical results and to allow better comparability of outcomes within the scientific community., Competing Interests: Declarations. Conflict of interest: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.)

Purpose: Evidence is accumulating that lipocalin2 (LCN2) is implicated in insulin resistance and glucose homeostasis, but the underlying possible mechanisms remain unclear. This study is to investigate the possible linkage between LCN2 and AMP-activated protein kinase (AMPK) or forkhead transcription factor O1 (FoxO1), which influences insulin sensitivity and gluconeogenesis in liver., Methods: LCN2 knockout (LCN2KO) mice and wild-type littermates were used to evaluate the effect of LCN2 on insulin sensitivity and hepatic gluconeogenesis through pyruvate tolerance test (PTT), glucose tolerance test (ipGTT), insulin tolerance test (ITT), and hyperinsulinemic-euglycemic clamps, respectively. LCN2KO mice and WT mice in vivo, and in vitro HepG2 cells were co-transfected with adenoviral FoxO1-siRNA (Ad-FoxO1-siRNA) or adenovirus expressing constitutively active form of AMPK (Ad-CA-AMPK), or dominant negative adenovirus AMPK (Ad-DN-AMPK), the relative mRNA and protein levels of two key gluconeogenic enzymes phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (G6P) were measured., Results: Improved insulin sensitivity and inhibited gluconeogenesis in the LCN2KO mice were confirmed by pyruvate tolerance tests and hyperinsulinemic-euglycemic clamps. Nuclear FoxO1 and its downstream genes PEECK and G6P were decreased in the livers of the LCN2KO mice, and AMPK activity was stimulated and directly phosphorylated FoxO1. In vitro, AMPK activity was inhibited in HepG2 cells overexpressing LCN2 leading to a decrease in phosphorylated FoxO1 and an increase in nuclear FoxO1., Conclusion: The present study demonstrates that LCN2 regulates insulin sensitivity and glucose metabolism through inhibiting AMPK activity, and regulating FoxO1 and its downstream genes PEPCK/G6P, which regulate hepatic gluconeogenesis., (© 2021. Italian Society of Endocrinology (SIE).)

Background: Liver biopsy remains the gold standard for characterizing and evaluating treatment response in nonalcoholic fatty liver disease (NAFLD). Liver heterogeneity and sampling variability can affect the reliability of results. This study aimed to compare histological variability of intraoperative wedge and core liver biopsies from different lobes in bariatric patients, to better inform surgeons on biopsy method and guide interpretation of results., Methods: We prospectively recruited bariatric surgical patients. Intraoperative core biopsies were taken from the left and right lobe, with a wedge biopsy taken from the left. All biopsies were graded by a specialist liver pathologist, blinded to clinical details and biopsy site. Concordance of histological findings between sites was evaluated., Results: There were 91 participants (72.2% female), mean age 46.8 ± 12.0 years, body mass index 45.9 ± 9.4 kg/m 2 . There was no significant pattern for up- or down-grading disease dependent on biopsy technique. Moderate to strong agreement was seen in the presence of NAFLD and nonalcoholic steatohepatitis (NASH, κ = 0.609-0.865, p < 0.001) between biopsy sites. Individual components (steatosis, inflammation, ballooning) showed weaker agreement (κ = 0.386-0.656, p < 0.01). Fibrosis showed particularly poor agreement (κ = 0.223-0.496, p < 0.01). Detection of pathology improved with a combination of biopsy techniques, compared to a single biopsy method., Conclusion: Overall diagnosis of NAFLD or NASH shows good agreement between biopsy sites, but individual components, particularly fibrosis stage, vary significantly. Clinicians should consider biopsies from varied sites, to better assess liver disease severity. These data have important implications in fibrosis assessment of NAFLD and are relevant in the interpretation of histological efficacy of investigational pharmacotherapies., Trial Registration: ACTRN12615000875505 (Australian Clinical Trials Register).

Cardiogenic liver disease is a common yet poorly characterized complication of advanced heart failure (HF), and may impact clinical management in the setting of heart transplant evaluation. In this retrospective study, we describe clinical and histopathological features of liver injury in advanced HF, with a focus on the role of liver biopsy. Included were 45 HF patients, assessed for possible heart transplant, who underwent liver biopsy for suspected liver disease. Median duration of HF symptoms was 5 years. Most patients had stiff hepatomegaly and elevated bilirubin. Viral hepatitis (19 patients, 42.2%) was the most common cause of prior known liver disease. Sinusoidal dilatation was detected in the majority of patients (64.4%). Median necroinflammatory index was 3 and median fibrosis was 1, consistent with a small burden of histologically proven liver disease. Viral hepatitis was the only variable associated with a higher grade of necroinflammation and fibrosis. Nine of the 14 (64.3%) advanced fibrosis/cirrhosis patients had a viral hepatitis infection. Fibrosis was significantly associated with splenomegaly. The MELD score was not correlated with cardiac index. A coarse liver echo-pattern had a 29% positive and 63% negative predictive value for advanced fibrosis/cirrhosis. Severe liver disease is uncommon in patients with advanced HF in the absence of splenomegaly or primary causes of liver disease. Ultrasound data need to be carefully evaluated, as it may overstate the severity of liver disease. Liver biopsy may be needed to accurately stage liver disease before excluding patients from advanced treatment strategies.

As transcriptomic studies are becoming more and more common, it is important to ensure that the RNA used in the analyses is of good quality. The RNA integrity may be compromised by storage temperature or freeze-thaw cycles, but these have not been well studied in poikilothermic fishes. This work studied the effects of tissue storage time and temperature, and freeze-thaw cycles of tissue and extracted RNA on RNA integrity in brown trout (Salmo trutta L.) liver. The storage time and temperature had an effect on RNA integrity, but RNA suitable for quantitative reverse transcription PCR (RT-qPCR) (RIN > 7) was still obtained from samples preserved at - 20 °C for 6 months. Freeze-thaw cycles of tissue or RNA did not compromise the integrity of RNA. RNA degradation had an effect on RT-qPCR results, and the effect depended on gene. The RT-qPCR analysis of historical samples from a bleached kraft pulp mill effluent exposure in 1984 revealed no significant cyp1a induction. Recommendations are given for the preservation and handling procedures of samples designated for transcriptomic analyses.

