arXiv subjects

Danail Stoyanov

Publications and source records attributed to Danail Stoyanov.

At least 145 records · Page 8Linked to original sources

arXiv2019

2017 Robotic Instrument Segmentation Challenge

In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand the strengths and limitations of different algorithms via performance comparison. However, this type of approach has had limited translation to problems in robotic assisted surgery as this field has never established the same level of common datasets and benchmarking methods. In 2015 a sub-challenge was introduced at the EndoVis workshop where a set of robotic images were provided with automatically generated annotations from robot forward kinematics. However, there were issues with this dataset due to the limited background variation, lack of complex motion and inaccuracies in the annotation. In this work we present the results of the 2017 challenge on robotic instrument segmentation which involved 10 teams participating in binary, parts and type based segmentation of articulated da Vinci robotic instruments.

cs.CV↗

arXiv2019

Nonrigid reconstruction of 3D breast surfaces with a low-cost RGBD camera for surgical planning and aesthetic evaluation

Accounting for 26% of all new cancer cases worldwide, breast cancer remains the most common form of cancer in women. Although early breast cancer has a favourable long-term prognosis, roughly a third of patients suffer from a suboptimal aesthetic outcome despite breast conserving cancer treatment. Clinical-quality 3D modelling of the breast surface therefore assumes an increasingly important role in advancing treatment planning, prediction and evaluation of breast cosmesis. Yet, existing 3D torso scanners are expensive and either infrastructure-heavy or subject to motion artefacts. In this paper we employ a single consumer-grade RGBD camera with an ICP-based registration approach to jointly align all points from a sequence of depth images non-rigidly. Subtle body deformation due to postural sway and respiration is successfully mitigated leading to a higher geometric accuracy through regularised locally affine transformations. We present results from 6 clinical cases where our method compares well with the gold standard and outperforms a previous approach. We show that our method produces better reconstructions qualitatively by visual assessment and quantitatively by consistently obtaining lower landmark error scores and yielding more accurate breast volume estimates.

cs.CV↗

arXiv2018

SurReal: enhancing Surgical simulation Realism using style transfer

Surgical simulation is an increasingly important element of surgical education. Using simulation can be a means to address some of the significant challenges in developing surgical skills with limited time and resources. The photo-realistic fidelity of simulations is a key feature that can improve the experience and transfer ratio of trainees. In this paper, we demonstrate how we can enhance the visual fidelity of existing surgical simulation by performing style transfer of multi-class labels from real surgical video onto synthetic content. We demonstrate our approach on simulations of cataract surgery using real data labels from an existing public dataset. Our results highlight the feasibility of the approach and also the powerful possibility to extend this technique to incorporate additional temporal constraints and to different applications.

cs.CV↗

arXiv2018

Spectral imaging of thermal damage induced during microwave ablation in the liver

Induction of thermal damage to tissue through delivery of microwave energy is frequently applied in surgery to destroy diseased tissue such as cancer cells. Minimization of unwanted harm to healthy tissue is still achieved subjectively, and the surgeon has few tools at their disposal to monitor the spread of the induced damage. This work describes the use of optical methods to monitor the time course of changes to the tissue during delivery of microwave energy in the porcine liver. Multispectral imaging and diffuse reflectance spectroscopy are used to monitor temporal changes in optical properties in parallel with thermal imaging. The results demonstrate the ability to monitor the spatial extent of thermal damage on a whole organ, including possible secondary effects due to vascular damage. Future applications of this type of imaging may see the multispectral data used as a feedback mechanism to avoid collateral damage to critical healthy structures and to potentially verify sufficient application of energy to the diseased tissue.

