MIS-SLAM: Real-Time Large-Scale Dense Deformable SLAM System in Minimal Invasive Surgery Based on Heterogeneous Computing

© 2016 IEEE. Real-time simultaneous localization and dense mapping is very helpful for providing virtual reality and augmented reality for surgeons or even surgical robots. In this letter, we propose MIS-SLAM: A complete real-time large-scale dense deformable SLAM system with stereoscope in minimal invasive surgery (MIS) based on heterogeneous computing by making full use of CPU and GPU. Idled CPU is used to perform ORB-SLAM for providing robust global pose. Strategies are taken to integrate modules from CPU and GPU. We solved the key problem raised in the previous work, that is, fast movement of scope and blurry images make the scope tracking fail. Benefiting from improved localization, MIS-SLAM can achieve large-scale scope localizing and dense mapping in real time. It transforms and deforms current model and incrementally fuses new observation while keeping vivid texture. In-vivo experiments conducted on publicly available datasets presented in the form of videos demonstrate the feasibility and practicality of MIS-SLAM for potential clinical purpose

EVALUACIÓN METROLOGICA DE LA ESTRUCTURA OPTITRACK-MOTIVE PARA EL RASTREO DE OBJETOS EN EL ESPACIO QUIRÚRGICO APLICADO EN PERFORACIÓN DE TUNELES TIBIAL Y FEMORAL EN RECONSTRUCCIÓN DE LIGAMENTO CRUZADO ANTERIOR DE RODILLA

Un alto porcentaje de cirugías de Reconstrucción de Ligamento Cruzado Anterior son fallidas debido al mal posicionamiento de los túneles tibial y femoral. Para esto se diseñó un sistema, basado en la estructura Optitrack-Motive, que, usando Realidad Aumentada, apoya el proceso de perforación de los túneles realizando el seguimiento de marcadores pasivos por visión estéreo para ubicar el instrumental en el campo quirúrgico. Sin embargo, en la literatura no existe información acerca de la precisión en el posicionamiento del sistema Optitrack-Motive. Debido a esto se hizo necesario realizar este estudio para verificar la precisión requerida para esta aplicación quirúrgica. Se posicionó una barra de calibración lineal en el volumen de trabajo y se midieron distancias a diferentes planos de observación con dos sistemas de exploración 3D: Motive-Tracker y un software construido en C++. El análisis de error sugiere que las fuentes aleatorias introducen una desviación estándar que no supera 39 micras para ambos casos, siendo más pequeña en la zona central para el software C++. El análisis del sesgo depende de las posiciones de las esferas en cada cámara. Para el software C++ la zona central es más corregida de la influencia de distorsiones geométricas, introduciéndose menos error en el cálculo de distancias: 22 micras para medir 87.5 mm. Para el software Motive- Tracker el error determinístico es más pequeño y homogéneo en todo el espacio de calibración: Medir una distancia de 250 mm se introduce un error absoluto máximo de 0.343 mm, siendo mínimo de 0.059 mm para Z=90 cm

Simultaneous Tracking, 3D Reconstruction and Deforming Point Detection for Stereoscope Guided Surgery

Tissue deformation is one of the major difficulties in the registration of pre-operative and intra-operative data. Vision based techniques have shown the potential to simultaneously track the endoscope and recover a sparse 3D structure of the tissue. However, most of such methods either assume a static environment or require the tissue organ to have a periodic motion such as respiration. To deal with the general tissue deformation, a new framework is proposed in this paper with the ability of simultaneous stereoscope tracking, 3D reconstruction and deforming point detection in the Minimally Invasive Surgery (MIS) environment. First, we adopt a Parallel Tracking and Mapping (PTAM) framework and extend it for the use of stereoscope in MIS. Second, this newly extended framework enables the detection of deforming points without restricted periodic motion model assumptions. Our proposed method has been evaluated on a phantom model, and in vivo experiments demonstrate its capability for accurate tracking in nearly real time speed as well as 3D reconstruction with hundreds of 3D points. Those experiments have shown that our method is robust towards tissue deformation and hence have promising potential for information integration by registration with pre-operative data