Purpose: The data acquisition of drug metabolism analysis requires a lot of time and animal resources. However, there are often many deviations in the results of pharmacokinetic analysis. Conventional methods cannot measure the blood drug concentration data in multiple tissues at the same time, and the data is obtained by in vitro measurement, which produces time errors, in vitro data errors, and individual differences between animals. In the analysis of pharmacokinetic parameters, it will seriously affect the pass rate of clinical trials of R&D drugs and the accuracy of the dosing schedule. To the best of our knowledge, we have not found the study of in vivo blood drug concentration using multi-channel equipment. Therefore, the purpose of this paper is to build a set of multi-organ monitoring and analysis instruments for synchronously monitoring the metabolism of drugs in various tissues of small animals, so as to obtain real in vivo data of blood drug concentration in real time., Procedures: Using the fluorescence properties and laser-induced fluorescence principle of drugs, we designed six channels to monitor the changes of fluorescence-labeled drugs in their main metabolic organs, a multi-channel calibration method was proposed to improve the accuracy of the time-division multiplexing, the real-time collection of drug concentration in vivo is realized, and the drug metabolism curve in vivo can be observed., Results: The instrument satisfies the collection of small doses of drugs such as microgram; the detection sensitivity can reach 10 ng/ml, and can monitor and collect the drug metabolism of multiple small animal tissues at the same time, which greatly reduces the use of animals, reduces the differences between individuals, and reduces consumption cost and improve the detection efficiency of parameters, and obtain data information that is closer to the real biology., Conclusion: The real-time continuous monitoring and data collection of the drug metabolism in the plasma of living small animals and the important organs such as kidney, liver, and spleen were realized. The research and development of new drugs and clinical research have higher practical value., (© 2023. The Author(s), under exclusive licence to World Molecular Imaging Society.)

The liver is one of the most important organs, with a complex physiology. Current in-vitro approaches are not accurate for disease modeling and drug toxicity research. One of those features is liver zonation, where cells display different physiological states due to different levels of oxygen and nutrient supplements. Organ-on-a-chip technology employs microfluidic platforms that enable a controlled environment for in-vitro cell culture. In this study, we propose a microfluidic design embedding a gas channel (of ambient air), creating an oxygen gradient. We numerically simulate different flow rates and cell densities with the COMSOL Multiphysics package considering cell-specific consumption rates of oxygen and glucose. We establish the cell density and flow rate for optimum oxygen and glucose distribution in the cell culture chamber. Furthermore, we show that a physiologically relevant concentration of oxygen and glucose in the chip is reached after 24 h and 30 min, respectively. The proposed microfluidic design and optimal parameters we identify in this paper provide a tool for in-vitro liver zonation studies. However, the microfluidic design is not exclusively for liver cell experiments but is foreseen to be applicable in cell studies where different gas concentration gradients are critical, e.g., studying hypoxia or toxic gas impact., (© 2023. International Federation for Medical and Biological Engineering.)

This paper proposes a new network framework, which leverages EfficientNetB4, attention gate, and residual learning techniques to achieve automatic and accurate liver segmentation. First, we use EfficientNetB4 as the encoder to extract more feature information during the encoding stage. Then, an attention gate is introduced in the skip connection to eliminate irrelevant regions and highlight features of a specific segmentation task. Finally, to alleviate the problem of gradient vanishment, we replace the traditional convolution of the decoder with a residual block to improve the segmentation accuracy. We verified the proposed method on the LiTS17 and SLiver07 datasets and compared it with classical networks such as FCN, U-Net, attention U-Net, and attention Res-U-Net. In the Sliver07 evaluation, the proposed method achieved the best segmentation performance on all five standard metrics. Meanwhile, in the LiTS17 assessment, the best performance is obtained except for a slight inferior on RVD. The proposed method's qualitative and quantitative results demonstrated its applicability in liver segmentation and proved its good prospect in computer-assisted liver segmentation., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Purpose: Detection of early-stage liver fibrosis has direct clinical implications on patient management and treatment. The aim of this paper is to develop a non-invasive, cost-effective method for classifying liver disease between "non-fibrosis" (F0) and "fibrosis" (F1-F4), and to evaluate the classification performance quantitatively., Methods: Image data from 75 patients who underwent a simultaneous liver biopsy and non-contrast CT examination were used for this study. Non-contrast CT image texture features such as wavelet-based features, standard deviation of variance filter, and mean CT number were calculated in volumes of interest (VOIs) positioned within the liver parenchyma. In addition, a combined feature was calculated using logistic regression with L2-norm regularization to further improve fibrosis detection. Based on the final pathology from the liver biopsy, the patients were labelled either as "non-fibrosis" or "fibrosis". Receiver-operating characteristic (ROC) curve, area under the ROC curve (AUROC), specificity, sensitivity, and accuracy were determined for the algorithm to differentiate between "non-fibrosis" and "fibrosis"., Results: The combined feature showed the highest classification performance with an AUROC of 0.86, compared to the wavelet-based feature (AUROC, 0.76), the standard deviation of variance filter (AUROC, 0.65), and mean CT number (AUROC, 0.84). The combined feature's specificity, sensitivity, and accuracy were 0.66, 0.88, and 0.76, respectively, showing the most promising results., Conclusion: A new non-invasive and cost-effective method was developed to classify liver diseases between "non-fibrosis" (F0) and "fibrosis" (F1-F4). The proposed method makes it possible to detect liver fibrosis in asymptomatic patients using non-contrast CT images for better patient management and treatment., (© 2022. CARS.)

