A fast and robust hepatocyte quantification algorithm including vein processing

<p>Abstract</p> <p>Background</p> <p>Quantification of different types of cells is often needed for analysis of histological images. In our project, we compute the relative number of proliferating hepatocytes for the evaluation of the regeneration process after partial hepatectomy in normal rat livers.</p> <p>Results</p> <p>Our presented automatic approach for hepatocyte (HC) quantification is suitable for the analysis of an entire digitized histological section given in form of a series of images. It is the main part of an automatic hepatocyte quantification tool that allows for the computation of the ratio between the number of proliferating HC-nuclei and the total number of all HC-nuclei for a series of images in one processing run. The processing pipeline allows us to obtain desired and valuable results for a wide range of images with different properties without additional parameter adjustment. Comparing the obtained segmentation results with a manually retrieved segmentation mask which is considered to be the ground truth, we achieve results with sensitivity above 90% and false positive fraction below 15%.</p> <p>Conclusions</p> <p>The proposed automatic procedure gives results with high sensitivity and low false positive fraction and can be applied to process entire stained sections.</p

Segmentation and 3D reconstruction of animal tissues in histological images

Histology is considered the "gold standard" to access anatomical informationat a cellular level. In histological studies, tissue samples are cut into very thinsections, stained, and observed under a microscope by a specialist. Such studies,mainly concerning tissue structures, cellular components and their interactions, canbe useful to detect and diagnose certain pathologies. Thus, to find new techniquesand computational solutions to assist this diagnosis, such as the 3D image basedtissue reconstruction, is extremely interesting. In this chapter, a methodology tobuild 3D models from histological images is proposed, and the results obtained usingthis methodology in four experimental cases are presented and discussed based onquantitative and qualitative metrics

Liver Segmentation and its Application to Hepatic Interventions

The thesis addresses the development of an intuitive and accurate liver segmentation approach, its integration into software prototypes for the planning of liver interventions, and research on liver regeneration. The developed liver segmentation approach is based on a combination of the live wire paradigm and shape-based interpolation. Extended with two correction modes and integrated into a user-friendly workflow, the method has been applied to more than 5000 data sets. The combination of the liver segmentation with image analysis of hepatic vessels and tumors allows for the computation of anatomical and functional remnant liver volumes. In several projects with clinical partners world-wide, the benefit of the computer-assisted planning was shown. New insights about the postoperative liver function and regeneration could be gained, and most recent investigations into the analysis of MRI data provide the option to further improve hepatic intervention planning