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Background Benign Multicystic Peritoneal Mesothelioma (BMPM) is one of the rarest diseases in medicine with only more than 200 cases worldwide. This paper aims to report a case of Benign Multicystic Peritoneal Mesothelioma that strangely arose from the liver and was long treated as Hydatid cyst. The case also had many risk factors including asbestos exposure that had not yet been linked with Benign Multicystic Peritoneal Mesothelioma. Case presentation We report a case of a 62 years old male with a history of a perforated peptic ulcer and a cystic mass in the liver that was misdiagnosed as hydatid cyst 7 years ago. He presented with generalized abdominal pain and bloating. Image studies showed many cystic formations filled with clear fluid. An en bloc surgery was performed and a pathologic study showed a multiloculated mass lined by flat or cuboidal epithelium leading to the diagnosis of BMPM. A follow up was scheduled after 3 months revealed total recurrence. Conclusion BMPM resembles many other cystic lesions in the abdomen and should be taken into consideration when dealing with nontypical cystic formations. Its diagnostic and treatment methods are still hazy making this disease difficult to approach.

The objective of this paper is to address the dramatic impact of multislice CT (MSCT) in imaging the liver. Standard helical (spiral) CT has finally allowed for scanning the majority of the liver during the critical portal venous phase (PVP). This is often referred to as the ‘optimal temporal window’. In general, it occurs following a 70 s scan delay and is coincidental with the maximal delivery of contrast via the portal vein that provides 80% of the hepatic blood supply. This yields maximal conspicuity between low-attenuation lesions and the dramatically enhanced normal liver parenchyma at routine injection rates of 2–3 ml/s. Most importantly, these scanners, when compared to single-slice scanners, avoid impinging on the ‘equilibrium’ phase where tumors can become isodense/invisible. The introduction of MSCT with four, eight and 16-detector systems has significantly increased imaging speed. Volumetric CT will continue to increase speed in the future. This provides a number of important gains that will be described.

Background Fuzzy connectedness method has shown its effectiveness for fuzzy object extraction in recent years. However, two problems may occur when applying it to hepatic vessel segmentation task. One is the excessive computational cost, and the other is the difficulty of choosing a proper threshold value for final segmentation. Methods In this paper, an accelerated strategy based on a lookup table was presented first which can reduce the connectivity scene calculation time and achieve a speed-up factor of above 2. When the computing of the fuzzy connectedness relations is finished, a threshold is needed to generate the final result. Currently the threshold is preset by users. Since different thresholds may produce different outcomes, how to determine a proper threshold is crucial. According to our analysis of the hepatic vessel structure, a watershed-like method was used to find the optimal threshold. Meanwhile, by using Ostu algorithm to calculate the parameters for affinity relations and assigning the seed with the mean value, it is able to reduce the influence on the segmentation result caused by the location of the seed and enhance the robustness of fuzzy connectedness method. Results Experiments based on four different datasets demonstrate the efficiency of the lookup table strategy. These experiments also show that an adaptive threshold found by watershed-like method can always generate correct segmentation results of hepatic vessels. Comparing to a refined region-growing algorithm that has been widely used for hepatic vessel segmentation, fuzzy connectedness method has advantages in detecting vascular edge and generating more than one vessel system through the weak connectivity of the vessel ends. Conclusions An improved algorithm based on fuzzy connectedness method is proposed. This algorithm has improved the performance of fuzzy connectedness method in hepatic vessel segmentation.

[Background]Recent advances in microscopy enable the acquisition of large numbers of tomographic images from living tissues. Three-dimensional microscope images are often displayed with volume rendering by adjusting the transfer functions. However, because the emissions from fluorescent materials and the optical properties based on point spread functions affect the imaging results, the intensity value can differ locally, even in the same structure. Further, images obtained from brain tissues contain a variety of neural structures such as dendrites and axons with complex crossings and overlapping linear structures. In these cases, the transfer functions previously used fail to optimize image generation, making it difficult to explore the connectivity of these tissues. [Results]This paper proposes an interactive visual exploration method by which the transfer functions are modified locally and interactively based on multidimensional features in the images. A direct editing interface is also provided to specify both the target region and structures with characteristic features, where all manual operations can be performed on the rendered image. This method is demonstrated using two-photon microscope images acquired from living mice, and is shown to be an effective method for interactive visual exploration of overlapping similar structures. [Conclusions]An interactive visualization method was introduced for local improvement of visualization by volume rendering in two-photon microscope images containing regions in which linear nerve structures crisscross in a complex manner. The proposed method is characterized by the localized multidimensional transfer function and interface where the parameters can be determined by the user to suit their particular visualization requirements.

