Robust Path Planning via Learning from Demonstrations for Robotic Catheters in Deformable Environments.

OBJECTIVE: Navigation through tortuous and deformable vessels using catheters with limited steering capability underscores the need for reliable path planning. State-of-the-art path planners do not fully account for the deformable nature of the environment. METHODS: This work proposes a robust path planner via a learning from demonstrations method, named Curriculum Generative Adversarial Imitation Learning (C-GAIL). This path planning framework takes into account the interaction between steerable catheters and vessel walls and the deformable property of vessels. RESULTS: In-silico comparative experiments show that the proposed network achieves a 38% higher success rate in static environments and 17% higher in dynamic environments compared to a state-of-the-art approach based on GAIL. In-vitro validation experiments indicate that the path generated by the proposed C-GAIL path planner achieves a targeting error of 1.26 ±0.55mm and a tracking error of 5.18 ±3.48mm. These results represent improvements of 41% and 40% over the conventional centerline-following technique for targeting error and tracking error, respectively. CONCLUSION: The proposed C-GAIL path planner outperforms the state-of-the-art GAIL approach. The in-vitro validation experiments demonstrate that the path generated by the proposed C-GAIL path planner aligns better with the actual steering capability of the pneumatic artificial muscle-driven catheter utilized in this study. Therefore, the proposed approach can provide enhanced support to the user in navigating the catheter towards the target with greater accuracy, effectively meeting clinical accuracy requirements. SIGNIFICANCE: The proposed path planning framework exhibits superior performance in managing uncertainty associated with vessel deformation, thereby resulting in lower tracking errors.

Comparative Analysis of Interactive Modalities for Intuitive Endovascular Interventions.

Endovascular intervention is a minimally invasive method for treating cardiovascular diseases. Although fluoroscopy, known for real-time catheter visualization, is commonly used, it exposes patients and physicians to ionizing radiation and lacks depth perception due to its 2D nature. To address these limitations, a study was conducted using teleoperation and 3D visualization techniques. This in-vitro study involved the use of a robotic catheter system and aimed to evaluate user performance through both subjective and objective measures. The focus was on determining the most effective modes of interaction. Three interactive modes for guiding robotic catheters were compared in the study: 1) Mode GM, using a gamepad for control and a standard 2D monitor for visual feedback; 2) Mode GH, with a gamepad for control and HoloLens providing 3D visualization; and 3) Mode HH, where HoloLens serves as both control input and visualization device. Mode GH outperformed other modalities in subjective metrics, except for mental demand. It exhibited a median tracking error of 4.72 mm, a median targeting error of 1.01 mm, a median duration of 82.34 s, and a median natural logarithm of dimensionless squared jerk of 40.38 in the in-vitro study. Mode GH showed 8.5%, 4.7%, 6.5%, and 3.9% improvements over Mode GM and 1.5%, 33.6%, 34.9%, and 8.1% over Mode HH for tracking error, targeting error, duration, and dimensionless squared jerk, respectively. To sum up, the user study emphasizes the potential benefits of employing HoloLens for enhanced 3D visualization in catheterization. The user study also illustrates the advantages of using a gamepad for catheter teleoperation, including user-friendliness and passive haptic feedback, compared to HoloLens. To further gauge the potential of using a more traditional joystick as a control input device, an additional study utilizing the Haption VirtuoseTM robot was conducted. It reveals the potential for achieving smoother trajectories, with a 38.9% reduction in total path length compared to a gamepad, potentially due to its larger range of motion and single-handed control.

A new robot-based proprioceptive training algorithm to induce sensorimotor enhancement in the human wrist.

