According to current evidence, localized kidney tumors are best treated by partial nephrectomy to preserve as much functional renal tissue as possible without sacrificing survival chances. In many expertise centers, a robotic-assisted technique is currently preferred over open surgery, mainly due to its benefits regarding post-operative recovery.
Currently, the robot surgery technique (Fig. 1) heavily depends on the limited intraoperative view and the 3D insight of the surgeon based on pre-operative imaging. Hence, 3D reconstructions could be really useful to assist intra-operatively in robot-assisted partial nephrectomy. 3D models could also be used to pre-operatively plan the surgical procedure. More specifically, the possible cutting locations should be investigated based on a detailed morphological analysis, while at the same time making sure that sufficient kidney function is preserved. Hence, the kidney should be still viable and foreseen of sufficient blood supply after partial nephrectomy.
The development of a planning tool that can help to determine the optimal surgical strategy (pre-operatively and intra-operatively) would thus be most welcome. In this way, chances for successful partial nephrectomies with negative cutting edges and maximum maintenance of healthy renal tissue would be higher.
Fig. 1. Illustrations of robot assisted surgery
The goal of this master thesis is to further develop and optimize a planning tool for robot assisted partial nephrectomy surgery based on 3D reconstructions of kidneys (and their tumors), and to test this approach during a feasibility study using patient-/animal-specific data.
As a first step, imaging data ((micro-)CT, MRI) will be acquired on the 3D geometry of a kidney. These data will have to be segmented in order to obtain 3D reconstructions of the kidney as a whole, the tumor(s) and its vascular tree (see Fig. 2 for an example).
Based on these reconstructions, a detailed morphological analysis of the vascular tree and the surrounding tissue will have to be performed in order to know which tissue is supplied by which blood vessel(s). Based on this information, we can simulate which region of the kidney is affected when cutting a certain blood vessel. An extension of this approach can help to pre-operatively plan partial nephrectomy procedures.
A possible extension of this feasibility study would be to setup an experiment in order to test our approach using a porcine or human kidney. This might include an ex-vivo isolated kidney perfusion setup, as well as vascular corrosion casting and imaging (Fig. 2) of the vasculature in order to validate the predictions of the partial nephrectomy planning tool.
In conclusion, this thesis will focus on the development and finetuning of a planning tool for robot assisted partial nephrectomy surgery based on 3D kidney models to improve pre-operative and intra-operative surgical planning. The student will have to program in Python on top of an initial code that is already available. As such, techniques for morphological operations, region growing, skeletonization (e.g. thinning algorithms) and graph analysis will be needed.
Fig. 2. Vascular corrosion cast and 3D reconstruction of the vascular trees of a human kidney