This paper reports on the seasonal and species comparison of hepatic nodular alterations in two indicator fish species from the hyper-eutrophic Roodeplaat Dam in South Africa. This freshwater system is characterized by seasonal cyanobacterial algal blooms which release bio-toxins, including hepato-toxins, which can have negative effects on the health of the resident fish population. A total of 115 Clarias gariepinus (Burchell) and 98 Oreochromis mossambicus (Peters) were collected seasonally across six different surveys over a period of 3 years. Nodular alterations in livers were assessed both macroscopically and microscopically. We found a species difference with a 48% prevalence of nodular alterations in C. gariepinus and no macroscopically visible alterations in O. mossambicus. Affected fish also showed an increase in the ratio of liver mass to body mass, i.e., the hepatosomatic index. The microscopic characteristics of the nodules were primarily associated with pre-neoplastic, focal areas of cellular alterations; most prevalent were focal areas of steatosis. However, we could not establish a seasonal pattern regarding the occurrence of these alterations and therefore no association between the occurrence of the liver pathology and the cyanobacterial blooms. Our results therefore suggest that the occurrence of nodular alterations is not an acute, seasonal response, but rather a chronic, and possibly, and more interestingly, a species-specific, pathological response., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: In this paper, a method for rapidly constructing a virtual surgical simulation system is proposed. A deformation model based on the mechanical properties of the liver and a rapid collision detection between the surgical micro-instruments and the liver tissue are included in this method. The purpose of this work is to improve the accuracy and real time of particle model deformation interaction in virtual surgery system., Methods: Firstly, a finite element model is established based on the constitutive model parameters of liver tissue. According to the simulation results, a mathematical model of node displacement is established. Secondly, the virtual liver is established based on the fast model reconstruction method, and the virtual manipulator is controlled by Geomagic Touch manipulator. Based on the hybrid bounding box, a rapid collision detection process between the instrument and liver is realized and the proposed deformation method is used to simulate the deformation of liver tissue., Results: The simulation and experiment results show that the proposed deformation model can achieve high deformation interaction accuracy. The collision detection algorithm based on the hybrid bounding boxes can realize the collision between the liver and the instrument, and the established virtual surgical simulation system can simulate the liver tissue deformation in the case of small loading displacement., Conclusions: The effectiveness of the collision detection algorithm and deformation model was verified by an established virtual surgery simulation system. The proposed rapid construction method of virtual surgical simulation is feasible.

Purpose: We investigated the parameter configuration in the automatic liver and tumor segmentation using a convolutional neural network based on 2.5D model. The implementation of 2.5D model shows promising results since it allows the network to have a deeper and wider network architecture while still accommodates the 3D information. However, there has been no detailed investigation of the parameter configurations on this type of network model., Methods: Some parameters, such as the number of stacked layers, image contrast, and the number of network layers, were studied and implemented on neural networks based on 2.5D model. Networks are trained and tested by utilizing the dataset from liver and tumor segmentation challenge (LiTS). The network performance was further evaluated by comparing the network segmentation with manual segmentation from nine technical physicians and an experienced radiologist., Results: Slice arrangement testing shows that multiple stacked layers have better performance than a single-layer network. However, the dice scores start decreasing when the number of stacked layers is more than three layers. Adding higher number of layers would cause overfitting on the training set. In contrast enhancement test, implementing contrast enhancement method did not show a statistically significant different to the network performance. While in the network layer test, adding more layers to the network architecture does not always correspond to the increasing dice score result of the network., Conclusions: This paper compares the performance of the network based on 2.5D model using different parameter configurations. The result obtained shows the effect of each parameter and allow the selection of the best configuration in order to improve the network performance in the application of automatic liver and tumor segmentation.

The liver is one of the body's tissues that has regenerative abilities. But if the damage is too much, it needs to medical interventions for the regeneration. Liver donor shortage causes researchers to turn to other treatments. Tissue engineering is a new approach to liver regeneration. Hydrogels are polymeric networks of hydrophilic, flexible, and similar to natural tissue. Therefore, they are used to encapsulate cells These constructs are potent substrates to induce differentiation of stem cells to the hepatocytes. According to inadequate availability of the hepatocytes, an alternative cell is required to produce hepatocyte-like cells. Due to the self-renewal and differentiation properties of stem cells, they are suitable cell sources to replace the lostcells. This review has focused on liver regeneration, advantages and disadvantages of hydrogels for liver regeneration, injectable materials, hydrogel fabrication methods, including 3D printing, and stem cells for liver regeneration. Furthermore, this paper shows in vitro, preclinical, and clinical trial studies of hydrogel and stem cells for liver regeneration. Graphical abstract.

Purpose The identification of abnormalities that are relatively rare within otherwise normal anatomy is a major challenge for deep learning in the semantic segmentation of medical images. The small number of samples of the minority classes in the training data makes the learning of optimal classification challenging, while the more frequently occurring samples of the majority class hamper the generalization of the classification boundary between infrequently occurring target objects and classes. In this paper, we developed a novel generative multi-adversarial network, called Ensemble-GAN, for mitigating this class imbalance problem in the semantic segmentation of abdominal images.Method The Ensemble-GAN framework is composed of a single-generator and a multi-discriminator variant for handling the class imbalance problem to provide a better generalization than existing approaches. The ensemble model aggregates the estimates of multiple models by training from different initializations and losses from various subsets of the training data. The single generator network analyzes the input image as a condition to predict a corresponding semantic segmentation image by use of feedback from the ensemble of discriminator networks. To evaluate the framework, we trained our framework on two public datasets, with different imbalance ratios and imaging modalities: the Chaos 2019 and the LiTS 2017.Result In terms of the F1 score, the accuracies of the semantic segmentation of healthy spleen, liver, and left and right kidneys were 0.93, 0.96, 0.90 and 0.94, respectively. The overall F1 scores for simultaneous segmentation of the lesions and liver were 0.83 and 0.94, respectively.Conclusion The proposed Ensemble-GAN framework demonstrated outstanding performance in the semantic segmentation of medical images in comparison with other approaches on popular abdominal imaging benchmarks. The Ensemble-GAN has the potential to segment abdominal images more accurately than human experts.

Soft tissue mechanical characterisation is important in many areas of medical research. Examples span from surgery training, device design and testing, sudden injury and disease diagnosis. The liver is of particular interest, as it is the most commonly injured organ in frontal and side motor vehicle crashes, and also assessed for inflammation and fibrosis in chronic liver diseases. Hence, an extensive rheological characterisation of liver tissue would contribute to advancements in these areas, which are dependent upon underlying biomechanical models. The aim of this paper is to define a liver constitutive equation that is able to characterise the nonlinear viscoelastic behaviour of liver tissue under a range of deformations and frequencies. The tissue response to large amplitude oscillatory shear (1-50%) under varying preloads (1-20%) and frequencies (0.5-2 Hz) is modelled using viscoelastic-adapted forms of the Mooney-Rivlin, Ogden and exponential models. These models are fit to the data using classical or modified objective norms. The results show that all three models are suitable for capturing the initial nonlinear regime, with the latter two being capable of capturing, simultaneously, the whole deformation range tested. The work presented here provides a comprehensive analysis across several material models and norms, leading to an identifiable constitutive equation that describes the nonlinear viscoelastic behaviour of the liver.

