Few-Human-Interaction Reinforcement Learning for Autonomous Transbronchial Intervention.

The transbronchial interventional surgery presents challenges with winding and convoluted pathways, prone to compression and friction. Current autonomous planning struggles to reach deeper bronchial positions, and hard to consider multiple conflicting goals simultaneously. This article introduces an innovative planning scheme with preference weights to achieve smooth, frictionless, and collision-free autonomous transbronchial intervention with continuum robot (CR).

Motion Decoupling Network for Intra-Operative Motion Estimation Under Occlusion.

In recent intelligent-robot-assisted surgery studies, an urgent issue is how to detect the motion of instruments and soft tissue accurately from intra-operative images. Although optical flow technology from computer vision is a powerful solution to the motion-tracking problem, it has difficulty obtaining the pixel-wise optical flow ground truth of real surgery videos for supervised learning. Thus, unsupervised learning methods are critical.

Semi-Supervised Medical Image Segmentation Guided by Bi-Directional Constrained Dual-Task Consistency.

Background: Medical image processing tasks represented by multi-object segmentation are of great significance for surgical planning, robot-assisted surgery, and surgical safety. However, the exceptionally low contrast among tissues and limited available annotated data makes developing an automatic segmentation algorithm for pelvic CT challenging.

Methods: A bi-direction constrained dual-task consistency model named PICT is proposed to improve segmentation quality by leveraging free unlabeled data.

Fast instruments and tissues segmentation of micro-neurosurgical scene using high correlative non-local network.

Surgical scene segmentation provides critical information for guidance in micro-neurosurgery. Segmentation of instruments and critical tissues contributes further to robot assisted surgery and surgical evaluation. However, due to the lack of relevant scene segmentation dataset, scale variation and local similarity, micro-neurosurgical segmentation faces many challenges.

Autonomous path planning for robot-assisted pelvic fracture closed reduction with collision avoidance.

Background: Robot-assisted pelvic fracture closed reduction (RPFCR) positively contributes to patient treatment. However, the current path planning suffers from incomplete obstacle avoidance and long paths.

Method: A collision detection method is proposed for applications in the pelvic environment to improve the safety of RPFCR surgery.

Spatio-Temporal Causal Transformer for Multi-Grained Surgical Phase Recognition.

Automatic surgical phase recognition plays a key role in surgical workflow analysis and overall optimization in clinical work. In the complicated surgical procedures, similar inter-class appearance and drastic variability in phase duration make this still a challenging task. In this paper, a spatio-temporal transformer is proposed for online surgical phase recognition with different granularity.

An automatic drug injection device with spatial micro-force perception guided by an microscopic image for robot-assisted ophthalmic surgery.

Retinal vein injection guided by microscopic image is an innovative procedure for treating retinal vein occlusion. However, the retina organization is complex, fine, and weak, and the operation scale and force are small. Surgeons' limited operation and force-sensing accuracy make it difficult to perform precise and stable drug injection operations on the retina in a magnified field of image vision.

Space Squeeze Reasoning and Low-Rank Bilinear Feature Fusion for Surgical Image Segmentation.

Surgical image segmentation is critical for surgical robot control and computer-assisted surgery. In the surgical scene, the local features of objects are highly similar, and the illumination interference is strong, which makes surgical image segmentation challenging. To address the above issues, a bilinear squeeze reasoning network is proposed for surgical image segmentation.

SurgiNet: Pyramid Attention Aggregation and Class-wise Self-Distillation for Surgical Instrument Segmentation.

Surgical instrument segmentation plays a promising role in robot-assisted surgery. However, illumination issues often appear in surgical scenes, altering the color and texture of surgical instruments. Changes in visual features make surgical instrument segmentation difficult.

A Unified Framework for Multi-Guidewire Endpoint Localization in Fluoroscopy Images.

Objective: In this paper, Keypoint Localization Region-based CNN (KL R-CNN) is proposed, which can simultaneously accomplish the guidewire detection and endpoint localization in a unified model.

Methods: KL R-CNN modifies Mask R-CNN by replacing the mask branch with a novel keypoint localization branch. Besides, some settings of Mask R-CNN are also modified to generate the keypoint localization results at a higher detail level.

Real-Time Multi-Guidewire Endpoint Localization in Fluoroscopy Images.

The real-time localization of the guidewire endpoints is a stepping stone to computer-assisted percutaneous coronary intervention (PCI). However, methods for multi-guidewire endpoint localization in fluoroscopy images are still scarce. In this paper, we introduce a framework for real-time multi-guidewire endpoint localization in fluoroscopy images.

Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge.

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g.

Optical coherence tomography-based deep learning algorithm for quantification of the location of the intraocular lens.

