Abstract:

The segmentation and registration of structures are gaining importance due to the increasing demand of auto- mated image enhancement and understanding. Especially in medicine and life science, assistance systems could have a large impact on diagnosis, treatment and quality control. Dye driven procedures, such as uorescence imaging with Indocya- nine green (ICG), are nowadays indispensable because they enhance contrast, reveal structures and deliver the operator with important information. The contact free ICG angiography is providing the surgeon spatial and temporal information on blood ow w ithin a v essel. T he p rocessing o f t hose informa- tion is done manually or semi automated but is very helpful for the surgeon. Extending the degree of automatism, the amount of information processed and even augment or transfer it into another domain could deliver the operator useful support and improve surgical work ow. Using, analyzing and transferring those information from ICG-IR domain into the RGB domain is the focus of this project. We are introducing a vessel regis- tration method in the RGB domain driven by the spatial u- orescence behavior of the vessel in the ICG-IR domain. The method includes Superpixel based segmentation of the vessel in the ICG-IR domain, the spatial gradient based transfer and registration in the RGB domain and the continuous segmen- tation of the vessel in a RGB video. This paper show a proof of concept of the method. The results show an successful in- ter domain information transfer and registration of the vessel. Further tracking of the vessel over all frames is possible. Nev- ertheless limitations are revealed and discussed.

Abstract:

Image segmentation plays an increasingly important role in image processing. It allows for various applications including the analysis of an image for automatic image understanding and the integration of complementary data. During vascular surgeries, the blood flow in the vessels has to be checked constantly, which could be facilitated by a segmentation of the affected vessels. The segmentation of medical images is still done manually, which depends on the surgeon’s experience and is time-consuming. As a result, there is a growing need for automatic image segmentation methods. We propose an unsupervised method to detect the regions of no interest (RONI) in intraoperative images with low depth-of-field (DOF). The proposed method is divided into three steps. First, a color segmentation using a clustering algorithm is performed. In a second step, we assume that the regions of interest (ROI) are in focus whereas the RONI are unfocused. This allows us to segment the image using an edge-based focus measure. Finally, we combine the focused edges with the color RONI to determine the final segmentation result. When tested on different intraoperative images of aneurysm clipping surgeries, the algorithm is able to segment most of the RONI not belonging to the pulsating vessel of interest. Surgical instruments like the metallic clips can also be excluded. Although the image data for the validation of the proposed method is limited to one intraoperative video, a proof of concept is demonstrated.