Purpose: Soft tissue deformation severely impacts the registration of pre- and intra-operative image data during computer-assisted navigation in laparoscopic liver surgery. However, quantifying the impact of target surface size, surface orientation, and mesh quality on non-rigid registration performance remains an open research question. This paper aims to uncover how these affect volume-to-surface registration performance., Methods: To find such evidence, we design three experiments that are evaluated using a three-step pipeline: (1) volume-to-surface registration using the physics-based shape matching method or PBSM, (2) voxelization of the deformed surface to a [Formula: see text] voxel grid, and (3) computation of similarity (e.g., mutual information), distance (i.e., Hausdorff distance), and classical metrics (i.e., mean squared error or MSE)., Results: Using the Hausdorff distance, we report a statistical significance for the different partial surfaces. We found that removing non-manifold geometry and noise improved registration performance, and a target surface size of only 16.5% was necessary., Conclusion: By investigating three different factors and improving registration results, we defined a generalizable evaluation pipeline and automatic post-processing strategies that were deemed helpful. All source code, reference data, models, and evaluation results are openly available for download: https://github.com/ghattab/EvalPBSM/ .

Metal pollution commonly occurs in many terrestrial environments and may pose a threat for the animals inhabiting such areas. Here, we present concentrations of six metals (cadmium [Cd], copper [Cu], nickel [Ni], lead [Pb], selenium [Se], and zinc [Zn]) in the liver tissues of seven species of mammals obtained from a study that examined the impact of mesopredator removal on northern bobwhite (Colinus virginianus) populations. A total of 1326 samples were collected from 2003 to 2006 at four sites in southwest Georgia and north Florida from nine-banded armadillos (Dasypus novemcinctus), bobcats (Lynx rufus), feral cats (Felis catus), coyotes (Canis latrans), grey foxes (Urocyon cinereoargenteus), opossums (Didelphis virginiana), and raccoons (Procyon lotor). Data from armadillos, bobcats, opossums, and raccoons were published previously to examine age, sex, spatial (between sites), and temporal (between years) variation. In this paper, we present similar comparisons for the remaining three species as well as comparisons of metal concentrations among all seven species. Concentrations of Cu and Pb exhibited strong negative relationships with body weight in coyotes, while Ni was positively correlated with weight in feral cats. Concentrations of these metals, as well as the other two tested (Cd and Zn), were not significantly correlated with one another in any of the three species. The only sex difference in liver metal concentrations was observed in female feral cats, which had higher levels of Pb than did males. Coyotes exhibited significant differences in Cu concentrations between sites and between years (2005 versus 2006). We also found significant differences between sites in Pb concentrations for both feral cats and grey foxes. There were significant differences in metal concentrations among all seven species for all metals except Cd. With the exception of Cd and Se (tested only in bobcats and opossums), a three-way ANOVA with species, year, and site as the three factors revealed significant differences among species for every metal but only a single main effect of year for Cu, and no main effects of site. In sum, our results provide an extensive survey of metal concentrations in a diverse assemblage of mammals and suggest that metal accumulation may be heavily influenced by species identity, which in turn may reflect ecological lifestyle.

Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.

Purpose: The pediatric computed tomography (CT) volume is acquired at a low dose because radiation is harmful to young children. Consequently, the pediatric CT volume has lower signal-to-noise ratio, which makes organ segmentation difficult. In this paper, we propose a liver segmentation algorithm for pediatric CT scan using a patient-specific level set distribution model (LSDM)., Methods: The patient-specific LSDM was constructed using a conditional LSDM (C-LSDM) conditioned on age. Furthermore, a patient-specific probabilistic atlas (PA) was generated using the model, which became a priori to the maximum a posteriori-based segmentation. The patient-specific PA generation by the C-LSDM using kernel density estimation was quicker than the conventional PA generation method using random numbers, and also, it was more accurate as it did not include any approximations., Results: The liver segmentation algorithm was tested on 42 CT volumes of children aged between 2 weeks and 7 years. In the proposed method, the calculation time of the PA was about 9 s for the single Gaussian method, while it was 337 s for the conventional PA generation method using random numbers. Furthermore, using the kernel density estimation, median and 25%/75% tile of the generalized Dice similarity index between the PA and the correct liver region were found to be 0.3443 and 0.3191/0.3595. The Dice similarity index in the segmentation was 0.8821 and 0.8545/0.9085, which are higher than those obtained by the conventional method, and requires lower computational cost., Conclusion: We proposed a method to quickly and accurately generate a PA, combined with C-LSDM using kernel density estimation, which enabled efficient calculation and improved segmentation accuracy.

Purpose: MRI slice reordering is a necessary step when three-dimensional (3D) motion of an anatomical region of interest has to be extracted from multiple two-dimensional (2D) dynamic acquisition planes, e.g., for the construction of motion models used for image-guided radiotherapy. Existing reordering methods focus on obtaining a spatially coherent reconstructed volume for each time. However, little attention has been paid to the temporal coherence of the reconstructed volumes, which is of primary importance for accurate 3D motion extraction. This paper proposes a fully automatic self-sorting four-dimensional MR volume construction method that ensures the temporal coherence of the results., Methods: First, a pseudo-navigator signal is extracted for each 2D dynamic slice acquisition series. Then, a weighted graph is created using both spatial and motion information provided by the pseudo-navigator. The volume at a given time point is reconstructed following the shortest paths in the graph starting that time point of a reference slice chosen based on its pseudo-navigator signal., Results: The proposed method is evaluated against two state-of-the-art slice reordering algorithms on a prospective dataset of 12 volunteers using both spatial and temporal quality metrics. The automated end-exhale extraction showed results closed to the median value of the manual operators. Furthermore, the results of the validation metrics show that the proposed method outperforms state-of-the-art methods in terms of both spatial and temporal quality., Conclusion: Our approach is able to automatically detect the end-exhale phases within one given anatomical position and cope with irregular breathing.

