[Modern imaging of liver and pancreatic neoplasms]

Modern imaging modalities play an outstanding role in the detection, characterization, staging, therapy planning, treatment outcome evaluation and follow-up of patients with liver and pancreatic neoplasms. Diagnostic performance and accuracy of the available modalities are continuously improving therefore, it is necessary to overview from time to time the diagnostic protocols and algorithms.

Virtual volume resection using multi-resolution triangular representation of B-spline surfaces

Computer assisted analysis of organs has an important role in clinical diagnosis and therapy planning. As well as the visualization, the manipulation of 3-dimensional (3D) objects are key features of medical image processing tools. The goal of this work was to develop an efficient and easy to use tool that allows the physician to partition a segmented organ into its segments or lobes. The proposed tool allows the user to define a cutting surface by drawing some traces on 2D sections of a 3D object, cut the object into two pieces with a smooth surface that fits the input traces, and iterate the process until the object is partitioned at the desired level. The tool is based on an algorithm that interpolates the user-defined traces with B-spline surface and computes a binary cutting volume that represents the different sides of the surface. The computation of the cutting volume is based on the multi-resolution triangulation of the B-spline surface. The proposed algorithm was integrated into an open-source medical image processing framework. Using the tool, the user can select the object to be partitioned (e.g. segmented liver), define the cutting surface based on the corresponding medical image (medical image visualizing the internal structure of the liver), cut the selected object, and iterate the process. In case of liver segment separation, the cuts can be performed according to a predefined sequence, which makes it possible to label the temporary as well as the final partitions (lobes, segments) automatically. The presented tool was evaluated for anatomical segment separation of the liver involving 14 cases and virtual liver tumor resection involving one case. The segment separation was repeated 3 different times by one physician for all cases, and the average and the standard deviation of segment volumes were computed. According to the test experiences the presented algorithm proved to be efficient and user-friendly enough to perform free form cuts for liver segment separation and virtual liver tumor resection. The volume quantification of segments showed good correlation with the prior art and the vessel-based liver segment separation, which demonstrate the clinical usability of the presented method.