Afferent proprioceptive signals, responsible for body awareness, have a crucial role when planning and executing motor tasks. Increasing evidence suggests that proprioceptive sensory training may improve motor performance. Although this topic had been partially investigated, there was a lack of studies involving the wrist joint. Proprioception at the wrist level is particularly relevant to interact with the environment through actions that require an accurate sense of position and motion, and fine haptic perception. In this study, we implemented and tested a robotic training algorithm of human wrist proprioception. The proposed task was a continuous tracking in the workspace identified by flexion-extension and radial-ulnar deviation movements. Healthy subjects were haptically guided towards the target, without any visual feedback of the position of the end- effector. Our results showed that, after the training, participants improved their motor performance in a different tracking task, completely active and with visual feedback Additionally, the training led them to more efficient use of kinesthetic feedback during haptically-guided reaching tasks. Our findings demonstrated that the proposed training algorithm of wrist proprioception induced a task-specific sensorimotor enhancement. From the perspective of a rehabilitative intervention, this robot-based training has the potential to improve motor functions and the quality of life of subjects with sensorimotor deficits.

Surgical planning assistance in keyhole and percutaneous surgery: A systematic review.

Surgical planning of percutaneous interventions has a crucial role to guarantee the success of minimally invasive surgeries. In the last decades, many methods have been proposed to reduce clinician work load related to the planning phase and to augment the information used in the definition of the optimal trajectory. In this survey, we include 113 articles related to computer assisted planning (CAP) methods and validations obtained from a systematic search on three databases. First, a general formulation of the problem is presented, independently from the surgical field involved, and the key steps involved in the development of a CAP solution are detailed. Secondly, we categorized the articles based on the main surgical applications, which have been object of study and we categorize them based on the type of assistance provided to the end-user.

Towards realistic laparoscopic image generation using image-domain translation.

Background and ObjectivesOver the last decade, Deep Learning (DL) has revolutionized data analysis in many areas, including medical imaging. However, there is a bottleneck in the advancement of DL in the surgery field, which can be seen in a shortage of large-scale data, which in turn may be attributed to the lack of a structured and standardized methodology for storing and analyzing surgical images in clinical centres. Furthermore, accurate annotations manually added are expensive and time consuming. A great help can come from the synthesis of artificial images; in this context, in the latest years, the use of Generative Adversarial Neural Networks (GANs) achieved promising results in obtaining photo-realistic images. MethodsIn this study, a method for Minimally Invasive Surgery (MIS) image synthesis is proposed. To this aim, the generative adversarial network pix2pix is trained to generate paired annotated MIS images by transforming rough segmentation of surgical instruments and tissues into realistic images. An additional regularization term was added to the original optimization problem, in order to enhance realism of surgical tools with respect to the background. Results Quantitative and qualitative (i.e., human-based) evaluations of generated images have been carried out in order to assess the effectiveness of the method. ConclusionsExperimental results show that the proposed method is actually able to translate MIS segmentations to realistic MIS images, which can in turn be used to augment existing data sets and help at overcoming the lack of useful images; this allows physicians and algorithms to take advantage from new annotated instances for their training.

Acceleration-based Assistive Strategy to Control a Back-support Exoskeleton for Load Handling: Preliminary Evaluation.

Industrial active exoskeletons have recently achieved considerable interest, due to their intrinsic versatility compared to passive devices. To achieve this versatility, an important open challenge is the design of appropriate control strategies to automatically modulate the physical assistance according to the activity the user is performing.This work focuses on active back-support exoskeletons. To improve the assistance provided in dynamic situations with respect to state-of-the-art methods, a new strategy making use of the angular acceleration of the user's trunk is presented.The feasibility and effectiveness of the proposed strategy were tested experimentally on a prototype in a load handling task. The main advantages in terms of assistive torque profiles emerge during the transition phases of the movement (i.e. beginning and end of lowering and lifting) indicating an appropriate adaptation to the dynamics of the execution.In this preliminary evaluation, the data on peak muscular activity at the spine show promising trends, encouraging further developments and a more detailed evaluation.

Brain-vascular segmentation for SEEG planning via a 3D fully-convolutional neural network.

