Hip Joint Center Localization with an Unscented Kalman Filter

The accurate estimation of the hip joint centre (HJC) in gait analysis and in computer assisted orthopaedic procedures is a basic requirement. Functional methods, based on rigid body localisation, assessing the kinematics of the femur during circumduction movements (pivoting) have been used for estimating the HJC. Localising the femoral segment only, as it is usually done in total knee replacement procedure, can give rise to estimation errors, since the pelvis, during the passive pivoting manoeuvre, might undergo spatial displacements. This paper presents the design and test of an unscented Kalman filter that allows the estimation of the HJC by observing the pose of the femur and the 3D coordinates of a single marker attached to the pelvis. This new approach was validated using a hip joint mechanical simulator, mimicking both hard and soft tissues. The algorithm performances were compared with the literature standards and proved to have better performances in case of pelvis translation greater than 8 mm, thus satisfying the clinical requirements of the application

Development of a surgical stereo endoscopic image dataset for validating 3D stereo reconstruction algorithms

In the last decades, endoscopic stereo images have been exploited to retrieve tissue surface information of the surgical site using 3D reconstruction algorithms. The application of such algorithms in Computer Assisted Surgery (CAS) tools for Minimally Invasive Surgery (MIS) requires a robust validation process in order to guarantee reliability and safety. 3D reconstruction algorithms are commonly evaluated comparing their result with respect to a reference Ground Truth (GT). However, few datasets providing endoscopic images and GT are openly available. Considering the increasing necessity of surgical datasets, the aim of this work is the generation of an Endoscopic Abdominal Stereo (EndoAbS) dataset composed of stereo-images with associated GT for 3D stereo-reconstruction algorithm validation. To recreate the surgical scenario, a polyurethane surgical phantom abdomen was built. Images were captured with a stereo-endoscope, while for acquiring the GT a laser scanner (calibrated with respect to the stereoendoscope) was used. This dataset is openly available on-line for the benefit of the CAS community

Deep Learning Based Robotic Tool Detection and Articulation Estimation with Spatio-Temporal Layers

Surgical-tool joint detection from laparoscopic images is an important but challenging task in computer-assisted minimally invasive surgery. Illumination levels, variations in background and the different number of tools in the field of view, all pose difficulties to algorithm and model training. Yet, such challenges could be potentially tackled by exploiting the temporal information in laparoscopic videos to avoid per frame handling of the problem. In this letter, we propose a novel encoder-decoder architecture for surgical instrument joint detection and localization that uses three-dimensional convolutional layers to exploit spatio-temporal features from laparoscopic videos. When tested on benchmark and custom-built datasets, a median Dice similarity coefficient of 85.1% with an interquartile range of 4.6% highlights performance better than the state of the art based on single-frame processing. Alongside novelty of the network architecture, the idea for inclusion of temporal information appears to be particularly useful when processing images with unseen backgrounds during the training phase, which indicates that spatio-temporal features for joint detection help to generalize the solution

A computational fluid dynamics approach to determine white matter permeability

Glioblastomas represent a challenging problem with an extremely poor survival rate. Since these tumour cells have a highly invasive character, an effective surgical resection as well as chemotherapy and radiotherapy is very difficult. Convection-enhanced delivery (CED), a technique that consists in the injection of a therapeutic agent directly into the parenchyma, has shown encouraging results. Its efficacy depends on the ability to predict, in the pre-operative phase, the distribution of the drug inside the tumour. This paper proposes a method to compute a fundamental parameter for CED modelling outcomes, the hydraulic permeability, in three brain structures. Therefore, a bidimensional brain-like structure was built out of the main geometrical features of the white matter: axon diameter distribution extrapolated from electron microscopy images, extracellular space (ECS) volume fraction and ECS width. The axons were randomly allocated inside a defined border, and the ECS volume fraction as well as the ECS width maintained in a physiological range. To achieve this result, an outward packing method coupled with a disc shrinking technique was implemented. The fluid flow through the axons was computed by solving Navier–Stokes equations within the computational fluid dynamics solver ANSYS. From the fluid and pressure fields, an homogenisation technique allowed establishing the optimal representative volume element (RVE) size. The hydraulic permeability computed on the RVE was found in good agreement with experimental data from the literature

Learned and handcrafted features for early-stage laryngeal SCC diagnosis

Squamous cell carcinoma (SCC) is the most common and malignant laryngeal cancer. An early-stage diagnosis is of crucial importance to lower patient mortality and preserve both the laryngeal anatomy and vocal-fold function. However, this may be challenging as the initial larynx modifications, mainly concerning the mucosa vascular tree and the epithelium texture and color, are small and can pass unnoticed to the human eye. The primary goal of this paper was to investigate a learning-based approach to early-stage SCC diagnosis, and compare the use of (i) texture-based global descriptors, such as local binary patterns, and (ii) deep-learning-based descriptors. These features, extracted from endoscopic narrow-band images of the larynx, were classified with support vector machines as to discriminate healthy, precancerous, and early-stage SCC tissues. When tested on a benchmark dataset, a median classification recall of 98% was obtained with the best feature combination, outperforming the state of the art (recall = 95%). Despite further investigation is needed (e.g., testing on a larger dataset), the achieved results support the use of the developed methodology in the actual clinical practice to provide accurate early-stage SCC diagnosis. [Figure not available: see fulltext.]