The paper presents the macroelement (Al, Ca, Cu, Fe, K, Mg, Mn, Na, P, and Zn) and microelement (As, Cd, Co, Cr, Hg, Mo, Ni, Pb, and Sn) contents found in the liver of wild animals (boar and deer) and farm animals (rabbit, chicken, duck, cow, goat, and turkey). Statistically, the differences in element contents between the two groups were not significant (at p = 0.05), with the exception of Fe, K, Mg, Cd, Hg, Mo, and Pb. The liver of farm animals contained more Al, Cu, K, Mg, Na, Cr, and Sn, while the content of the remaining elements was higher in wild animals. An analysis of correlations between element content and age in wild animals (boar) showed that Pb and Al content increases with age, while Na and Cr contents decrease significantly. Comparisons between the test results and the maximum limits allowed by law showed that, in the case of wild animals, the regulatory limits were exceeded in 18% (for Cd and Cu) and 9% (for Hg) of the liver samples analyzed. In the case of farm animals, the limits for micro- and macroelement contents were not exceeded. The hazard index (HI) values for farm animals were lower than for wild animals, with regard to consumption by both children and adults. Based on the HI values calculated, it seems recommendable that consumption of the liver (preferably from farm animals) by children be limited to once weekly. For adults, the liver can be a valuable source of elements such as Zn, Fe, and Cr, which may be an indication for more frequent consumption.

Purpose: The nonalcoholic fatty liver disease is the most common liver abnormality. Up to date, liver biopsy is the reference standard for direct liver steatosis quantification in hepatic tissue samples. In this paper we propose a neural network-based approach for nonalcoholic fatty liver disease assessment in ultrasound., Methods: We used the Inception-ResNet-v2 deep convolutional neural network pre-trained on the ImageNet dataset to extract high-level features in liver B-mode ultrasound image sequences. The steatosis level of each liver was graded by wedge biopsy. The proposed approach was compared with the hepatorenal index technique and the gray-level co-occurrence matrix algorithm. After the feature extraction, we applied the support vector machine algorithm to classify images containing fatty liver. Based on liver biopsy, the fatty liver was defined to have more than 5% of hepatocytes with steatosis. Next, we used the features and the Lasso regression method to assess the steatosis level., Results: The area under the receiver operating characteristics curve obtained using the proposed approach was equal to 0.977, being higher than the one obtained with the hepatorenal index method, 0.959, and much higher than in the case of the gray-level co-occurrence matrix algorithm, 0.893. For regression the Spearman correlation coefficients between the steatosis level and the proposed approach, the hepatorenal index and the gray-level co-occurrence matrix algorithm were equal to 0.78, 0.80 and 0.39, respectively., Conclusions: The proposed approach may help the sonographers automatically diagnose the amount of fat in the liver. The presented approach is efficient and in comparison with other methods does not require the sonographers to select the region of interest.

Purpose: Fast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being invasive and time consuming. Due to the short time availability between liver procurement and transplantation, surgeons perform HS assessment through clinical evaluation (medical history, blood tests) and liver texture visual analysis. Despite visual analysis being recognized as challenging in the clinical literature, few efforts have been invested to develop computer-assisted solutions for HS assessment. The objective of this paper is to investigate the automatic analysis of liver texture with machine learning algorithms to automate the HS assessment process and offer support for the surgeon decision process., Methods: Forty RGB images of forty different donors were analyzed. The images were captured with an RGB smartphone camera in the operating room (OR). Twenty images refer to livers that were accepted and 20 to discarded livers. Fifteen randomly selected liver patches were extracted from each image. Patch size was [Formula: see text]. This way, a balanced dataset of 600 patches was obtained. Intensity-based features (INT), histogram of local binary pattern ([Formula: see text]), and gray-level co-occurrence matrix ([Formula: see text]) were investigated. Blood-sample features (Blo) were included in the analysis, too. Supervised and semisupervised learning approaches were investigated for feature classification. The leave-one-patient-out cross-validation was performed to estimate the classification performance., Results: With the best-performing feature set ([Formula: see text]) and semisupervised learning, the achieved classification sensitivity, specificity, and accuracy were 95, 81, and 88%, respectively., Conclusions: This research represents the first attempt to use machine learning and automatic texture analysis of RGB images from ubiquitous smartphone cameras for the task of graft HS assessment. The results suggest that is a promising strategy to develop a fully automatic solution to assist surgeons in HS assessment inside the OR.

Purpose: Thermotherapy is a clinical procedure which delivers thermal energy to a target, and it has been applied for various medical treatments. Temperature monitoring during thermotherapy is important to achieve precise and reproducible results. Medical ultrasound can be used for thermal monitoring and is an attractive medical imaging modality due to its advantages including non-ionizing radiation, cost-effectiveness and portability. We propose an ultrasound thermal monitoring method using a speed-of-sound tomographic approach coupled with a biophysical heat diffusion model., Methods: We implement an ultrasound thermometry approach using an external ultrasound source. We reconstruct the speed-of-sound images using time-of-flight information from the external ultrasound source and convert the speed-of-sound information into temperature by using the a priori knowledge brought by a biophysical heat diffusion model., Results: Customized treatment shapes can be created using switching channels of radio frequency bipolar needle electrodes. Simulations of various ablation lesion shapes in the temperature range of 21-59 [Formula: see text]C are performed to study the feasibility of the proposed method. We also evaluated our method with ex vivo porcine liver experiments, in which we generated temperature images between 22 and 45 [Formula: see text]C., Conclusion: In this paper, we present a proof of concept showing the feasibility of our ultrasound thermal monitoring method. The proposed method could be applied to various thermotherapy procedures by only adding an ultrasound source.

