Structured light for touchless 3D registration in video-based surgical navigation.

PURPOSE: Arthroscopic surgery, with its inherent difficulties on visibility and maneuverability inside the joint, poses significant challenges to surgeons. Video-based surgical navigation (VBSN) has proven to have clinical benefits in arthroscopy but relies on a time-consuming and challenging surface digitization using a touch probe to accomplish registration of intraoperative data with preoperative anatomical models. This paper presents an off-the-shelf laser scanner for noninvasive registration that enables an increased area of reachable region. METHODS: Our solution uses a standard arthroscope and a light projector with visual markers for real-time extrinsic calibration. Nevertheless, the shift from a touch probe to a laser scanner introduces a new challenge-the presence of a significant amount of outliers resulting from the reconstruction of nonrigid structures. To address this issue, we propose to identify the structures of interest prior to reconstruction using a deep learning-based semantic segmentation technique. RESULTS: Experimental validation using knee and hip phantoms, as well as ex-vivo data, assesses the laser scanner's effectiveness. The integration of the segmentation model improves results in ex-vivo experiments by mitigating outliers. Specifically, the laser scanner with the segmentation model achieves registration errors below 2.2 mm, with the intercondylar region exhibiting errors below 1 mm. In experiments with phantoms, the errors are always below 1 mm. CONCLUSION: The results show the viability of integrating the laser scanner with VBSN as a noninvasive and potential alternative to traditional methods by overcoming surface digitization challenges and expanding the reachable region. Future efforts aim to improve hardware to further optimize performance and applicability in complex procedures.

Localized Trajectories for 2D and 3D Action Recognition.

The Dense Trajectories concept is one of the most successful approaches in action recognition, suitable for scenarios involving a significant amount of motion. However, due to noise and background motion, many generated trajectories are irrelevant to the actual human activity and can potentially lead to performance degradation. In this paper, we propose Localized Trajectories as an improved version of Dense Trajectories where motion trajectories are clustered around human body joints provided by RGB-D cameras and then encoded by local Bag-of-Words. As a result, the Localized Trajectories concept provides an advanced discriminative representation of actions. Moreover, we generalize Localized Trajectories to 3D by using the depth modality. One of the main advantages of 3D Localized Trajectories is that they describe radial displacements that are perpendicular to the image plane. Extensive experiments and analysis were carried out on five different datasets.

Deep segmentation leverages geometric pose estimation in computer-aided total knee arthroplasty.

Knee arthritis is a common joint disease that usually requires a total knee arthroplasty. There are multiple surgical variables that have a direct impact on the correct positioning of the implants, and an optimal combination of all these variables is the most challenging aspect of the procedure. Usually, preoperative planning using a computed tomography scan or magnetic resonance imaging helps the surgeon in deciding the most suitable resections to be made. This work is a proof of concept for a navigation system that supports the surgeon in following a preoperative plan. Existing solutions require costly sensors and special markers, fixed to the bones using additional incisions, which can interfere with the normal surgical flow. In contrast, the authors propose a computer-aided system that uses consumer RGB and depth cameras and do not require additional markers or tools to be tracked. They combine a deep learning approach for segmenting the bone surface with a recent registration algorithm for computing the pose of the navigation sensor with respect to the preoperative 3D model. Experimental validation using ex-vivo data shows that the method enables contactless pose estimation of the navigation sensor with the preoperative model, providing valuable information for guiding the surgeon during the medical procedure.

Piecewise-Planar StereoScan: Sequential Structure and Motion Using Plane Primitives.

The article describes a pipeline that receives as input a sequence of stereo images, and outputs the camera motion and a Piecewise-Planar Reconstruction (PPR) of the scene. The pipeline, named Piecewise-Planar StereoScan (PPSS), works as follows: the planes in the scene are detected for each stereo view using semi-dense depth estimation; the relative pose is computed by a new closed-form minimal algorithm that only uses point correspondences whenever plane detections do not fully constrain the motion; the camera motion and the PPR are jointly refined by alternating between discrete optimization and continuous bundle adjustment; and, finally, the detected 3D planes are segmented in images using a new framework that handles low texture and visibility issues. PPSS is extensively validated in indoor and outdoor datasets, and benchmarked against two popular point-based SfM pipelines. The experiments confirm that plane-based visual odometry is resilient to situations of small image overlap, poor texture, specularity, and perceptual aliasing where the fast LIBVISO2 [1] pipeline fails. The comparison against VisualSfM+CMVS/PMVS [2] , [3] shows that, for a similar computational complexity, PPSS is more accurate and provides much more compelling and visually pleasant 3D models. These results strongly suggest that plane primitives are an advantageous alternative to point correspondences for applications of SfM and 3D reconstruction in man-made environments.

Proposta de um novo modelo experimental de indução de doença inflamatória intestinal / Proposal of a new experimental model of inflammatory bowel disease induction

OBJETIVO: Avaliar as lesões intestinais em ratos imunizados com proteínas intestinais murinas. MÉTODOS: Preparou-se suspensão a 40 por cento de cólon de ratos normais em PBS pH7,4, seguida de maceração, purificação, inativação e eletrofocalização de proteínas. Com esta suspensão 20 ratos Wistar foram imunizados conforme as seguintes fases: sensibilização via SC e IM com antígeno emulsificado em adjuvante completo de Freund. Reforços via IM com suspensão antigênica pura. Nesta fase foram pesquisados anticorpos séricos anti-tecido colônico por imunodifusão e face à positividade, dez ratos foram submetidos à avaliação histológica do cólon e em outros dez, inoculação via IP com suspensão antigênica pura. Após seis dias apresentaram: blefarite, diarréia, apatia, hematoquesia e então submetidos à coleta de amostras do cólon para avaliação histológica. RESULTADOS: A suspensão antigênica apresentou oito bandas de proteínas, entre 100 a 420 kD. Nas amostras de cólon observaram-se histologicamente perda de criptas, edema da camada sub-mucosa e inflamação aguda. CONCLUSAO: Foi possível reproduzir doença inflamatória intestinal em ratos a partir de imunização com antígenos protéicos intestinais da própria espécie. A presença de anticorpos séricos anti-intestino foi relacionada com as alterações histológicas encontradas no cólon de ratos imunizados.