physics.med-ph↗

arXiv2018

FaceOff: Anonymizing Videos in the Operating Rooms

Video capture in the surgical operating room (OR) is increasingly possible and has potential for use with computer assisted interventions (CAI), surgical data science and within smart OR integration. Captured video innately carries sensitive information that should not be completely visible in order to preserve the patient's and the clinical teams' identities. When surgical video streams are stored on a server, the videos must be anonymized prior to storage if taken outside of the hospital. In this article, we describe how a deep learning model, Faster R-CNN, can be used for this purpose and help to anonymize video data captured in the OR. The model detects and blurs faces in an effort to preserve anonymity. After testing an existing face detection trained model, a new dataset tailored to the surgical environment, with faces obstructed by surgical masks and caps, was collected for fine-tuning to achieve higher face-detection rates in the OR. We also propose a temporal regularisation kernel to improve recall rates. The fine-tuned model achieves a face detection recall of 88.05 % and 93.45 % before and after applying temporal-smoothing respectively.

cs.CV↗

arXiv2018

Affordable Mobile-based Simulator for Robotic Surgery

Robotic surgery and novel surgical instrumentation present great potentials towards safer, more accurate and consistent minimally invasive surgery. However, their adoption is dependent to the access to training facilities and extensive surgical training. Robotic instruments require different dexterity skills compared to open or laparoscopic. Surgeons, therefore, are required to invest significant time by attending extensive training programs. Contrary, hands on experiences represent an additional operational cost for hospitals as the availability of robotic systems for training purposes is limited. All these technological and financial barriers for surgeons and hospitals hinder the adoption of robotic surgery. In this paper, we present a mobile dexterity training kit to develop basic surgical techniques within an affordable setting. The system could be used to train basic surgical gestures and to develop the motor skills needed for manoeuvring robotic instruments. Our work presents the architecture and components needed to create a simulated environment for training sub-tasks as well as a design for portable mobile manipulators that can be used as master controllers of different instruments. A preliminary study results demonstrate usability and skills development with this system.

cs.RO↗

arXiv2018

DeepPhase: Surgical Phase Recognition in CATARACTS Videos

Automated surgical workflow analysis and understanding can assist surgeons to standardize procedures and enhance post-surgical assessment and indexing, as well as, interventional monitoring. Computer-assisted interventional (CAI) systems based on video can perform workflow estimation through surgical instruments' recognition while linking them to an ontology of procedural phases. In this work, we adopt a deep learning paradigm to detect surgical instruments in cataract surgery videos which in turn feed a surgical phase inference recurrent network that encodes temporal aspects of phase steps within the phase classification. Our models present comparable to state-of-the-art results for surgical tool detection and phase recognition with accuracies of 99 and 78% respectively.

cs.CV↗

arXiv2018

Higher Order of Motion Magnification for Vessel Localisation in Surgical Video

Locating vessels during surgery is critical for avoiding inadvertent damage, yet vasculature can be difficult to identify. Video motion magnification can potentially highlight vessels by exaggerating subtle motion embedded within the video to become perceivable to the surgeon. In this paper, we explore a physiological model of artery distension to extend motion magnification to incorporate higher orders of motion, leveraging the difference in acceleration over time (jerk) in pulsatile motion to highlight the vascular pulse wave. Our method is compared to first and second order motion based Eulerian video magnification algorithms. Using data from a surgical video retrieved during a robotic prostatectomy, we show that our method can accentuate cardio-physiological features and produce a more succinct and clearer video for motion magnification, with more similarities in areas without motion to the source video at large magnifications. We validate the approach with a Structure Similarity (SSIM) and Peak Signal to Noise Ratio (PSNR) assessment of three videos at an increasing working distance, using three different levels of optical magnification. Spatio-temporal cross sections are presented to show the effectiveness of our proposal and video samples are provided to demonstrates qualitatively our results.

cs.CV↗

arXiv2018

Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks

Transoesophageal echocardiography (TEE) is a valuable diagnostic and monitoring imaging modality. Proper image acquisition is essential for diagnosis, yet current assessment techniques are solely based on manual expert review. This paper presents a supervised deep learn ing framework for automatically evaluating and grading the quality of TEE images. To obtain the necessary dataset, 38 participants of varied experience performed TEE exams with a high-fidelity virtual reality (VR) platform. Two Convolutional Neural Network (CNN) architectures, AlexNet and VGG, structured to perform regression, were finetuned and validated on manually graded images from three evaluators. Two different scoring strategies, a criteria-based percentage and an overall general impression, were used. The developed CNN models estimate the average score with a root mean square accuracy ranging between 84%-93%, indicating the ability to replicate expert valuation. Proposed strategies for automated TEE assessment can have a significant impact on the training process of new TEE operators, providing direct feedback and facilitating the development of the necessary dexterous skills.

