Guest Editorial: IJCARS-MICCAI 2018 special issue.

status: publishe

Automatic registration of 3D models to laparoscopic video images for guidance during liver surgery

Laparoscopic liver interventions offer significant advantages over open surgery, such as less pain and trauma, and shorter recovery time for the patient. However, they also bring challenges for the surgeons such as the lack of tactile feedback, limited field of view and occluded anatomy. Augmented reality (AR) can potentially help during laparoscopic liver interventions by displaying sub-surface structures (such as tumours or vasculature). The initial registration between the 3D model extracted from the CT scan and the laparoscopic video feed is essential for an AR system which should be efficient, robust, intuitive to use and with minimal disruption to the surgical procedure. Several challenges of registration methods in laparoscopic interventions include the deformation of the liver due to gas insufflation in the abdomen, partial visibility of the organ and lack of prominent geometrical or texture-wise landmarks. These challenges are discussed in detail and an overview of the state of the art is provided. This research project aims to provide the tools to move towards a completely automatic registration. Firstly, the importance of pre-operative planning is discussed along with the characteristics of the liver that can be used in order to constrain a registration method. Secondly, maximising the amount of information obtained before the surgery, a semi-automatic surface based method is proposed to recover the initial rigid registration irrespective of the position of the shapes. Finally, a fully automatic 3D-2D rigid global registration is proposed which estimates a global alignment of the pre-operative 3D model using a single intra-operative image. Moving towards incorporating the different liver contours can help constrain the registration, especially for partial surfaces. Having a robust, efficient AR system which requires no manual interaction from the surgeon will aid in the translation of such approaches to the clinics

Augmented Reality Assistance for Surgical Interventions using Optical See-Through Head-Mounted Displays

Augmented Reality (AR) offers an interactive user experience via enhancing the real world environment with computer-generated visual cues and other perceptual information. It has been applied to different applications, e.g. manufacturing, entertainment and healthcare, through different AR media. An Optical See-Through Head-Mounted Display (OST-HMD) is a specialized hardware for AR, where the computer-generated graphics can be overlaid directly onto the user's normal vision via optical combiners. Using OST-HMD for surgical intervention has many potential perceptual advantages. As a novel concept, many technical and clinical challenges exist for OST-HMD-based AR to be clinically useful, which motivates the work presented in this thesis. From the technical aspects, we first investigate the display calibration of OST-HMD, which is an indispensable procedure to create accurate AR overlay. We propose various methods to reduce the user-related error, improve robustness of the calibration, and remodel the calibration as a 3D-3D registration problem. Secondly, we devise methods and develop hardware prototype to increase the user's visual acuity of both real and virtual content through OST-HMD, to aid them in tasks that require high visual acuity, e.g. dental procedures. Thirdly, we investigate the occlusion caused by the OST-HMD hardware, which limits the user's peripheral vision. We propose to use alternative indicators to remind the user of unattended environment motion. From the clinical perspective, we identified many clinical use cases where OST-HMD-based AR is potentially helpful, developed applications integrated with current clinical systems, and conducted proof-of-concept evaluations. We first present a "virtual monitor'' for image-guided surgery. It can replace real radiology monitors in the operating room with easier user control and more flexibility in positioning. We evaluated the "virtual monitor'' for simulated percutaneous spine procedures. Secondly, we developed ARssist, an application for the bedside assistant in robotic surgery. The assistant can see the robotic instruments and endoscope within the patient body with ARssist. We evaluated the efficiency, safety and ergonomics of the assistant during two typical tasks: instrument insertion and manipulation. The performance for inexperienced users is significantly improved with ARssist, and for experienced users, the system significantly enhanced their confidence level. Lastly, we developed ARAMIS, which utilizes real-time 3D reconstruction and visualization to aid the laparoscopic surgeon. It demonstrates the concept of "X-ray see-through'' surgery. Our preliminary evaluation validated the application via a peg transfer task, and also showed significant improvement in hand-eye coordination. Overall, we have demonstrated that OST-HMD based AR application provides ergonomic improvements, e.g. hand-eye coordination. In challenging situations or for novice users, the improvements in ergonomic factors lead to improvement in task performance. With continuous effort as a community, optical see-through augmented reality technology will be a useful interventional aid in the near future