Three dimensional visualization of vascular structures can assist clinicians in preoperative planning, intra-operative guidance, and post-operative decision-making. The goal of this work is to provide an automatic, accurate and fast method for brain vessels segmentation in Contrast Enhanced Cone Beam Computed Tomography (CE-CBCT) dataset based on a residual Fully Convolutional Neural Network (FCNN). The proposed NN embeds in an encoder-decoder architecture residual elements which decreases the vanishing effect due to deep architecture while accelerating the convergence. Moreover, a two-stage training has been proposed as a countermeasure for the unbalanced nature of the dataset. The FCNN training was performed on 20 CE-CBCT volumes exploiting mini-batch gradient descent and the Adam optimizer. Binary cross-entropy was used as loss function. Performance evaluation was conducted considering 5 datasets. A median value of Dice, Precision and Recall of 0.79, 0.8 and 0.69 were obtained with respect to manual annotations.

Effective Diffusion and Tortuosity in Brain White Matter.

Patients affected by glioblastomas have a very low survival rate. Emerging techniques, such as convection enhanced delivery (CED), need complex numerical models to be effective; furthermore, the estimation of the main parameters to be used to instruct constitutive laws in simulations represents a major challenge. This work proposes a new method to compute tortuosity, a key parameter for drug diffusion in fibrous tissue, starting from a model which incorporates the main white matter geometrical features. It is shown that tortuosity increases from 1.35 to 1.85 as the extracellular space width decreases. The results are in good agreement with experimental data reported in the literature.

Learning-based classification of informative laryngoscopic frames.

BACKGROUND AND OBJECTIVE: Early-stage diagnosis of laryngeal cancer is of primary importance to reduce patient morbidity. Narrow-band imaging (NBI) endoscopy is commonly used for screening purposes, reducing the risks linked to a biopsy but at the cost of some drawbacks, such as large amount of data to review to make the diagnosis. The purpose of this paper is to present a strategy to perform automatic selection of informative endoscopic video frames, which can reduce the amount of data to process and potentially increase diagnosis performance. METHODS: A new method to classify NBI endoscopic frames based on intensity, keypoint and image spatial content features is proposed. Support vector machines with the radial basis function and the one-versus-one scheme are used to classify frames as informative, blurred, with saliva or specular reflections, or underexposed. RESULTS: When tested on a balanced set of 720 images from 18 different laryngoscopic videos, a classification recall of 91% was achieved for informative frames, significantly overcoming three state of the art methods (Wilcoxon rank-signed test, significance level = 0.05). CONCLUSIONS: Due to the high performance in identifying informative frames, the approach is a valuable tool to perform informative frame selection, which can be potentially applied in different fields, such us computer-assisted diagnosis and endoscopic view expansion.

Modular multiple sensors information management for computer-integrated surgery.

BACKGROUND: In the past 20 years, technological advancements have modified the concept of modern operating rooms (ORs) with the introduction of computer-integrated surgery (CIS) systems, which promise to enhance the outcomes, safety and standardization of surgical procedures. With CIS, different types of sensor (mainly position-sensing devices, force sensors and intra-operative imaging devices) are widely used. Recently, the need for a combined use of different sensors raised issues related to synchronization and spatial consistency of data from different sources of information. METHODS: In this study, we propose a centralized, multi-sensor management software architecture for a distributed CIS system, which addresses sensor information consistency in both space and time. The software was developed as a data server module in a client-server architecture, using two open-source software libraries: Image-Guided Surgery Toolkit (IGSTK) and OpenCV. The ROBOCAST project (FP7 ICT 215190), which aims at integrating robotic and navigation devices and technologies in order to improve the outcome of the surgical intervention, was used as the benchmark. An experimental protocol was designed in order to prove the feasibility of a centralized module for data acquisition and to test the application latency when dealing with optical and electromagnetic tracking systems and ultrasound (US) imaging devices. RESULTS: Our results show that a centralized approach is suitable for minimizing synchronization errors; latency in the client-server communication was estimated to be 2 ms (median value) for tracking systems and 40 ms (median value) for US images. CONCLUSION: The proposed centralized approach proved to be adequate for neurosurgery requirements. Latency introduced by the proposed architecture does not affect tracking system performance in terms of frame rate and limits US images frame rate at 25 fps, which is acceptable for providing visual feedback to the surgeon in the OR.