cs.CV↗

arXiv2018

Surgical Data Science: A Consensus Perspective

Surgical data science is a scientific discipline with the objective of improving the quality of interventional healthcare and its value through capturing, organization, analysis, and modeling of data. The goal of the 1st workshop on Surgical Data Science was to bring together researchers working on diverse topics in surgical data science in order to discuss existing challenges, potential standards and new research directions in the field. Inspired by current open space and think tank formats, it was organized in June 2016 in Heidelberg. While the first day of the workshop, which was dominated by interactive sessions, was open to the public, the second day was reserved for a board meeting on which the information gathered on the public day was processed by (1) discussing remaining open issues, (2) deriving a joint definition for surgical data science and (3) proposing potential strategies for advancing the field. This document summarizes the key findings.

cs.CY↗

arXiv2018

Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery

Intraoperative segmentation and tracking of minimally invasive instruments is a prerequisite for computer- and robotic-assisted surgery. Since additional hardware like tracking systems or the robot encoders are cumbersome and lack accuracy, surgical vision is evolving as promising techniques to segment and track the instruments using only the endoscopic images. However, what is missing so far are common image data sets for consistent evaluation and benchmarking of algorithms against each other. The paper presents a comparative validation study of different vision-based methods for instrument segmentation and tracking in the context of robotic as well as conventional laparoscopic surgery. The contribution of the paper is twofold: we introduce a comprehensive validation data set that was provided to the study participants and present the results of the comparative validation study. Based on the results of the validation study, we arrive at the conclusion that modern deep learning approaches outperform other methods in instrument segmentation tasks, but the results are still not perfect. Furthermore, we show that merging results from different methods actually significantly increases accuracy in comparison to the best stand-alone method. On the other hand, the results of the instrument tracking task show that this is still an open challenge, especially during challenging scenarios in conventional laparoscopic surgery.

cs.CV↗

arXiv2018

Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia

In this work, we have concentrated our efforts on the interpretability of classification results coming from a fully convolutional neural network. Motivated by the classification of oesophageal tissue for real-time detection of early squamous neoplasia, the most frequent kind of oesophageal cancer in Asia, we present a new dataset and a novel deep learning method that by means of deep supervision and a newly introduced concept, the embedded Class Activation Map (eCAM), focuses on the interpretability of results as a design constraint of a convolutional network. We present a new approach to visualise attention that aims to give some insights on those areas of the oesophageal tissue that lead a network to conclude that the images belong to a particular class and compare them with those visual features employed by clinicians to produce a clinical diagnosis. In comparison to a baseline method which does not feature deep supervision but provides attention by grafting Class Activation Maps, we improve the F1-score from 87.3% to 92.7% and provide more detailed attention maps.

cs.CV↗

arXiv2018

Automated pick-up of suturing needles for robotic surgical assistance

Robot-assisted laparoscopic prostatectomy (RALP) is a treatment for prostate cancer that involves complete or nerve sparing removal prostate tissue that contains cancer. After removal the bladder neck is successively sutured directly with the urethra. The procedure is called urethrovesical anastomosis and is one of the most dexterity demanding tasks during RALP. Two suturing instruments and a pair of needles are used in combination to perform a running stitch during urethrovesical anastomosis. While robotic instruments provide enhanced dexterity to perform the anastomosis, it is still highly challenging and difficult to learn. In this paper, we presents a vision-guided needle grasping method for automatically grasping the needle that has been inserted into the patient prior to anastomosis. We aim to automatically grasp the suturing needle in a position that avoids hand-offs and immediately enables the start of suturing. The full grasping process can be broken down into: a needle detection algorithm; an approach phase where the surgical tool moves closer to the needle based on visual feedback; and a grasping phase through path planning based on observed surgical practice. Our experimental results show examples of successful autonomous grasping that has the potential to simplify and decrease the operational time in RALP by assisting a small component of urethrovesical anastomosis.

