Learning Skill Characteristics From Manipulations.

Percutaneous coronary intervention (PCI) has increasingly become the main treatment for coronary artery disease. The procedure requires high experienced skills and dexterous manipulations. However, there are few techniques to model PCI skill so far. In this study, a learning framework with local and ensemble learning is proposed to learn skill characteristics of different skill-level subjects from their PCI manipulations. Ten interventional cardiologists (four experts and six novices) were recruited to deliver a medical guidewire to two target arteries on a porcine model for in vivo studies. Simultaneously, translation and twist manipulations of thumb, forefinger, and wrist are acquired with electromagnetic (EM) and fiber-optic bend (FOB) sensors, respectively. These behavior data are then processed with wavelet packet decomposition (WPD) under 1-10 levels for feature extraction. The feature vectors are further fed into three candidate individual classifiers in the local learning layer. Furthermore, the local learning results from different manipulation behaviors are fused in the ensemble learning layer with three rule-based ensemble learning algorithms. In subject-dependent skill characteristics learning, the ensemble learning can achieve 100% accuracy, significantly outperforming the best local result (90%). Furthermore, ensemble learning can also maintain 73% accuracy in subject-independent schemes. These promising results demonstrate the great potential of the proposed method to facilitate skill learning in surgical robotics and skill assessment in clinical practice.

Long non-coding RNA VPS9D1-AS1 enhances proliferation, invasion, and epithelial-mesenchymal transition in endometrial cancer via miR-377-3p/SGK1.

Endometrial cancer (EC) is a kind of gynecologic malignancy with a rising incidence rate. This study aimed to explore the role of VPS9D1 antisense RNA1 (VPS9D1-AS1) in EC. The expression of VPS9D1-AS1, microRNA (miR)-377-3p, and serum and glucocorticoid-regulated kinase 1 (SGK1) was detected by Quantitative Real-Time PCR (qRT-PCR). Cell proliferation, invasion and epithelial-mesenchymal transition (EMT) were determined by cell counting kit-8 (CCK-8), 5-Ethynyl-2'-Deoxyuridine (EdU) transwell, and western bolt. VPS9D1-AS1 was predicted to sponge miR-377-3p via Starbase, and verified by luciferase reporter, RNA binding protein immunoprecipitation (RIP), and RNA pull-down experiments. The clinical characteristics of VPS9D1-AS1, miR-377-3p, and SGK1 were analyzed. The role of VPS9D1-AS1 on EC tumorigenesis was assessed in xenografted nude mice. VPS9D1-AS1 was upregulated in EC cells and tissues. Interference of VPS9D1-AS1 inhibited growth, invasion, and EMT of EC cells. Mechanically, VPS9D1-AS1 was a molecular sponge of miR-377-3p, and overexpression of miR-377-3p reversed VPS9D1-AS1-induced EC cells proliferation, invasion, and EMT. Moreover, SGK1 was confirmed to bind with miR-377-3p. Furthermore, overexpression of SGK1 alleviated sh-VPS9D1-AS1-caused effects on EC cells. High level of VPS9D1-AS1 and SGK1, or low miR-377-3p expression predicted a poor prognosis. The expression of the three genes was correlated with lymph node metastasis, pathological stage, and International Federation of Gynecology and Obstetrics (FIGO) stage, but not associated with age, ER, and PR expression. Interestingly, knockdown of VPS9D1-AS1 suppressed EC tumor growth in mice. VPS9D1-AS1 promoted cell invasion, proliferation, and EMT via modulating miR-377-3p/SGK1 axis, which provided new options for therapeutic strategies of EC.

TR-GAN: Multi-Session Future MRI Prediction With Temporal Recurrent Generative Adversarial Network.

Magnetic Resonance Imaging (MRI) has been proven to be an efficient way to diagnose Alzheimer's disease (AD). Recent dramatic progress on deep learning greatly promotes the MRI analysis based on data-driven CNN methods using a large-scale longitudinal MRI dataset. However, most of the existing MRI datasets are fragmented due to unexpected quits of volunteers. To tackle this problem, we propose a novel Temporal Recurrent Generative Adversarial Network (TR-GAN) to complete missing sessions of MRI datasets. Unlike existing GAN-based methods, which either fail to generate future sessions or only generate fixed-length sessions, TR-GAN takes all past sessions to recurrently and smoothly generate future ones with variant length. Specifically, TR-GAN adopts recurrent connection to deal with variant input sequence length and flexibly generate future variant sessions. Besides, we also design a multiple scale & location (MSL) module and a SWAP module to encourage the model to better focus on detailed information, which helps to generate high-quality MRI data. Compared with other popular GAN architectures, TR-GAN achieved the best performance in all evaluation metrics of two datasets. After expanding the Whole MRI dataset, the balanced accuracy of AD vs. cognitively normal (CN) vs. mild cognitive impairment (MCI) and stable MCI vs. progressive MCI classification can be increased by 3.61% and 4.00%, respectively.