Purpose: The bag of visual words (BoVW) model is a powerful tool for feature representation that can integrate various handcrafted features like intensity, texture, and spatial information. In this paper, we propose a novel BoVW-based method that incorporates texture and spatial information for the content-based image retrieval to assist radiologists in clinical diagnosis., Methods: This paper presents a texture-specific BoVW method to represent focal liver lesions (FLLs). Pixels in the region of interest (ROI) are classified into nine texture categories using the rotation-invariant uniform local binary pattern method. The BoVW-based features are calculated for each texture category. In addition, a spatial cone matching (SCM)-based representation strategy is proposed to describe the spatial information of the visual words in the ROI. In a pilot study, eight radiologists with different clinical experience performed diagnoses for 20 cases with and without the top six retrieved results. A total of 132 multiphase computed tomography volumes including five pathological types were collected., Results: The texture-specific BoVW was compared to other BoVW-based methods using the constructed dataset of FLLs. The results show that our proposed model outperforms the other three BoVW methods in discriminating different lesions. The SCM method, which adds spatial information to the orderless BoVW model, impacted the retrieval performance. In the pilot trial, the average diagnosis accuracy of the radiologists was improved from 66 to 80% using the retrieval system., Conclusion: The preliminary results indicate that the texture-specific features and the SCM-based BoVW features can effectively characterize various liver lesions. The retrieval system has the potential to improve the diagnostic accuracy and the confidence of the radiologists.

In this paper, region-difference filters for the segmentation of liver ultrasound (US) images are proposed. Region-difference filters evaluate maximum difference of the average of two regions of the window around the center pixel. Implementing the filters on the whole image gives region-difference image. This image is then converted into binary image and morphologically operated for segmenting the desired lesion from the ultrasound image. The proposed method is compared with the maximum a posteriori-Markov random field (MAP-MRF), Chan-Vese active contour method (CV-ACM), and active contour region-scalable fitting energy (RSFE) methods. MATLAB code available online for the RSFE method is used for comparison whereas MAP-MRF and CV-ACM methods are coded in MATLAB by authors. Since no comparison is available on common database for the performance of the three methods, therefore, performance comparison of the three methods and proposed method was done on liver US images obtained from PGIMER, Chandigarh, India and from online resource. A radiologist blindly analyzed segmentation results of the 4 methods implemented on 56 images and had selected the segmentation result obtained from the proposed method as best for 46 test US images. For the remaining 10 US images, the proposed method performance was very near to the other three segmentation methods. The proposed segmentation method obtained the overall accuracy of 99.32% in comparison to the overall accuracy of 85.9, 98.71, and 68.21% obtained by MAP-MRF, CV-ACM, and RSFE methods, respectively. Computational time taken by the proposed method is 5.05 s compared to the time of 26.44, 24.82, and 28.36 s taken by MAP-MRF, CV-ACM, and RSFE methods, respectively.

Purpose: Automated liver segmentation from a postmortem computed tomography (PMCT) volume is a challenging problem owing to the large deformation and intensity changes caused by severe pathology and/or postmortem changes. This paper addresses this problem by a novel segmentation algorithm using a statistical shape model (SSM) for a postmortem liver., Methods: The location and shape parameters of a liver are directly estimated from a given volume by the proposed SSM-guided expectation-maximization (EM) algorithm without any spatial standardization that might fail owing to the large deformation and intensity changes. The estimated location and shape parameters are then used as a constraint of the subsequent fine segmentation process based on graph cuts. Algorithms with eight different SSMs were trained using 144 in vivo and 32 postmortem livers, and the segmentation algorithm was tested on 32 postmortem livers in a twofold cross validation manner. The segmentation performance is measured by the Jaccard index (JI) between the segmentation result and the true liver label., Results: The average JI of the segmentation result with the best SSM was 0.8501, which was better compared with the results obtained using conventional SSMs and the results of the previous postmortem liver segmentation with statistically significant difference., Conclusions: We proposed an algorithm for automated liver segmentation from a PMCT volume, in which an SSM-guided EM algorithm estimated the location and shape parameters of a liver in a given volume accurately. We demonstrated the effectiveness of the proposed algorithm using actual postmortem CT volumes.

Rheological soft tissue models play an important role in designing control methods for modern teleoperation systems. In the meanwhile, these models are also essential for creating a realistic virtual environment for surgical training. The implementation of model-based control in teleoperation has been a frequently discussed topic in the past decades, offering solutions for the loss of stability caused by time delay, which is one of the major issues in long-distance force control. In this paper, mass-spring-damper soft tissue models are investigated, showing that the widely used linear models do not represent realistic behavior under surgical manipulations. A novel, nonlinear model is proposed, where mechanical parameters are estimated using curve fitting methods. Theoretical reaction force curves are estimated using the proposed model, and the results are verified using measurement results from uniaxial indentation. The model is extended with force estimation by nonuniform surface deformation, where the surface deformation function is approximated according to visual data. Results show that using the proposed nonlinear model, a good estimation of reaction force can be achieved within the range of 0-4 mm, provided that the tissue deformation shape function is appropriately approximated.

Purpose: Laparoscopic liver resection has significant advantages over open surgery due to less patient trauma and faster recovery times, yet is difficult for most lesions due to the restricted field of view and lack of haptic feedback. Image guidance provides a potential solution but is challenging in a soft deforming organ such as the liver. In this paper, we therefore propose a laparoscopic ultrasound (LUS) image guidance system and study the feasibility of a locally rigid registration for laparoscopic liver surgery., Methods: We developed a real-time segmentation method to extract vessel centre points from calibrated, freehand, electromagnetically tracked, 2D LUS images. Using landmark-based initial registration and an optional iterative closest point (ICP) point-to-line registration, a vessel centre-line model extracted from preoperative computed tomography (CT) is registered to the ultrasound data during surgery., Results: Using the locally rigid ICP method, the RMS residual error when registering to a phantom was 0.7 mm, and the mean target registration error (TRE) for two in vivo porcine studies was 3.58 and 2.99 mm, respectively. Using the locally rigid landmark-based registration method gave a mean TRE of 4.23 mm using vessel centre lines derived from CT scans taken with pneumoperitoneum and 6.57 mm without pneumoperitoneum., Conclusion: In this paper we propose a practical image-guided surgery system based on locally rigid registration of a CT-derived model to vascular structures located with LUS. In a physical phantom and during porcine laparoscopic liver resection, we demonstrate accuracy of target location commensurate with surgical requirements. We conclude that locally rigid registration could be sufficient for practically useful image guidance in the near future.

