Automatic summarization of endoscopic skull base surgical videos through object detection and hidden Markov modeling.

Endoscopic endonasal surgery is a medical procedure that utilizes an endoscopic video camera to view and manipulate a surgical site accessed through the nose. Despite these surgeries being video recorded, these videos are seldom reviewed or even saved in patient files due to the size and length of the video file. Editing to a manageable size may necessitate viewing 3 h or more of surgical video and manually splicing together the desired segments. We suggest a novel multi-stage video summarization procedure utilizing deep semantic features, tool detections, and video frame temporal correspondences to create a representative summarization. Summarization by our method resulted in a 98.2% reduction in overall video length while preserving 84% of key medical scenes. Furthermore, resulting summaries contained only 1% of scenes with irrelevant detail such as endoscope lens cleaning, blurry frames, or frames external to the patient. This outperformed leading commercial and open source summarization tools not designed for surgery, which only preserved 57% and 46% of key medical scenes in similar length summaries, and included 36% and 59% of scenes containing irrelevant detail. Experts agreed that on average (Likert Scale = 4) that the overall quality of the video was adequate to share with peers in its current state.

Object-Agnostic Vision Measurement Framework Based on One-Shot Learning and Behavior Tree.

Vision measurement is important for intelligent systems to obtain the precise structural and spatial information of objects. Beyond the object-specific vision measurement developed for fixed object type, it is appealing to explore the object-agnostic vision measurement, which can be efficiently reconfigured and adapted to various novel objects. This article proposes a framework to mimic the human's versatile visual measurement behavior: extract a set of contour primitives of interest (CPIs) from an image, then utilize the CPIs to calculate the key geometric information. First, a deep convolutional neural network (CNN) CPieNet+ is proposed under the one-shot learning scheme, aiming to extract the pixel-level object CPI from a raw query image, given an annotated support image. The fine-grained CPI prototypes are formed by sampling multiple points on the feature map of the support image. To leverage the explicit geometric knowledge in the CNN inference, the annotation map is encoded as a shape descriptor to guide the feature channel attention, and the geometric attribute awareness is realized by supervising the model to predict the direction and size of CPI. Second, the measurement behavior tree (BT) is designed to model the hierarchical geometric calculation procedure, which is flexibly configurable for different measurement requirements and is interpretable for nonexpert users. After the execution of the measurement BT, the pixel-level CPIs are converted to the required key geometric data. The effectiveness of the proposed methods is validated by a series of experiments.

Opportunities and Barriers to Rural Telerobotic Surgical Health Care in 2021: Report and Research Agenda from a Stakeholder Workshop.

Background: There are well-recognized challenges to delivering specialty health care in rural settings. These challenges are particularly evident for specialized surgical health care due to the lack of trained operators in rural communities. Telerobotic surgery could have a significant impact on the rural-urban health care gap, but thus far, the promise of this method of health care delivery has gone unrealized. With the increasing adoption of telehealth over the past year, along with the maturation of telecommunication and robotic technologies over the past 2 decades, a reappraisal of the opportunities and barriers to widespread implementation of telerobotic surgery is warranted. Here we report the outcome of a rural telerobotic stakeholder workshop to explore modern-day issues critical to the advancement of telerobotic surgical health care. Materials and Methods: We assembled a multidisciplinary stakeholder panel to participate in a 2-day Rural Telerobotic Surgery Stakeholder Workshop. Participants had diverse expertise, including specialty surgeons, technology experts, and representatives of the broader telerobotic health care ecosystem, including economists, lawyers, regulatory consultants, public health advocates, rural hospital administrators, nurses, and payers. The research team reviewed transcripts from the workshop with themes identified and research questions generated based on stakeholder comments and feedback. Results: Stakeholder discussions fell into four general themes, including (1) operating room team interactions, (2) education and training, (3) network and security, and (4) economic issues. The research team then identified several research questions within each of these themes and provided specific research strategies to address these questions. Conclusions: There are still important unanswered questions regarding the implementation and adoption of rural telerobotic surgery. Based on stakeholder feedback, we have developed a research agenda along with suggested strategies to address outstanding research questions. The successful execution of these research opportunities will fill critical gaps in our understanding of how to advance the widespread adoption of rural telerobotic health care.

Real-time virtual intraoperative CT in endoscopic sinus surgery.

PURPOSE: Endoscopic sinus surgery (ESS) is typically guided under preoperative computed tomography (CT), which increasingly diverges from actual patient anatomy as the surgery progresses. Studies have reported that the revision surgery rate in ESS ranges between 28 and 47%. This paper presents a method that can update the preoperative CT in real time to improve surgical completeness in ESS. APPROACH: The work presents and compares three novel methods that use instrument motion data and anatomical structures to predict surgical modifications in real time. The methods use learning techniques, such as nonparametric filtering and Gaussian process regression, to correlate surgical modifications with instrument tip positions, tip trajectories, and instrument shapes. Preoperative CT image sets are updated with modification predictions to serve as a virtual intraoperative CT. RESULTS: The three methods were compared in eight ESS cadaver cases, which were performed by five surgeons and included the following representative ESS operations: maxillary antrostomy, uncinectomy, anterior and posterior ethmoidectomy, and sphenoidotomy. Experimental results showed accuracy metrics that were clinically acceptable with dice similarity coefficients > 86%, with F-score > 92% and precision > 89.91% in surgical completeness evaluation. Among the three methods, the tip trajectory-based estimator had the highest precision of 96.87%. CONCLUSIONS: This work demonstrated that virtually modified intraoperative CT scans improved the consistency between the actual surgical scene and the reference model, and could lead to improved surgical completeness in ESS. Compared to actual intraoperative CT scans, the proposed method has no impact on existing surgical protocols, does not require extra hardware, does not expose the patient to radiation, and does not lengthen time under anesthesia.

