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.

Real-time 3D interactive segmentation of echocardiographic data through user-based deformation of B-spline explicit active surfaces

Image segmentation is an ubiquitous task in medical image analysis, which is required to estimate morphological or functional properties of given anatomical targets. While automatic processing is highly desirable, image segmentation remains to date a supervised process in daily clinical practice. Indeed, challenging data often requires user interaction to capture the required level of anatomical detail. To optimize the analysis of 3D images, the user should be able to efficiently interact with the result of any segmentation algorithm to correct any possible disagreement. Building on a previously developed real-time 3D segmentation algorithm, we propose in the present work an extension towards an interactive application where user information can be used online to steer the segmentation result. This enables a synergistic collaboration between the operator and the underlying segmentation algorithm, thus contributing to higher segmentation accuracy, while keeping total analysis time competitive. To this end, we formalize the user interaction paradigm using a geometrical approach, where the user input is mapped to a non-cartesian space while this information is used to drive the boundary towards the position provided by the user. Additionally, we propose a shape regularization term which improves the interaction with the segmented surface, thereby making the interactive segmentation process less cumbersome. The resulting algorithm offers competitive performance both in terms of segmentation accuracy, as well as in terms of total analysis time. This contributes to a more efficient use of the existing segmentation tools in daily clinical practice. Furthermore, it compares favorably to state-of-the-art interactive segmentation software based on a 3D livewire-based algorithm.

Hand-held robotic device for laparoscopic surgery and training

Laparoscopic surgery (LS) has revolutionized traditional surgical techniques introducing minimally invasive procedures for diagnosis and local therapies. LSs have undeniable advantages, such as small patient incisions, reduced postoperative pain and faster recovery. On the other hand, restricted vision of the anatomical target, difficult handling of the surgical instruments, restricted mobility inside the human body, need of dexterity to hand-eye coordination and inadequate and non-ergonomic surgical instruments may restrict LS only to more specialized surgeons. To overcome the referred limitations, this work presents a new robotic surgical handheld system – the EndoRobot. The EndoRobot was designed to be used in clinical practice or even as a surgical simulator. It integrates an electromechanical system with 3 degrees of freedom. Each degree can be manipulated independently and combined with different levels of sensitivity allowing fast and slow movements. As other features, the EndoRobot has battery power or external power supply, enables the use of bipolar radiofrequency to prevent bleeding while cutting and allows plug-and-play of the laparoscopic forceps for rapid exchange. As a surgical simulator, the system was also instrumented to measure and transmit, in real time, its position and orientation for a training software able to monitor and assist the trainee’s surgical movements.