A comprehensive survey on recent deep learning-based methods applied to surgical data

Minimally invasive surgery is highly operator dependant with a lengthy procedural time causing fatigue to surgeon and risks to patients such as injury to organs, infection, bleeding, and complications of anesthesia. To mitigate such risks, real-time systems are desired to be developed that can provide intra-operative guidance to surgeons. For example, an automated system for tool localization, tool (or tissue) tracking, and depth estimation can enable a clear understanding of surgical scenes preventing miscalculations during surgical procedures. In this work, we present a systematic review of recent machine learning-based approaches including surgical tool localization, segmentation, tracking, and 3D scene perception. Furthermore, we provide a detailed overview of publicly available benchmark datasets widely used for surgical navigation tasks. While recent deep learning architectures have shown promising results, there are still several open research problems such as a lack of annotated datasets, the presence of artifacts in surgical scenes, and non-textured surfaces that hinder 3D reconstruction of the anatomical structures. Based on our comprehensive review, we present a discussion on current gaps and needed steps to improve the adaptation of technology in surgery.Comment: This paper is to be submitted to International journal of computer visio

Robot Assisted Object Manipulation for Minimally Invasive Surgery

Robotic systems have an increasingly important role in facilitating minimally invasive surgical treatments. In robot-assisted minimally invasive surgery, surgeons remotely control instruments from a console to perform operations inside the patient. However, despite the advanced technological status of surgical robots, fully autonomous systems, with decision-making capabilities, are not yet available. In 2017, a structure to classify the research efforts toward autonomy achievable with surgical robots was proposed by Yang et al. Six different levels were identified: no autonomy, robot assistance, task autonomy, conditional autonomy, high autonomy, and full autonomy. All the commercially available platforms in robot-assisted surgery is still in level 0 (no autonomy). Despite increasing the level of autonomy remains an open challenge, its adoption could potentially introduce multiple benefits, such as decreasing surgeons’ workload and fatigue and pursuing a consistent quality of procedures. Ultimately, allowing the surgeons to interpret the ample and intelligent information from the system will enhance the surgical outcome and positively reflect both on patients and society. Three main aspects are required to introduce automation into surgery: the surgical robot must move with high precision, have motion planning capabilities and understand the surgical scene. Besides these main factors, depending on the type of surgery, there could be other aspects that might play a fundamental role, to name some compliance, stiffness, etc. This thesis addresses three technological challenges encountered when trying to achieve the aforementioned goals, in the specific case of robot-object interaction. First, how to overcome the inaccuracy of cable-driven systems when executing fine and precise movements. Second, planning different tasks in dynamically changing environments. Lastly, how the understanding of a surgical scene can be used to solve more than one manipulation task. To address the first challenge, a control scheme relying on accurate calibration is implemented to execute the pick-up of a surgical needle. Regarding the planning of surgical tasks, two approaches are explored: one is learning from demonstration to pick and place a surgical object, and the second is using a gradient-based approach to trigger a smoother object repositioning phase during intraoperative procedures. Finally, to improve scene understanding, this thesis focuses on developing a simulation environment where multiple tasks can be learned based on the surgical scene and then transferred to the real robot. Experiments proved that automation of the pick and place task of different surgical objects is possible. The robot was successfully able to autonomously pick up a suturing needle, position a surgical device for intraoperative ultrasound scanning and manipulate soft tissue for intraoperative organ retraction. Despite automation of surgical subtasks has been demonstrated in this work, several challenges remain open, such as the capabilities of the generated algorithm to generalise over different environment conditions and different patients

Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence

Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices. MAR systems enable users to interact with MAR devices, such as smartphones and head-worn wearables, and perform seamless transitions from the physical world to a mixed world with digital entities. These MAR systems support user experiences using MAR devices to provide universal access to digital content. Over the past 20 years, several MAR systems have been developed, however, the studies and design of MAR frameworks have not yet been systematically reviewed from the perspective of user-centric design. This article presents the first effort of surveying existing MAR frameworks (count: 37) and further discuss the latest studies on MAR through a top-down approach: (1) MAR applications; (2) MAR visualisation techniques adaptive to user mobility and contexts; (3) systematic evaluation of MAR frameworks, including supported platforms and corresponding features such as tracking, feature extraction, and sensing capabilities; and (4) underlying machine learning approaches supporting intelligent operations within MAR systems. Finally, we summarise the development of emerging research fields and the current state-of-the-art, and discuss the important open challenges and possible theoretical and technical directions. This survey aims to benefit both researchers and MAR system developers alike.Peer reviewe

Optimization of craniosynostosis surgery: virtual planning, intraoperative 3D photography and surgical navigation

Mención Internacional en el título de doctorCraniosynostosis is a congenital defect defined as the premature fusion of one or more cranial sutures. This fusion leads to growth restriction and deformation of the cranium, caused by compensatory expansion parallel to the fused sutures. Surgical correction is the preferred treatment in most cases to excise the fused sutures and to normalize cranial shape. Although multiple technological advancements have arisen in the surgical management of craniosynostosis, interventional planning and surgical correction are still highly dependent on the subjective assessment and artistic judgment of craniofacial surgeons. Therefore, there is a high variability in individual surgeon performance and, thus, in the surgical outcomes. The main objective of this thesis was to explore different approaches to improve the surgical management of craniosynostosis by reducing subjectivity in all stages of the process, from the preoperative virtual planning phase to the intraoperative performance. First, we developed a novel framework for automatic planning of craniosynostosis surgery that enables: calculating a patient-specific normative reference shape to target, estimating optimal bone fragments for remodeling, and computing the most appropriate configuration of fragments in order to achieve the desired target cranial shape. Our results showed that automatic plans were accurate and achieved adequate overcorrection with respect to normative morphology. Surgeons’ feedback indicated that the integration of this technology could increase the accuracy and reduce the duration of the preoperative planning phase. Second, we validated the use of hand-held 3D photography for intraoperative evaluation of the surgical outcome. The accuracy of this technology for 3D modeling and morphology quantification was evaluated using computed tomography imaging as gold-standard. Our results demonstrated that 3D photography could be used to perform accurate 3D reconstructions of the anatomy during surgical interventions and to measure morphological metrics to provide feedback to the surgical team. This technology presents a valuable alternative to computed tomography imaging and can be easily integrated into the current surgical workflow to assist during the intervention. Also, we developed an intraoperative navigation system to provide real-time guidance during craniosynostosis surgeries. This system, based on optical tracking, enables to record the positions of remodeled bone fragments and compare them with the target virtual surgical plan. Our navigation system is based on patient-specific surgical guides, which fit into the patient’s anatomy, to perform patient-to-image registration. In addition, our workflow does not rely on patient’s head immobilization or invasive attachment of dynamic reference frames. After testing our system in five craniosynostosis surgeries, our results demonstrated a high navigation accuracy and optimal surgical outcomes in all cases. Furthermore, the use of navigation did not substantially increase the operative time. Finally, we investigated the use of augmented reality technology as an alternative to navigation for surgical guidance in craniosynostosis surgery. We developed an augmented reality application to visualize the virtual surgical plan overlaid on the surgical field, indicating the predefined osteotomy locations and target bone fragment positions. Our results demonstrated that augmented reality provides sub-millimetric accuracy when guiding both osteotomy and remodeling phases during open cranial vault remodeling. Surgeons’ feedback indicated that this technology could be integrated into the current surgical workflow for the treatment of craniosynostosis. To conclude, in this thesis we evaluated multiple technological advancements to improve the surgical management of craniosynostosis. The integration of these developments into the surgical workflow of craniosynostosis will positively impact the surgical outcomes, increase the efficiency of surgical interventions, and reduce the variability between surgeons and institutions.Programa de Doctorado en Ciencia y Tecnología Biomédica por la Universidad Carlos III de MadridPresidente: Norberto Antonio Malpica González.- Secretario: María Arrate Muñoz Barrutia.- Vocal: Tamas Ung

