Single-view X-ray depth recovery: toward a novel concept for image-guided interventions.

PURPOSE: X-ray imaging is widely used for guiding minimally invasive surgeries. Despite ongoing efforts in particular toward advanced visualization incorporating mixed reality concepts, correct depth perception from X-ray imaging is still hampered due to its projective nature. METHODS: In this paper, we introduce a new concept for predicting depth information from single-view X-ray images. Patient-specific training data for depth and corresponding X-ray attenuation information are constructed using readily available preoperative 3D image information. The corresponding depth model is learned employing a novel label-consistent dictionary learning method incorporating atlas and spatial prior constraints to allow for efficient reconstruction performance. RESULTS: We have validated our algorithm on patient data acquired for different anatomy focus (abdomen and thorax). Of 100 image pairs per each of 6 experimental instances, 80 images have been used for training and 20 for testing. Depth estimation results have been compared to ground truth depth values. CONCLUSION: We have achieved around [Formula: see text] and [Formula: see text] mean squared error on abdomen and thorax datasets, respectively, and visual results of our proposed method are very promising. We have therefore presented a new concept for enhancing depth perception for image-guided interventions.

Get the most out of your metagenome: computational analysis of environmental sequence data.

New advances in sequencing technologies bring random shotgun sequencing of ecosystems within reach of smaller labs, but the complexity of metagenomics data can be overwhelming. Recently, many novel computational tools have been developed to unravel ecosystem properties starting from fragmented sequences. In addition, the so-called 'comparative metagenomics' approaches have allowed the discovery of specific genomic and community adaptations to environmental factors. However, many of the parameters extracted from these data to describe the environment at hand (e.g. genomic features, functional complement, phylogenetic composition) are interdependent and influenced by technical aspects of sample preparation and data treatment, leading to various pitfalls during analysis. To avoid this and complement existing initiatives in data standards, we propose a minimal standard for metagenomics data analysis ('MINIMESS') to be able to take full advantage of the power of comparative metagenomics in understanding microbial life on earth.