Smoke veil prior regularized surgical field desmoking without paired in-vivo data.

Though deep learning-based surgical smoke removal methods have shown significant improvements in effectiveness and efficiency, the lack of paired smoke and smoke-free images in real surgical scenarios limits the performance of these methods. Therefore, methods that can achieve good generalization performance without paired in-vivo data are in high demand. In this work, we propose a smoke veil prior regularized two-stage smoke removal framework based on the physical model of smoke image formation. More precisely, in the first stage, we leverage a reconstruction loss, a consistency loss and a smoke veil prior-based regularization term to perform fully supervised training on a synthetic paired image dataset. Then a self-supervised training stage is deployed on the real smoke images, where only the consistency loss and the smoke veil prior-based loss are minimized. Experiments show that the proposed method outperforms the state-of-the-art ones on synthetic dataset. The average PSNR, SSIM and RMSE values are 21.99±2.34, 0.9001±0.0252 and 0.2151±0.0643, respectively. The qualitative visual inspection on real dataset further demonstrates the effectiveness of the proposed method.

A neural network based framework for effective laparoscopic video quality assessment.

Video quality assessment is a challenging problem having a critical significance in the context of medical imaging. For instance, in laparoscopic surgery, the acquired video data suffers from different kinds of distortion that not only hinder surgery performance but also affect the execution of subsequent tasks in surgical navigation and robotic surgeries. For this reason, we propose in this paper neural network-based approaches for distortion classification as well as quality prediction. More precisely, a Residual Network (ResNet) based approach is firstly developed for simultaneous ranking and classification task. Then, this architecture is extended to make it appropriate for the quality prediction task by using an additional Fully Connected Neural Network (FCNN). To train the overall architecture (ResNet and FCNN models), transfer learning and end-to-end learning approaches are investigated. Experimental results, carried out on a new laparoscopic video quality database, have shown the efficiency of the proposed methods compared to recent conventional and deep learning based approaches.

SmartSIM - a virtual reality simulator for laparoscopy training using a generic physics engine.

Virtual reality (VR) training simulators have started playing a vital role in enhancing surgical skills, such as hand-eye coordination in laparoscopy, and practicing surgical scenarios that cannot be easily created using physical models. We describe a new VR simulator for basic training in laparoscopy, i.e. SmartSIM, which has been developed using a generic open-source physics engine called the simulation open framework architecture (SOFA). This paper describes the systems perspective of SmartSIM including design details of both hardware and software components, while highlighting the critical design decisions. Some of the distinguishing features of SmartSIM include: (i) an easy-to-fabricate custom-built hardware interface; (ii) use of a generic physics engine to facilitate wider accessibility of our work and flexibility in terms of using various graphical modelling algorithms and their implementations; and (iii) an intelligent and smart evaluation mechanism that facilitates unsupervised and independent learning.