Kidney targeting and puncturing during percutaneous nephrolithotomy: recent advances and future perspectives

Background and Purpose: Precise needle puncture of the kidney is a challenging and essential step for successful percutaneous nephrolithotomy (PCNL). Many devices and surgical techniques have been developed to easily achieve suitable renal access. This article presents a critical review to address the methodologies and techniques for conducting kidney targeting and the puncture step during PCNL. Based on this study, research paths are also provided for PCNL procedure improvement. Methods: Most relevant works concerning PCNL puncture were identified by a search of Medline/PubMed, ISI Web of Science, and Scopus databases from 2007 to December 2012. Two authors independently reviewed the studies. Results: A total of 911 abstracts and 346 full-text articles were assessed and discussed; 52 were included in this review as a summary of the main contributions to kidney targeting and puncturing. Conclusions: Multiple paths and technologic advances have been proposed in the field of urology and minimally invasive surgery to improve PCNL puncture. The most relevant contributions, however, have been provided by the applicationofmedical imaging guidance, newsurgical tools,motion tracking systems, robotics, andimage processing and computer graphics. Despite the multiple research paths for PCNL puncture guidance, no widely acceptable solution has yet been reached, and it remains an active and challenging research field. Future developments should focus on real-time methods, robust and accurate algorithms, and radiation free imaging techniques

Kidney targeting and puncturing during percutaneous nephrolithotomy:recent advances and future perspectives

Precise needle puncture of the kidney is a challenging and essential step for successful percutaneous nephrolithotomy (PCNL). Many devices and surgical techniques have been developed to easily achieve suitable renal access. This article presents a critical review to address the methodologies and techniques for conducting kidney targeting and the puncture step during PCNL. Based on this study, research paths are also provided for PCNL procedure improvement. Methods: Most relevant works concerning PCNL puncture were identified by a search of Medline/PubMed, ISI Web of Science, and Scopus databases from 2007 to December 2012. Two authors independently reviewed the studies.

Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans

In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a process often used as disease progression readout and to develop therapeutic strategies. This work presents an image processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode Caenorhabditis Elegans. A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals of distinct genetic conditions. Because of the animals’ transparency, most of the slices pixels appeared dark, hampering their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm that uses the Tukey’s biweight as edge-stopping function. The image histogram median of this outcome was used to dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations were subsequently segmented based on an iso-value and blended with the resulting volume mesh. The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative diseases treatment planning and interventions prevention