Automated Distinct Bone Segmentation from Computed Tomography Images using Deep Learning

Large-scale CT scans are frequently performed for forensic and diagnostic purposes, to plan and direct surgical procedures, and to track the development of bone-related diseases. This often involves radiologists who have to annotate bones manually or in a semi-automatic way, which is a time consuming task. Their annotation workload can be reduced by automated segmentation and detection of individual bones. This automation of distinct bone segmentation not only has the potential to accelerate current workflows but also opens up new possibilities for processing and presenting medical data for planning, navigation, and education. In this thesis, we explored the use of deep learning for automating the segmentation of all individual bones within an upper-body CT scan. To do so, we had to find a network architec- ture that provides a good trade-off between the problem’s high computational demands and the results’ accuracy. After finding a baseline method and having enlarged the dataset, we set out to eliminate the most prevalent types of error. To do so, we introduced an novel method called binary-prediction-enhanced multi-class (BEM) inference, separating the task into two: Distin- guishing bone from non-bone is conducted separately from identifying the individual bones. Both predictions are then merged, which leads to superior results. Another type of error is tack- led by our developed architecture, the Sneaky-Net, which receives additional inputs with larger fields of view but at a smaller resolution. We can thus sneak more extensive areas of the input into the network while keeping the growth of additional pixels in check. Overall, we present a deep-learning-based method that reliably segments most of the over one hundred distinct bones present in upper-body CT scans in an end-to-end trained matter quickly enough to be used in interactive software. Our algorithm has been included in our groups virtual reality medical image visualisation software SpectoVR with the plan to be used as one of the puzzle piece in surgical planning and navigation, as well as in the education of future doctors

Automatic Esophageal Abnormality Detection and Classification

Esophageal cancer is counted as one of the deadliest cancers worldwide ranking the sixth among all types of cancers. Early esophageal cancer typically causes no symp- toms and mainly arises from overlooked/untreated premalignant abnormalities in the esophagus tube. Endoscopy is the main tool used for the detection of abnormalities, and the cell deformation stage is confirmed by taking biopsy samples. The process of detection and classification is considered challenging for several reasons such as; different types of abnormalities (including early cancer stages) can be located ran- domly throughout the esophagus tube, abnormal regions can have various sizes and appearances which makes it difficult to capture, and failure in discriminating between the columnar mucosa from the metaplastic epithelium. Although many studies have been conducted, it remains a challenging task and improving the accuracy of auto- matically classifying and detecting different esophageal abnormalities is an ongoing field. This thesis aims to develop novel automated methods for the detection and classification of the abnormal esophageal regions (precancerous and cancerous) from endoscopic images and videos. In this thesis, firstly, the abnormality stage of the esophageal cell deformation is clas- sified from confocal laser endomicroscopy (CLE) images. The CLE is an endoscopic tool that provides a digital pathology view of the esophagus cells. The classifica- tion is achieved by enhancing the internal features of the CLE image, using a novel enhancement filter that utilizes fractional integration and differentiation. Different imaging features including, Multi-Scale pyramid rotation LBP (MP-RLBP), gray level co-occurrence matrices (GLCM), fractal analysis, fuzzy LBP and maximally stable extremal regions (MSER), are calculated from the enhanced image to assure a robust classification result. The support vector machine (SVM) and random forest (RF) classifiers are employed to classify each image into its pathology stage. Secondly, we propose an automatic detection method to locate abnormality regions from high definition white light (HD-WLE) endoscopic images. We first investigate the performance of different deep learning detection methods on our dataset. Then we propose an approach that combines hand-designed Gabor features with extracted convolutional neural network features that are used by the Faster R-CNN to detect abnormal regions. Moreover, to further improve the detection performance, we pro- pose a novel two-input network named GFD-Faster RCNN. The proposed method generates a Gabor fractal image from the original endoscopic image using Gabor filters. Then features are learned separately from the endoscopic image and the gen- erated Gabor fractal image using the densely connected convolutional network to detect abnormal esophageal regions. Thirdly, we present a novel model to detect the abnormal regions from endoscopic videos. We design a 3D Sequential DenseConvLstm network to extract spatiotem- poral features from the input videos that are utilized by a region proposal network and ROI pooling layer to detect abnormality regions in each frame throughout the video. Additionally, we suggest an FS-CRF post-processing method that incorpor- ates the Conditional Random Field (CRF) on a frame-based level to recover missed abnormal regions in neighborhood frames within the same clip. The methods are evaluated on four datasets: (1) CLE dataset used for the classific- ation model, (2) Publicly available dataset named Kvasir, (3) MICCAI’15 Endovis challenge dataset, Both datasets (2) and (3) are used for the evaluation of detection model from endoscopic images. Finally, (4) Gastrointestinal Atlas dataset used for the evaluation of the video detection model. The experimental results demonstrate promising results of the different models and have outperformed the state-of-the-art methods