Purpose: Malignant neoplasms of the liver are among the most frequent cancers worldwide. Given the diversity of options for liver cancer therapy, the choice of treatment depends on various parameters including patient condition, tumor size and location, liver function, and previous interventions. To address this issue, we present the first approach to treatment strategy planning based on holistic processing of patient-individual data, practical knowledge (i.e., case knowledge), and factual knowledge (e.g., clinical guidelines and studies)., Methods: The contributions of this paper are as follows: (1) a formalized dynamic patient model that incorporates all the heterogeneous data acquired for a specific patient in the whole course of disease treatment; (2) a concept for formalizing factual knowledge; and (3) a technical infrastructure that enables storing, accessing, and processing of heterogeneous data to support clinical decision making., Results: Our patient model, which currently covers 602 patient-individual parameters, was successfully instantiated for 184 patients. It was sufficiently comprehensive to serve as the basis for the formalization of a total of 72 rules extracted from studies on patients with colorectal liver metastases or hepatocellular carcinoma. For a subset of 70 patients with these diagnoses, the system derived an average of [Formula: see text] assertions per patient., Conclusion: The proposed concept paves the way for holistic treatment strategy planning by enabling joint storing and processing of heterogeneous data from various information sources.

Three dimensional microscopy images present significant potential to enhance biomedical studies. This paper presents an automated method for quantitative analysis of 3D primary vessel structures with histology whole slide images. With registered microscopy images of liver tissue, we identify primary vessels with an improved variational level set framework at each 2D slide. We propose a Vessel Directed Fitting Energy (VDFE) to provide prior information on vessel wall probability in an energy minimization paradigm. We find the optimal vessel cross-section associations along the image sequence with a two-stage procedure. Vessel mappings are first found between each pair of adjacent slides with a similarity function for four association cases. These bi-slide vessel components are further linked by Bayesian Maximum A Posteriori (MAP) estimation where the posterior probability is modeled as a Markov chain. The efficacy of the proposed method is demonstrated with 54 whole slide microscopy images of sequential sections from a human liver.

The Fontan operation allows for longer survival of those born with functionally single ventricle physiology. Although it effectively eliminates cyanosis, increased systemic venous pressure is an unavoidable consequence and low cardiac output is frequent. The abdomen is particularly vulnerable to these alterations in hemodynamics because the hepatic blood flow consists predominantly of portal venous flow, which relies on a low pressure gradient between the portal and hepatic veins. Therefore, any subtle increase in systemic venous pressure will adversely affect the hemodynamic balance of the liver as well as the intestine. As the clinical manifestations and routine laboratory findings of abdominal complications can lag behind the hemodynamic and pathologic changes in the abdominal organs, regular imaging surveillance is critical. Magnetic resonance (MR) provides excellent visualization of both cardiovascular and abdominal systems. It provides robust anatomic and hemodynamic data which can be used for timely implementation of treatment options. In this review paper, we discuss the pathogenesis and MR findings of abdominal complications following the Fontan operation. Specifically we explore the utility of phase-contrast MR for assessment of the abdominal circulation in children following the Fontan palliation.

Purpose: The aim of this paper is to present a simple and quantitative data analysis method with a new potential in the application of liver single-photon emission computed tomography (SPECT) imaging. We have established quantitative SPECT/computed tomography (CT) in vivo imaging protocols for determination of liver tumor burden based on the known role of Kupffer cells in cancer of the liver., Procedures: As it is also known that functional Kupffer cells accumulate particulate material contained in the arterial blood of liver supply, we used radiolabeled macro-aggregated albumin particles ([(99m)Tc]-MAA) injected intravenously to image liver disease. Quantification of cold spot liver lesion imaging was also a general objective., Methods: We examined a healthy control group (BALB/C mice, n = 6) and group of induced hepatocellular carcinoma (HCC, matrilin-2 transgenic KO mice, n = 9), where hepatocellular carcinoma was induced by diethylnitrosamine. We used [(99m)Tc]-MAA as radiopharmaceutical for liver SPECT imaging in a small animal SPECT/CT system. A liver radioactivity overview map was generated. Segmentation of the liver was calculated by Otsu thresholding method. Based on the segmentation the radioactivity volume and the summarized liver activity were determined., Results: Tumor burden of the livers was quantitatively determined by creating parametric data from the resulting volumetric maps. Ex vivo liver mass data were applied for the validation of in vivo measurements. An uptake with cold spots as tumors was observed in all diseased animals in SPECT/CT scans. Isotope-labeled particle uptake (standardized uptake concentration) of control (median 0.33) and HCC (median 0.18) groups was significantly different (p = 0.0015, Mann Whitney U test)., Conclusion: A new potential application of [(99m)Tc]-MAA was developed and presents a simple and very effective means to quantitatively characterize liver cold spot lesions resulting from Kupffer cell dysfunctions as a consequence of tumor burden.

An environment composed of different types of living tissues (such as the abdominal cavity) reveals a high complexity of boundary conditions, which are the attachments (e.g. connective tissues, ligaments) connecting different anatomical structures. Together with the material properties, the boundary conditions have a significant influence on the mechanical response of the organs, however corresponding correct mechanical modeling remains a challenging task, as the connective structures are difficult to identify in certain standard imaging modalities. In this paper, we present a method for automatic modeling of boundary conditions in deformable anatomical structures, which is an important step in patient-specific biomechanical simulations. The method is based on a statistical atlas which gathers data defining the connective structures attached to the organ of interest. In order to transfer the information stored in the atlas to a specific patient, the atlas is registered to the patient data using a physics-based technique and the resulting boundary conditions are defined according to the mean position and variance available in the atlas. The method is evaluated using abdominal scans of ten patients. The results show that the atlas provides a sufficient information about the boundary conditions which can be reliably transferred to a specific patient. The boundary conditions obtained by the atlas-based transfer show a good match both with actual segmented boundary conditions and in terms of mechanical response of deformable organs.