cs.RO↗

arXiv2018

Augmented Reality needle ablation guidance tool for Irreversible Electroporation in the pancreas

Irreversible electroporation (IRE) is a soft tissue ablation technique suitable for treatment of inoperable tumours in the pancreas. The process involves applying a high voltage electric field to the tissue containing the mass using needle electrodes, leaving cancerous cells irreversibly damaged and vulnerable to apoptosis. Efficacy of the treatment depends heavily on the accuracy of needle placement and requires a high degree of skill from the operator. In this paper, we describe an Augmented Reality (AR) system designed to overcome the challenges associated with planning and guiding the needle insertion process. Our solution, based on the HoloLens (Microsoft, USA) platform, tracks the position of the headset, needle electrodes and ultrasound (US) probe in space. The proof of concept implementation of the system uses this tracking data to render real-time holographic guides on the HoloLens, giving the user insight into the current progress of needle insertion and an indication of the target needle trajectory. The operator's field of view is augmented using visual guides and real-time US feed rendered on a holographic plane, eliminating the need to consult external monitors. Based on these early prototypes, we are aiming to develop a system that will lower the skill level required for IRE while increasing overall accuracy of needle insertion and, hence, the likelihood of successful treatment.

cs.CV↗

arXiv2017

Widening siamese architectures for stereo matching

Computational stereo is one of the classical problems in computer vision. Numerous algorithms and solutions have been reported in recent years focusing on developing methods for computing similarity, aggregating it to obtain spatial support and finally optimizing an energy function to find the final disparity. In this paper, we focus on the feature extraction component of stereo matching architecture and we show standard CNNs operation can be used to improve the quality of the features used to find point correspondences. Furthermore, we propose a simple space aggregation that hugely simplifies the correlation learning problem. Our results on benchmark data are compelling and show promising potential even without refining the solution.

cs.CV↗

arXiv2017

Simultaneous Recognition and Pose Estimation of Instruments in Minimally Invasive Surgery

Detection of surgical instruments plays a key role in ensuring patient safety in minimally invasive surgery. In this paper, we present a novel method for 2D vision-based recognition and pose estimation of surgical instruments that generalizes to different surgical applications. At its core, we propose a novel scene model in order to simultaneously recognize multiple instruments as well as their parts. We use a Convolutional Neural Network architecture to embody our model and show that the cross-entropy loss is well suited to optimize its parameters which can be trained in an end-to-end fashion. An additional advantage of our approach is that instrument detection at test time is achieved while avoiding the need for scale-dependent sliding window evaluation. This allows our approach to be relatively parameter free at test time and shows good performance for both instrument detection and tracking. We show that our approach surpasses state-of-the-art results on in-vivo retinal microsurgery image data, as well as ex-vivo laparoscopic sequences.

cs.CV↗

arXiv2017

Patch-based adaptive weighting with segmentation and scale (PAWSS) for visual tracking

Tracking-by-detection algorithms are widely used for visual tracking, where the problem is treated as a classification task where an object model is updated over time using online learning techniques. In challenging conditions where an object undergoes deformation or scale variations, the update step is prone to include background information in the model appearance or to lack the ability to estimate the scale change, which degrades the performance of the classifier. In this paper, we incorporate a Patch-based Adaptive Weighting with Segmentation and Scale (PAWSS) tracking framework that tackles both the scale and background problems. A simple but effective colour-based segmentation model is used to suppress background information and multi-scale samples are extracted to enrich the training pool, which allows the tracker to handle both incremental and abrupt scale variations between frames. Experimentally, we evaluate our approach on the online tracking benchmark (OTB) dataset and Visual Object Tracking (VOT) challenge datasets. The results show that our approach outperforms recent state-of-the-art trackers, and it especially improves the successful rate score on the OTB dataset, while on the VOT datasets, PAWSS ranks among the top trackers while operating at real-time frame rates.

cs.CV↗

arXiv2017

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.

cs.CV↗