IntroductionThe intra-hepatic vascular anatomy in rodents, its variations and corresponding supplying and draining territories in respect to the lobar structure of the liver have not been described. We performed a detailed anatomical imaging study in rats and mice to allow for further refinement of experimental surgical approaches.MethodsLEWIS-Rats and C57Bl/6N-Mice were subjected to ex-vivo imaging using μCT. The image data were used for semi-automated segmentation to extract the hepatic vascular tree as prerequisite for 3D visualization. The underlying vascular anatomy was reconstructed, analysed and used for determining hepatic vascular territories.ResultsThe four major liver lobes have their own lobar portal supply and hepatic drainage territories. In contrast, the paracaval liver is supplied by various small branches from right and caudate portal veins and drains directly into the vena cava. Variations in hepatic vascular anatomy were observed in terms of branching pattern and distance of branches to each other. The portal vein anatomy is more variable than the hepatic vein anatomy. Surgically relevant variations were primarily observed in portal venous supply.ConclusionsFor the first time the key variations of intrahepatic vascular anatomy in mice and rats and their surgical implications were described. We showed that lobar borders of the liver do not always match vascular territorial borders. These findings are of importance for the design of new surgical procedures and for understanding eventual complications following hepatic surgery.
The new and elaborate concept improves the quality of teaching. In the long run resources for patient care should be saved when training students according to this concept prior to performing tasks in the operating theater. These resources should be allocated for further refining innovative teaching concepts.
Quantitative analysis of histologic slides is of importance for pathology and also to address surgical questions. Recently, a novel application was developed for the automated quantification of whole-slide images. The aim of this study was to test and validate the underlying image analysis algorithm with respect to user friendliness, accuracy, and transferability to different histologic scenarios. The algorithm splits the images into tiles of a predetermined size and identifies the tissue class of each tile. In the training procedure, the user specifies example tiles of the different tissue classes. In the subsequent analysis procedure, the algorithm classifies each tile into the previously specified classes. User friendliness was evaluated by recording training time and testing reproducibility of the training procedure of users with different background. Accuracy was determined with respect to single and batch analysis. Transferability was demonstrated by analyzing tissue of different organs (rat liver, kidney, small bowel, and spleen) and with different stainings (glutamine synthetase and hematoxylin-eosin). Users of different educational background could apply the program efficiently after a short introduction. When analyzing images with similar properties, accuracy of >90% was reached in single images as well as in batch mode. We demonstrated that the novel application is user friendly and very accurate. With the "training" procedure the application can be adapted to novel image characteristics simply by giving examples of relevant tissue structures. Therefore, it is suitable for the fast and efficient analysis of high numbers of fully digitalized histologic sections, potentially allowing "high-throughput" quantitative "histomic" analysis.
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