A system to characterize normal liver, cirrhotic liver and hepatocellular carcinoma (HCC) evolved on cirrhotic liver is proposed in this paper. The study is performed with 56 real ultrasound images (15 normal, 16 cirrhotic and 25 HCC liver images) taken from 56 subjects. A total of 180 nonoverlapping regions of interest (ROIs), i.e. 60 from each image class, are extracted by an experienced participating radiologist. The multiresolution wavelet packet texture descriptors, i.e. mean, standard deviation and energy features, are computed from all 180 ROIs by using various compact support wavelet filters including Haar, Daubechies (db4 and db6), biorthogonal (bior3.1,bior3.3 and bior4.4), symlets (sym3 and sym5) and coiflets (coif1 and coif2). It is observed that a combined texture descriptor feature vector of length 48 consisting of 16 mean, 16 standard deviation and 16 energy features estimated from all 16 subband feature images (wavelet packets) obtained by second-level decomposition with two-dimensional wavelet packet transform by using Haar wavelet filter gives the best characterization performance of 86.6 %. Feature selection by genetic algorithm-support vector machine method increased the classification accuracy to 88.8 % with sensitivity of 90 % for detecting normal and cirrhotic cases and sensitivity of 86.6 % for HCC cases. Considering limited sensitivity of B-mode ultrasound for detecting HCCs evolved on cirrhotic liver, the sensitivity of 86.6 % for HCC lesions obtained by the proposed computer-aided diagnostic system is quite promising and suggests that the proposed system can be used in a clinical environment to support radiologists in lesion interpretation.

The diagnosis and treatment of fatty liver disease requires accurate quantification of the amount of fat in the liver. Image-based methods for quantification of liver fat are of increasing interest due to the high sampling error and invasiveness associated with liver biopsy, which despite these difficulties remains the gold standard. Current computed tomography (CT) methods for liver-fat quantification are only semi-quantitative and infer the concentration of liver fat heuristically. Furthermore, these techniques are only applicable to images acquired without the use of contrast agent, even though contrast-enhanced CT imaging is more prevalent in clinical practice. In this paper, we introduce a method that allows for direct quantification of liver fat for both contrast-free and contrast- enhanced CT images. Phantom and patient data are used for validation, and we conclude that our algorithm allows for highly accurate and repeatable quantification of liver fat for spectral CT.

The delineation of tumor boundaries in medical images is an essential task for the early detection, diagnosis and follow-up of cancer. However accurate segmentation remains challenging due to presence of noise, inhomogeneity and high appearance variability of malignant tissue. In this paper, we propose an automatic segmentation approach using fully-connected higher-order conditional random fields (HOCRF) where potentials are computed within a discriminant Grassmannian manifold. First, the framework learns within-class and between-class similarity distributions from a training set of images to discover the optimal manifold discrimination between normal and pathological tissues. Second, the conditional optimization scheme computes non-local pairwise as well as pattern-based higher-order potentials from the manifold subspace to recognize regions with similar labelings and incorporate global consistency in the inference process. Our HOCRF framework is applied in the context of metastatic liver tumor segmentation in CT images. Compared to state of the art methods, our method achieves better performance on a group of 30 liver tumors and can deal with highly pathological cases.

Playing an important role in a broad range of biological and pathological processes, sialylation has been drawing wide interest. The efficient sialoglycopeptides enrichment methods are therefore attracting considerable attention. In this paper, we first compared two conventional enrichment methods, lectin and TiO(2), and analyzed their characteristics. Furthermore, considering the highly negatively charged nature of sialic acids, we developed a new strategy, peptide immobilized pH gradient isoelectric focusing (IPG-IEF) assisted TiO(2) chromatography (PIAT), for the highly efficient enrichment of sialoglycopeptides. In this method, peptides were first separated into 24 fractions using peptide IPG-IEF. Sialoglycopeptides were relatively concentrated in low-pH fractions of the immobilized pH strips and were captured using TiO(2) chromatography. As a result, 614 N-glycosylation sites were identified in 582 sialoglycopeptides within 322 sialoglycoproteins from rat liver using PIAT. To our knowledge, this work represents one of the most comprehensive sialoglycoproteomic analyses in general and exhibits the largest database of sialoglycoproteome in rat liver currently. So the new strategy introduced here exhibits high efficiency and universality in the sialoglycopeptide enrichment, and is a powerful tool for sialoglycoproteome exploration.

Tracking anatomical structures in x-ray sequences has broad applications, such as motion compensation for dynamic 3D/2D model overlay during image guided interventions. Many anatomical structures are curve-like such as ribs and liver dome. To handle various types of anatomical curves, a generic and robust tracking framework is needed to track shapes of different anatomies in noisy x-ray images. In this paper, we present a novel tracking framework, which is based on adaptive measurements of structures' shape, motion, and image intensity patterns. The framework does not need offline training to achieve robust tracking results. The framework also incorporates an online learning method to robustly adapt to anatomical structures of different shape and appearances. Experimental results on real-world clinical sequences confirm that the presented anatomical curve tracking method improves the tracking performance compared to a baseline performance.

In Europe only, about 100,000 deaths per year are related to cirrhosis or liver cancer. While surgery remains the option that offers the foremost success rate against such pathologies, several limitations still hinder its widespread development. Among the limiting factors is the lack of accurate planning systems, which has been a motivation for several recent works, aiming at better resection planning and training systems, relying on pre-operative imaging, anatomical and biomechanical modelling. While the vascular network in the liver plays a key role in defining the operative strategy, its influence at a biomechanical level has not been taken into account. In the paper we propose a real-time model of vascularized organs such as the liver. The model takes into account separate constitutive laws for the parenchyma and vessels, and defines a coupling mechanism between these two entities. In the evaluation section, we present results of in vitro porcine liver experiments that indicate a significant influence of vascular structures on the mechanical behaviour of tissue. We confirm the values obtained in the experiments by computer simulation using standard FEM. Finally, we show that the conventional modelling approach can be efficiently approximated with the proposed composite model capable of real-time calculations.

In this paper we present a hybrid OD-3D modeling method to investigate the hepatic flow in a virtual right lobe hepatectomy (RLH), the surgical procedure for adult-to-adult living donor liver transplanation (LDLT). The 3D method is employed to simulate complex 3D flow in the portal vein, and the OD model is used to study the systemic hepatic circulation. In particular, we quantify the flow velocity and wall shear stress (WSS) in the left portal vein which increase dramatically post-RLH, and also simulate the essential hepatic distribution features in a healthy adult pre- and post-procedure. We further predict the arterial flow in the remnant left liver, which would decrease due to a hepatic arterial buffer response (HABR) effect. Finally we discuss the physiological significance of these phenomena, and the potential of this hybrid modeling approach.