Automatic prebent customized prosthesis for pectus excavatum minimally invasive surgery correction

Pectus excavatum is the most common deformity of the thorax. A minimally invasive surgical correction is commonly carried out to remodel the anterior chest wall by using an intrathoracic convex prosthesis in the substernal position. The process of prosthesis modeling and bending still remains an area of improvement. The authors developed a new system, i3DExcavatum, which can automatically model and bend the bar preoperatively based on a thoracic CT scan. This article presents a comparison between automatic and manual bending. The i3DExcavatum was used to personalize prostheses for 41 patients who underwent pectus excavatum surgical correction between 2007 and 2012. Regarding the anatomical variations, the soft-tissue thicknesses external to the ribs show that both symmetric and asymmetric patients always have asymmetric variations, by comparing the patients’ sides. It highlighted that the prosthesis bar should be modeled according to each patient’s rib positions and dimensions. The average differences between the skin and costal line curvature lengths were 84 ± 4 mm and 96 ± 11 mm, for male and female patients, respectively. On the other hand, the i3DExcavatum ensured a smooth curvature of the surgical prosthesis and was capable of predicting and simulating a virtual shape and size of the bar for asymmetric and symmetric patients. In conclusion, the i3DExcavatum allows preoperative personalization according to the thoracic morphology of each patient. It reduces surgery time and minimizes the margin error introduced by the manually bent bar, which only uses a template that copies the chest wall curvature.

Pectus Excavatum postsurgical outcome based on preoperative soft body dynamics simulation

Pectus excavatum is the most common congenital deformity of the anterior chest wall, in which an abnormal formation of the rib cage gives the chest a caved-in or sunken appearance. Today, the surgical correction of this deformity is carried out in children and adults through Nuss technic, which consists in the placement of a prosthetic bar under the sternum and over the ribs. Although this technique has been shown to be safe and reliable, not all patients have achieved adequate cosmetic outcome. This often leads to psychological problems and social stress, before and after the surgical correction. This paper targets this particular problem by presenting a method to predict the patient surgical outcome based on pre-surgical imagiologic information and chest skin dynamic modulation. The proposed approach uses the patient pre-surgical thoracic CT scan and anatomical-surgical references to perform a 3D segmentation of the left ribs, right ribs, sternum and skin. The technique encompasses three steps: a) approximation of the cartilages, between the ribs and the sternum, trough b-spline interpolation; b) a volumetric mass spring model that connects two layers - inner skin layer based on the outer pleura contour and the outer surface skin; and c) displacement of the sternum according to the prosthetic bar position. A dynamic model of the skin around the chest wall region was generated, capable of simulating the effect of the movement of the prosthetic bar along the sternum. The results were compared and validated with patient postsurgical skin surface acquired with Polhemus FastSCAN system

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

Automatic prebent customized prosthesis for pectus excavatum minimally invasive surgery correction

Pectus excavatum is the most common deformity of the thorax. A minimally invasive surgical correction is commonly carried out to remodel the anterior chest wall by using an intrathoracic convex prosthesis in the substernal position. The process of prosthesis modeling and bending still remains an area of improvement. The authors developed a new system, i3DExcavatum, which can automatically model and bend the bar preoperatively based on a thoracic CT scan. This article presents a comparison between automatic and manual bending. The i3DExcavatum was used to personalize prostheses for 41 patients who underwent pectus excavatum surgical correction between 2007 and 2012. Regarding the anatomical variations, the soft-tissue thicknesses external to the ribs show that both symmetric and asymmetric patients always have asymmetric variations, by comparing the patients’ sides. It highlighted that the prosthesis bar should be modeled according to each patient’s rib positions and dimensions. The average differences between the skin and costal line curvature lengths were 84 ± 4 mm and 96 ± 11 mm, for male and female patients, respectively. On the other hand, the i3DExcavatum ensured a smooth curvature of the surgical prosthesis and was capable of predicting and simulating a virtual shape and size of the bar for asymmetric and symmetric patients. In conclusion, the i3DExcavatum allows preoperative personalization according to the thoracic morphology of each patient. It reduces surgery time and minimizes the margin error introduced by the manually bent bar, which only uses a template that copies the chest wall curvature.

Automatic prebent customized prosthesis for pectus excavatum minimally invasive surgery correction

Pectus excavatum is the most common deformity of the thorax. A minimally invasive surgical correction is commonly carried out to remodel the anterior chest wall by using an intrathoracic convex prosthesis in the substernal position. The process of prosthesis modeling and bending still remains an area of improvement. The authors developed a new system, i3DExcavatum, which can automatically model and bend the bar preoperatively based on a thoracic CT scan. This article presents a comparison between automatic and manual bending. The i3DExcavatum was used to personalize prostheses for 41 patients who underwent pectus excavatum surgical correction between 2007 and 2012. Regarding the anatomical variations, the soft-tissue thicknesses external to the ribs show that both symmetric and asymmetric patients always have asymmetric variations, by comparing the patients’ sides. It highlighted that the prosthesis bar should be modeled according to each patient’s rib positions and dimensions. The average differences between the skin and costal line curvature lengths were 84 ± 4 mm and 96 ± 11 mm, for male and female patients, respectively. On the other hand, the i3DExcavatum ensured a smooth curvature of the surgical prosthesis and was capable of predicting and simulating a virtual shape and size of the bar for asymmetric and symmetric patients. In conclusion, the i3DExcavatum allows preoperative personalization according to the thoracic morphology of each patient. It reduces surgery time and minimizes the margin error introduced by the manually bent bar, which only uses a template that copies the chest wall curvature.

Survival Analysis Employed in Predicting Corporate Failure: A Forecasting Model Proposal

In face of the current economic and financial environment, predicting corporate bankruptcy is arguably a phenomenon of increasing interest to investors, creditors, borrowing firms, and governments alike. Within the strand of literature focused on bankruptcy forecasting we can find diverse types of research employing a wide variety of techniques, but only a few researchers have used survival analysis for the examination of this issue. We propose a model for the prediction of corporate bankruptcy based on survival analysis, a technique which stands on its own merits. In this research, the hazard rate is the probability of ‘‘bankruptcy’’ as of time t, conditional upon having survived until time t. Many hazard models are applied in a context where the running of time naturally affects the hazard rate. The model employed in this paper uses the time of survival or the hazard risk as dependent variable, considering the unsuccessful companies as censured observations.

Semi-automatic 3D Segmentation Of Costal Cartilage In CT Data From Pectus Excavatum Patients

One of the current frontiers in the clinical management of Pectus Excavatum (PE) patients is the prediction of the surgical outcome prior to the intervention. This can be done through computerized simulation of the Nuss procedure, which requires an anatomically correct representation of the costal cartilage. To this end, we take advantage of the costal cartilage tubular structure to detect it through multi-scale vesselness filtering. This information is then used in an interactive 2D initialization procedure which uses anatomical maximum intensity projections of 3D vesselness feature images to efficiently initialize the 3D segmentation process. We identify the cartilage tissue centerlines in these projected 2D images using a livewire approach. We finally refine the 3D cartilage surface through region-based sparse field level-sets. We have tested the proposed algorithm in 6 noncontrast CT datasets from PE patients. A good segmentation performance was found against reference manual contouring, with an average Dice coefficient of 0.75±0.04 and an average mean surface distance of 1.69±0.30mm. The proposed method requires roughly 1 minute for the interactive initialization step, which can positively contribute to an extended use of this tool in clinical practice, since current manual delineation of the costal cartilage can take up to an hour.