Guest Editorial : Special issue on advanced computing for image-guided intervention

Editorial Guest Editorial: Special issue on advanced computing for image-guided intervention In the past years, we have witnessed a growing number of applications of minimally invasive or non-invasive interventions in clinical practice, where imaging is playing an essential role for the success of both diagnosis and therapy. Particularly, advanced signal and image processing algorithms are receiving increasing attention, which aim to provide accurate and reliable information directly to physicians. We have seen the applications of these technologies during all stages of an intervention, including preoperational planning, intra-operational guidance and post-operational verification. For this special issue, we have received a significant number of submissions from both academia and industry, out of which we have carefully selected eleven articles with outstanding quality. These articles have covered the topics of anatomic structure identification and tracking, image registration, data visualization and newly emerging applications. In [1] have addressed the image registration problem between preand post-radiated MRI to facilitate the evaluation of the therapeutic response after External Beam Radiation Treatment (EBRT) for the prostate cancer. A different approach has been employed by We have also included three papers on ultrasound-guided image interventions. In We have included in this special issue two papers on tissue characterization from endoscopic images. Nawarathna et al. have proposed in With the increasing use of various imaging modalities in image-guided intervention and therapy, how to optimally present and visualize the data becomes also an important issue. In [10], the authors have addressed the use of autostereoscopic volumetric visualization of the patient's anatomy, which has the potential to be combined with augmented reality. The paper especially addresses the latency problem in the visualization chain, and a few improvements have been proposed. A new adjacent application has been presented in In summary, we have seen from submissions to this special issue a growing interest in applying advanced signal and image processing technologies to image-guided interventions. The submissions have covered a wide range of clinical applications using various imaging modalities. Image feature extraction remains to be an important subject and it has to be specifically designed to suit the needs for specific applications. Learning-based approaches have also attracted a lot of attention, especially in applications requiring automatic tissue characterization and classification. We are also very happy to have received new emerging applications which are able to extend the traditional interventional imaging into greater application areas. Acknowledgments We would like to thank all the reviewers who have helped to peer-review the submitted papers and their constructive comments are well appreciated

Scalable joint segmentation and registration framework for infant brain images

The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately measure structure changes is critical in early brain development study, which highly relies on the performances of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than segmentation/registration of adult brains due to dynamic appearance change with rapid brain development. In fact, image segmentation and registration of infant images can assists each other to overcome the above challenges by using the growth trajectories (i.e., temporal correspondences) learned from a large set of training subjects with complete longitudinal data. Specifically, a one-year-old image with ground-truth tissue segmentation can be first set as the reference domain. Then, to register the infant image of a new subject at earlier age, we can estimate its tissue probability maps, i.e., with sparse patch-based multi-atlas label fusion technique, where only the training images at the respective age are considered as atlases since they have similar image appearance. Next, these probability maps can be fused as a good initialization to guide the level set segmentation. Thus, image registration between the new infant image and the reference image is free of difficulty of appearance changes, by establishing correspondences upon the reasonably segmented images. Importantly, the segmentation of new infant image can be further enhanced by propagating the much more reliable label fusion heuristics at the reference domain to the corresponding location of the new infant image via the learned growth trajectories, which brings image segmentation and registration to assist each other. It is worth noting that our joint segmentation and registration framework is also flexible to handle the registration of any two infant images even with significant age gap in the first year of life, by linking their joint segmentation and registration through the reference domain. Thus, our proposed joint segmentation and registration method is scalable to various registration tasks in early brain development studies. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our method in early brain development study