170 research outputs found
ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools
Real-time tool segmentation from endoscopic videos is an essential part of
many computer-assisted robotic surgical systems and of critical importance in
robotic surgical data science. We propose two novel deep learning architectures
for automatic segmentation of non-rigid surgical instruments. Both methods take
advantage of automated deep-learning-based multi-scale feature extraction while
trying to maintain an accurate segmentation quality at all resolutions. The two
proposed methods encode the multi-scale constraint inside the network
architecture. The first proposed architecture enforces it by cascaded
aggregation of predictions and the second proposed network does it by means of
a holistically-nested architecture where the loss at each scale is taken into
account for the optimization process. As the proposed methods are for real-time
semantic labeling, both present a reduced number of parameters. We propose the
use of parametric rectified linear units for semantic labeling in these small
architectures to increase the regularization ability of the design and maintain
the segmentation accuracy without overfitting the training sets. We compare the
proposed architectures against state-of-the-art fully convolutional networks.
We validate our methods using existing benchmark datasets, including ex vivo
cases with phantom tissue and different robotic surgical instruments present in
the scene. Our results show a statistically significant improved Dice
Similarity Coefficient over previous instrument segmentation methods. We
analyze our design choices and discuss the key drivers for improving accuracy.Comment: Paper accepted at IROS 201
State-of-the-Art of Non-Radiative, Non-Visual Spine Sensing with a Focus on Sensing Forces, Vibrations and Bioelectrical Properties: A Systematic Review
In the research field of robotic spine surgery, there is a big upcoming momentum for surgeon-like autonomous behaviour and surgical accuracy in robotics which goes beyond the standard engineering notions such as geometric precision. The objective of this review is to present an overview of the state of the art in non-visual, non-radiative spine sensing for the enhancement of surgical techniques in robotic automation. It provides a vantage point that facilitates experimentation and guides new research projects to what has not been investigated or integrated in surgical robotics. Studies were identified, selected and processed according to the PRISMA guidelines. Relevant study characteristics that were searched for include the sensor type and measured feature, the surgical action, the tested sample, the method for data analysis and the system's accuracy of state identification. The 6DOF f/t sensor, the microphone and the electromyography probe were the most commonly used sensors in each category, respectively. The performance of the electromyography probe is unsatisfactory in terms of preventing nerve damage as it can only signal after the nerve is disturbed. Feature thresholding and artificial neural networks were the most common decision algorithms for state identification. The fusion of different sensor data in the decision algorithm improved the accuracy of state identification
Physiological Motion Compensation for Neuroscience Research based on Electrical Bio-Impedance Sensing
Deep Sequential Mosaicking of Fetoscopic Videos
Twin-to-twin transfusion syndrome treatment requires fetoscopic laser
photocoagulation of placental vascular anastomoses to regulate blood flow to
both fetuses. Limited field-of-view (FoV) and low visual quality during
fetoscopy make it challenging to identify all vascular connections. Mosaicking
can align multiple overlapping images to generate an image with increased FoV,
however, existing techniques apply poorly to fetoscopy due to the low visual
quality, texture paucity, and hence fail in longer sequences due to the drift
accumulated over time. Deep learning techniques can facilitate in overcoming
these challenges. Therefore, we present a new generalized Deep Sequential
Mosaicking (DSM) framework for fetoscopic videos captured from different
settings such as simulation, phantom, and real environments. DSM extends an
existing deep image-based homography model to sequential data by proposing
controlled data augmentation and outlier rejection methods. Unlike existing
methods, DSM can handle visual variations due to specular highlights and
reflection across adjacent frames, hence reducing the accumulated drift. We
perform experimental validation and comparison using 5 diverse fetoscopic
videos to demonstrate the robustness of our framework.Comment: Accepted at MICCAI 201
High-Fidelity Low-Cost Synthetic Training Model for Fetoscopic Spina Bifida Repair
BACKGROUND: Fetoscopic Spina Bifida repair (fSB-repair) is increasingly being practiced, but limited skill acquisition poses a barrier to widespread adoption. Extensive training in relevant models, including both ex- and in-vivo models may help. To address this, a synthetic training model that is affordable, realistic and allows skill analysis would be useful.OBJECTIVE: To create a high-fidelity model for training the essential neurosurgical steps of fetoscopic spina bifida repair using synthetic materials. Additionally, we aimed to obtain a cheap and easily reproducible model.STUDY DESIGN: We developed a three-layered silicon-based model resembling the anatomical layers of a typical myelomeningocele lesion. It allows for filling the cyst with fluid and conducting a water tightness test post-repair. A compliant silicon ball mimics the uterine cavity, and is fixed to a solid 3D printed base. The fetal back with the lesion (single-use) is placed inside the uterine ball, which is reusable and repairable to allow practicing port insertion and fixation multiple times. Following cannula insertion, the uterus is insufflated, and clinical fetoscopic, robotic or prototype instruments can be used. Three skilled endoscopic surgeons each did six simulated fetoscopic repairs following the surgical steps of an open repair. The primary outcome was surgical success, based on water tightness of the repair, operation time <180 minutes and an Objective-Structured-Assessment-of-Technical-Skills (OSATS)-score of ≥ 18/25. Skill retention was measured using a competence commulative sum (C-CUSUM) analysis on composite binary outcome for surgical success. Secondary outcomes were cost and fabrication time of the model.RESULTS: We made a model for simulating spina bifida repair neurosurgical steps with anatomical details, port insertion, placode release and descent, undermining of skin and muscular layer, and endoscopic suturing. The model is made with reusable 3D-printed molds with easily accessible materials. The one-time startup cost was 211€, and each single-use simulated MMC-lesion costs 9.5€ in materials and 50 min working hours. Two skilled endoscopic surgeons performed six simulated three-port fetoscopic repairs, while a third used a Da-Vinci surgical robot. Operation times decreased over 30% from the first to last trial. Six experiments per surgeon did not show an obvious OSATS-score improvement. C-CUSUM analysis confirmed competency for each surgeon.CONCLUSION: This high-fidelity low-cost spina bifida model allows simulated dissection and closure of a myelomeningocele lesion.</p
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