Background: Cataract surgery has been recently developed from sight rehabilitating surgery to accurate refractive surgery. The precise concentration of intraocular lens (IOL) is crucial for postoperative high visual quanlity. The three-dimentional (3D) images of ocular anterior segment captured by optial coherence tomography (OCT) make it possible to evaluate the IOL position in 3D space, which provide insights into factors relavant to the visual quanlity and better design of new functional IOL.

A Multilayer and Multimodal-Fusion Architecture for Simultaneous Recognition of Endovascular Manipulations and Assessment of Technical Skills.

The clinical success of the percutaneous coronary intervention (PCI) is highly dependent on endovascular manipulation skills and dexterous manipulation strategies of interventionalists. However, the analysis of endovascular manipulations and related discussion for technical skill assessment are limited. In this study, a multilayer and multimodal-fusion architecture is proposed to recognize six typical endovascular manipulations.

Pyramid attention recurrent networks for real-time guidewire segmentation and tracking in intraoperative X-ray fluoroscopy.

In endovascular and cardiovascular surgery, real-time and accurate segmentation and tracking of interventional instruments can aid in reducing radiation exposure, contrast agent and processing time. Nevertheless, this task often comes with the challenges of the elongated deformable structures with low contrast in noisy X-ray fluoroscopy. To address these issues, a novel efficient network architecture, termed pyramid attention recurrent networks (PAR-Net), is proposed for real-time guidewire segmentation and tracking.

An intelligent learning approach for improving ECG signal classification and arrhythmia analysis.

The recognition of cardiac arrhythmia in minimal time is important to prevent sudden and untimely deaths. The proposed work includes a complete framework for analyzing the Electrocardiogram (ECG) signal. The three phases of analysis include 1) the ECG signal quality enhancement through noise suppression by a dedicated filter combination; 2) the feature extraction by a devoted wavelet design and 3) a proposed hidden Markov model (HMM) for cardiac arrhythmia classification into Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Premature Ventricular Contraction (PVC) and Atrial Premature Contraction (APC).

Recognition of Endovascular Manipulations using Recurrent Neural Networks.

The ability to accurately recognize elementary surgical gestures is a stepping stone to automated surgical assessment and surgical training. In this paper, a long short-term memory (LSTM) recurrent neural network is applied to the task of recognizing six typical manipulations in percutaneous coronary intervention (PCI). The manipulation mentioned above is referring to the atomic surgical operation, also called surgeme in many research.

RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network.

Segmentation for tracking surgical instruments plays an important role in robot-assisted surgery. Segmentation of surgical instruments contributes to capturing accurate spatial information for tracking. In this paper, a novel network, Refined Attention Segmentation Network, is proposed to simultaneously segment surgical instruments and identify their categories.

Qualitative and Quantitative Assessment of Technical Skills in Percutaneous Coronary Intervention: In Vivo Porcine Studies.

Objective: Technical skill assessment plays an important role in the professional development of an interventionalist in percutaneous coronary intervention (PCI). However, most of the traditional assessment methods are time consuming and subjective. This paper aims to develop objective assessment techniques.

Removal of Artifacts from EEG Signals: A Review.

Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings.

Analysis of Interventionalists' Natural Behaviors for Recognizing Motion Patterns of Endovascular Tools During Percutaneous Coronary Interventions.

Many robotic platforms can indeed reduce radiation exposure to clinicians during percutaneous coronary intervention (PCI), however, interventionalists' natural manipulations are rarely involved in robot-assisted PCI. This requires more attention to analyze interventionalists' natural behaviors during conventional PCI. In this study, four types of natural behavior (i.

Simultaneous Recognition and Assessment of Post-Stroke Hemiparetic Gait by Fusing Kinematic, Kinetic, and Electrophysiological Data.

Gait analysis for the patients with lower limb motor dysfunction is a useful tool in assisting clinicians for diagnosis, assessment, and rehabilitation strategy making. Implementing accurate automatic gait analysis for the hemiparetic patients after stroke is a great challenge in clinical practice. This study is to develop a new automatic gait analysis system for qualitatively recognizing and quantitatively assessing the gait abnormality of the post-stroke hemiparetic patients.

An HMM-based recognition framework for endovascular manipulations.

Robotic surgical systems are becoming increasingly popular for the treatment of cardiovascular diseases. However, most of them have been designed without considering techniques and skills of natural surgical manipulations, which are key factors to clinical success of percutaneous coronary intervention. This paper proposes an HMM-based framework to recognize six typical endovascular manipulations for surgical skill analysis.

A Multimodal Framework Based on Integration of Cortical and Muscular Activities for Decoding Human Intentions About Lower Limb Motions.

In this study, a multimodal fusion framework based on three different modal biosignals is developed to recognize human intentions related to lower limb multi-joint motions which commonly appear in daily life. Electroencephalogram (EEG), electromyogram (EMG) and mechanomyogram (MMG) signals were simultaneously recorded from twelve subjects while performing nine lower limb multi-joint motions. These multimodal data are used as the inputs of the fusion framework for identification of different motion intentions.