3D shape instantiation for intra-operative navigation from a single 2D projection

Unlike traditional open surgery where surgeons can see the operation area clearly, in robot-assisted Minimally Invasive Surgery (MIS), a surgeon’s view of the region of interest is usually limited. Currently, 2D images from fluoroscopy, Magnetic Resonance Imaging (MRI), endoscopy or ultrasound are used for intra-operative guidance as real-time 3D volumetric acquisition is not always possible due to the acquisition speed or exposure constraints. 3D reconstruction, however, is key to navigation in complex in vivo geometries and can help resolve this issue. Novel 3D shape instantiation schemes are developed in this thesis, which can reconstruct the high-resolution 3D shape of a target from limited 2D views, especially a single 2D projection or slice. To achieve a complete and automatic 3D shape instantiation pipeline, segmentation schemes based on deep learning are also investigated. These include normalization schemes for training U-Nets and network architecture design of Atrous Convolutional Neural Networks (ACNNs). For U-Net normalization, four popular normalization methods are reviewed, then Instance-Layer Normalization (ILN) is proposed. It uses a sigmoid function to linearly weight the feature map after instance normalization and layer normalization, and cascades group normalization after the weighted feature map. Detailed validation results potentially demonstrate the practical advantages of the proposed ILN for effective and robust segmentation of different anatomies. For network architecture design in training Deep Convolutional Neural Networks (DCNNs), the newly proposed ACNN is compared to traditional U-Net where max-pooling and deconvolutional layers are essential. Only convolutional layers are used in the proposed ACNN with different atrous rates and it has been shown that the method is able to provide a fully-covered receptive field with a minimum number of atrous convolutional layers. ACNN enhances the robustness and generalizability of the analysis scheme by cascading multiple atrous blocks. Validation results have shown the proposed method achieves comparable results to the U-Net in terms of medical image segmentation, whilst reducing the trainable parameters, thus improving the convergence and real-time instantiation speed. For 3D shape instantiation of soft and deforming organs during MIS, Sparse Principle Component Analysis (SPCA) has been used to analyse a 3D Statistical Shape Model (SSM) and to determine the most informative scan plane. Synchronized 2D images are then scanned at the most informative scan plane and are expressed in a 2D SSM. Kernel Partial Least Square Regression (KPLSR) has been applied to learn the relationship between the 2D and 3D SSM. It has been shown that the KPLSR-learned model developed in this thesis is able to predict the intra-operative 3D target shape from a single 2D projection or slice, thus permitting real-time 3D navigation. Validation results have shown the intrinsic accuracy achieved and the potential clinical value of the technique. The proposed 3D shape instantiation scheme is further applied to intra-operative stent graft deployment for the robot-assisted treatment of aortic aneurysms. Mathematical modelling is first used to simulate the stent graft characteristics. This is then followed by the Robust Perspective-n-Point (RPnP) method to instantiate the 3D pose of fiducial markers of the graft. Here, Equally-weighted Focal U-Net is proposed with a cross-entropy and an additional focal loss function. Detailed validation has been performed on patient-specific stent grafts with an accuracy between 1-3mm. Finally, the relative merits and potential pitfalls of all the methods developed in this thesis are discussed, followed by potential future research directions and additional challenges that need to be tackled.Open Acces