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. The synchronously acquired multimodal motion signals from ten subjects are used as the inputs of the architecture independently. Six classification-based and two rule-based fusion algorithms are evaluated for performance comparisons. The recognition metrics under the determined architecture are further used to assess technical skills. The experimental results indicate that the proposed architecture can achieve the overall accuracy of 96.41%, much higher than that of a single-layer recognition architecture (92.85%). In addition, the multimodal fusion brings significant performance improvement in comparison with single-modal schemes. Furthermore, the K -means-based skill assessment can obtain an accuracy of 95% to cluster the attempts made by different skill-level groups. These hopeful results indicate the great possibility of the architecture to facilitate clinical skill assessment and skill learning.

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. At the same time, based on the existing metrics of Average Precision (AP) and Percentage of Correct Keypoints (PCK), a new metric named APPCK is proposed to evaluate the overall performance on the multi-guidewire endpoint localization task. Compared with existing metrics, APPCK is easy to use and its results are more intuitive. RESULTS: Compared with existing methods, KL R-CNN has better performance when the threshold is loose, reaching a mean APPCK of 90.65% when the threshold is 9 pixels. CONCLUSION: KL R-CNN achieves the state-of-the-art performance on the multi-guidewire endpoint localization task and has application potentials. SIGNIFICANCE: KL R-CNN can achieve the localization of guidewire endpoints in fluoroscopy images, which is a prerequisite for computer-assisted percutaneous coronary intervention. KL R-CNN can also be extended to other multi-instrument localization tasks.

An Improved Networked Predictive Controller for Vascular Robot Using 5G Networks.

Percutaneous coronary intervention (PCI) has gradually become the most common treatment of coronary artery disease (CAD) in clinical practice due to its advantages of small trauma and quick recovery. However, the availability of hospitals with cardiac catheterization facilities and trained interventionalists is extremely limited in remote and underdeveloped areas. Remote vascular robotic system can assist interventionalists to complete operations precisely, and reduce occupational health hazards occurrence. In this paper, a bionic remote vascular robot is introduced in detail from three parts: mechanism, communication architecture, and controller model. Firstly, human finger-like mechanisms in vascular robot enable the interventionalists to advance, retract and rotate the guidewires or balloons. Secondly, a 5G-based communication system is built to satisfy the end-to-end requirements of strong data transmission and packet priority setting in remote robot control. Thirdly, a generalized predictive controller (GPC) is developed to suppress the effect of time-varying network delay and parameter identification error, while adding a designed polynomial compensation module to reduce tracking error and improve system responsiveness. Then, the simulation experiment verifies the system performance in comparison with different algorithms, network delay, and packet loss rate. Finally, the improved control system conducted PCI on an experimental pig, which reduced the delivery integral absolute error (IAE) by at least 20% compared with traditional methods.

Design and Performance Evaluation of a Novel Vascular Robotic System for Complex Percutaneous Coronary Interventions.

The robotic-assisted percutaneous coronary intervention is an emerging technology with great potential to solve the shortcomings of existing treatments. However, the current robotic systems can not manipulate two guidewires or ballons/stents simultaneously for coronary bifurcation lesions. This paper presents VasCure, a novel bio-inspired vascular robotic system, to deliver two guidewires and stents into the main branch and side branch of bifurcation lesions in sequence. The system is designed in master-slave architecture to reduce occupational hazards of radiation exposure and orthopedic injury to interventional surgeons. The slave delivery device has one active roller and two passive rollers to manipulate two interventional devices. The performance of the VasCure was verified by in vitro and in vivo animal experiments. In vitro results showed the robotic system has good accuracy to deliver guidewires and the maximum error is 0.38mm. In an animal experiment, the interventional surgeon delivered two guidewires and balloons to the left circumflex branch and the left anterior descending branch of the pig, which confirmed the feasibility of the vascular robotic system.

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. The framework consists of two stages, first detecting all guidewire instances in the fluoroscopy image, and then locating the endpoints of each single guidewire instance. In the first stage, a YOLOv3 detector is used for guidewire detection, and a post-processing algorithm is proposed to refine the guidewire detection results. In the second stage, a Segmentation Attention-hourglass (SA-hourglass) network is proposed to predict the endpoint locations of each single guidewire instance. The SA-hourglass network can be generalized to the keypoint localization of other surgical instruments. In our experiments, the SA-hourglass network is applied not only on a guidewire dataset but also on a retinal microsurgery dataset, reaching the mean pixel error (MPE) of 2.20 pixels on the guidewire dataset and the MPE of 5.30 pixels on the retinal microsurgery dataset, both achieving the state-of-the-art localization results. Besides, the inference rate of our framework is at least 20FPS, which meets the real-time requirement of fluoroscopy images (6-12FPS).

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. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).