Background Marine polyunsaturated fatty acids, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) have been associated with improvement in the Metabolic Syndrome (MS). The aim of this study is to evaluate how three fish-oil diets with different eicosapentaenoic acid/docosahexaenoic acid ratios (EPA/DHA ratio) affect the histology of liver, kidney, adipose tissue and aorta in a preliminary morphological study. This work uses an animal model of metabolic syndrome in comparison with healthy animals in order to provide information about the best EPA:DHA ratio to prevent or to improve metabolic syndrome symptoms. Methods 35 Wistar rats, as a control, and 35 spontaneously hypertensive obese rats (SHROB) were fed for 13 weeks with 3 different suplemmentation of fish oil containing EPA and DHA ratios (1:1, 2:1 and 1:2, respectively). All samples were stained with haematoxylin/eosin stain, except aorta samples, which were stained also with Verhoeff and van Gieson’s stain. A histological study was carried out to evaluate changes. These changes were statistically analyzed using SPSS IBM 19 software. The quantitative data were expressed by mean ± SD and were compared among groups and treatments using ANOVA with post-hoc tests for parametric data and the U-Mann–Whitney for non-parametric data. Qualitative data were expressed in frequencies, and compared with contingency tables using χ2 statistics. Results EPA:DHA 1:1 treatment tended to improve the density and the wrinkling of elastic layers in SHROB rats. Only Wistar rats fed with EPA:DHA 1:1 treatment did not show mast cells in adipose tissue and has less kidney atrophy. In both strains EPA:DHA 1:1 treatment improved inflammation related parameters in liver and kidney. Conclusions EPA:DHA 1:1 treatment was the most beneficial treatment since improved many histological parameters in both groups of rats., This investigation was supported, in part, by the Spanish Ministry of Science and Innovation (Grants AGL2009-12374-C03-01,-02 and -03). EM acknowledges the Panamanian government (SENACYT / IFARHU) for her pre-doctoral fellowship. We thank URV’s Language Service for rewriting the English version of this paper. SUPPORTED BY: Proyectos de Investigación Fundamental, Ministerio Español de Ciencia e Innovación, Plan Nacional 2009.Referencia: AGL2009-12374-C03-01, -02 and -03.

Background Artificial neural networks (ANNs) are widely studied for evaluating diseases. This paper discusses the intelligence mode of an ANN in grading the diagnosis of liver fibrosis by duplex ultrasonogaphy. Methods 239 patients who were confirmed as having liver fibrosis or cirrhosis by ultrasound guided liver biopsy were investigated in this study. We quantified ultrasonographic parameters as significant parameters using a data optimization procedure applied to an ANN. 179 patients were typed at random as the training group; 60 additional patients were consequently enrolled as the validating group. Performance of the ANN was evaluated according to accuracy, sensitivity, specificity, Youden’s index and receiver operating characteristic (ROC) analysis. Results 5 ultrasonographic parameters; i.e., the liver parenchyma, thickness of spleen, hepatic vein (HV) waveform, hepatic artery pulsatile index (HAPI) and HV damping index (HVDI), were enrolled as the input neurons in the ANN model. The sensitivity, specificity and accuracy of the ANN model for quantitative diagnosis of liver fibrosis were 95.0%, 85.0% and 88.3%, respectively. The Youden’s index (YI) was 0.80. Conclusions The established ANN model had good sensitivity and specificity in quantitative diagnosis of hepatic fibrosis or liver cirrhosis. Our study suggests that the ANN model based on duplex ultrasound may help non-invasive grading diagnosis of liver fibrosis in clinical practice.