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. The proposed PAR-Net contains three major modules, namely pyramid attention module, recurrent residual module and pre-trained MobileNetV2 encoder. Specifically, a hybrid loss function of both reinforced focal loss and dice loss is proposed to better address the issues of class imbalance and misclassified examples. Quantitative and qualitative evaluations on clinical intraoperative images demonstrate that the proposed approach significantly outperforms simpler baselines as well as the best previously published result for this task, achieving the state-of-the-art performance.

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. METHODS: In this study, a natural-behavior-based assessment framework is proposed to qualitatively and quantitatively assess technical skills in PCI. In vivo porcine studies were conducted to deliver a medical guidewire to two target coronaries of left circumflex arteries by six novice and four expert interventionalists. Simultaneously, four types of natural behaviors (i.e., hand motion, proximal force, muscle activity, and finger motion) were acquired from the subjects' dominant hand and arm. The features extracted from the behaviors of different skill-level groups were compared using the Mann-Whitney U-test for effective behavior selection. The effective ones were further applied in the Gaussian-mixture-model-based qualitative assessment and Mahalanobis-distance-based quantitative assessment. RESULTS: The qualitative assessment achieves an accuracy of 92% to distinguish the novice and expert attempts, which is significantly higher than that of using single guidewire motions. Furthermore, the quantitative assessment can assign objective and effective scores for all attempts, indicating high correlation ( R = 0.9225) to those obtained by traditional methods. CONCLUSION: The objective, effective, and comprehensive assessment of technical skills can be provided by qualitatively and quantitatively analyzing interventionalists' natural behaviors in PCI. SIGNIFICANCE: This paper suggests a novel approach for the technical skill assessment and the promising results demonstrate the great importance and effectiveness of the proposed method for promoting the development of objective assessment techniques.

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. The U-shape network which is popular in segmentation is used. Different from previous work, an attention module is adopted to help the network focus on key regions, which can improve the segmentation accuracy. To solve the class imbalance problem, the weighted sum of the cross entropy loss and the logarithm of the Jaccard index is used as loss function. Furthermore, transfer learning is adopted in our network. The encoder is pre-trained on ImageNet. The dataset from the MICCAI EndoVis Challenge 2017 is used to evaluate our network. Based on this dataset, our network achieves state-of-the-art performance 94.65% mean Dice and 90.33% mean IOU.

Alternative splicing results in a lack of starch synthase IIa-D in Chinese wheat landrace.

We evaluated the SGP-1 protein composition of 368 Chinese wheat landraces using SDS-PAGE. The SGP-D1 null type was identified in three accessions (Xiaoqingmang, Pushanbamai, and P119). An 18-bp deletion and 9-bp variation were found at the junction region of the 7th intron and 8th exon, leading to deletion of the intron-exon junction recognition site AG when aligned the 8261-bp DNA sequence of TaSSIIa-D in Pushanbamai with that of Chinese Spring. Four cDNA types with mis-spliced isoforms were subsequently detected through amplification of TaSSIIa-D cDNAs. Among these, nine type II cDNAs with a 16-bp deletion in the 8th exon were detected, indicating that the major transcriptional pattern of TaSSIIa in Pushanbamai is type II. In the type IV cDNA, a 97-bp sequence remains undeleted in the end of the 5th exon. The amylose content in Pushanbamai was significantly higher than that in all control lines under field conditions, which suggested that deletion of SGP-D1 has an efficient impact on amylose content. As the TaSSIIa gene plays an important role in regulating the content of amylose, it is anticipated that these natural variants of TaSSIIa-D will provide useful resources for quality improvement in wheat.

Biodegradability of chlorinated anilines in waters.

OBJECTIVE: To identify the bacteria tolerating chlorinated anilines and to study the biodegradability of o-chloroaniline and its coexistent compounds. METHODS: Microbial community of complex bacteria was identified by plate culture observation techniques and Gram stain method. Bacterial growth inhibition test was used to determine t he tolerance of complex bacteria to toxicant. Biodegradability of chlorinated anilines was determined using domesticated complex bacteria as an inoculum by shaking-flask test. RESULTS: The complex bacteria were identified, consisting of Xanthomonas, Bacillus alcaligenes, Acinetobacter Pseudomonas, and Actinomycetaceae nocardia. The obtained complex bacteria were more tolerant to o-chloroaniline than mixture bacteria in natural river waters. The effects of exposure concentration and inoculum size on the biodegradability of o-chloroaniline were analyzed, and the biodegradation characteristics of single o-chloroaniline and 2, 4-dichloroaniline were compared with the coexistent compounds. CONCLUSION: The biodegradation rates can be improved by decreasing concentration of compounds and increasing inoculum size of complex bacteria. When o-chloroaniline coexists with aniline, the latter is biodegraded prior to the former, and as a consequence the metabolic efficiency of o-chloroaniline is improved with the increase of aniline concentration. Meanwhile, when o-chloroaniline coexists with 2,4-dichloroaniline, the metabolic efficiency of 2,4-dichloroaniline is markedly improved.