Exploring telerobotic cardiac catheter ablation in a rural community hospital: A pilot study.

Background: Telerobotic surgery could improve access to specialty procedures such as cardiac catheter ablation in rural and underserved regions in the United States and worldwide. Advancements in telecommunications, internet infrastructure, and surgical robotics are lowering the technical hurdles for this future healthcare delivery paradigm. Nonetheless, important questions remain regarding the safe implementation of telerobotic surgery in rural community hospital settings. Objective: The purpose of this study was to pilot test a system and methods to explore telerobotic cardiac catheter ablation in a rural community hospital setting. Methods: We assembled a portable preclinical telerobotic catheter ablation system from commercial-grade components using third-party vendors. We then carried out 4 telerobotic surgery simulations with an urban surgeon and a rural community hospital operating room (OR) team spanning a distance of more than 2000 miles. Two challenge scenarios were incorporated into the simulations, including loss of network connection and cardiac perforation with subsequent life-threatening tamponade physiology. An ethnographic analysis was then performed. Results: Interviews and observations suggested that rural OR teams readily adapt to the telesurgery context. However, participant perceptions of team trust, communication, and emergency management were significantly altered by the remote location of the surgeon. In addition, most participants believed the OR team would have been better equipped for the challenges had they received formal training or had prior experience with the procedure being simulated. Conclusion: We demonstrate the utility and feasibility of a system and methods for studying specialty telerobotic surgery in a rural hospital OR setting.

A decade retrospective of medical robotics research from 2010 to 2020.

Robotics is a forward-looking discipline. Attention is focused on identifying the next grand challenges. In an applied field such as medical robotics, however, it is important to plan the future based on a clear understanding of what the research community has recently accomplished and where this work stands with respect to clinical needs and commercialization. This Review article identifies and analyzes the eight key research themes in medical robotics over the past decade. These thematic areas were identified using search criteria that identified the most highly cited papers of the decade. Our goal for this Review article is to provide an accessible way for readers to quickly appreciate some of the most exciting accomplishments in medical robotics over the past decade; for this reason, we have focused only on a small number of seminal papers in each thematic area. We hope that this article serves to foster an entrepreneurial spirit in researchers to reduce the widening gap between research and translation.

Local Style Preservation in Improved GAN-Driven Synthetic Image Generation for Endoscopic Tool Segmentation.

Accurate semantic image segmentation from medical imaging can enable intelligent vision-based assistance in robot-assisted minimally invasive surgery. The human body and surgical procedures are highly dynamic. While machine-vision presents a promising approach, sufficiently large training image sets for robust performance are either costly or unavailable. This work examines three novel generative adversarial network (GAN) methods of providing usable synthetic tool images using only surgical background images and a few real tool images. The best of these three novel approaches generates realistic tool textures while preserving local background content by incorporating both a style preservation and a content loss component into the proposed multi-level loss function. The approach is quantitatively evaluated, and results suggest that the synthetically generated training tool images enhance UNet tool segmentation performance. More specifically, with a random set of 100 cadaver and live endoscopic images from the University of Washington Sinus Dataset, the UNet trained with synthetically generated images using the presented method resulted in 35.7% and 30.6% improvement over using purely real images in mean Dice coefficient and Intersection over Union scores, respectively. This study is promising towards the use of more widely available and routine screening endoscopy to preoperatively generate synthetic training tool images for intraoperative UNet tool segmentation.

Automated atlas-based segmentation for skull base surgical planning.

PURPOSE: Computational surgical planning tools could help develop novel skull base surgical approaches that improve safety and patient outcomes. This defines a need for automated skull base segmentation to improve the usability of surgical planning software. The objective of this work was to design and validate an algorithm for atlas-based automated segmentation of skull base structures in individual image sets for skull base surgical planning. METHODS: Advanced Normalization Tools software was used to construct a synthetic CT template from 6 subjects, and skull base structures were manually segmented to create a reference atlas. Landmark registration followed by Elastix deformable registration was applied to the template to register it to each of the 30 trusted reference image sets. Dice coefficient, average Hausdorff distance, and clinical usability scoring were used to compare the atlas segmentations to those of the trusted reference image sets. RESULTS: The mean for average Hausdorff distance for all structures was less than 2 mm (mean for 95th percentile Hausdorff distance was less than 5 mm). For structures greater than 2.5 mL in volume, the average Dice coefficient was 0.73 (range 0.59-0.82), and for structures less than 2.5 mL in volume the Dice coefficient was less than 0.7. The usability scoring survey was completed by three experts, and all structures met the criteria for acceptable effort except for the foramen spinosum, rotundum, and carotid artery, which required more than minor corrections. CONCLUSION: Currently available open-source algorithms, such as the Elastix deformable algorithm, can be used for automated atlas-based segmentation of skull base structures with acceptable clinical accuracy and minimal corrections with the use of the proposed atlas. The first publicly available CT template and anterior skull base segmentation atlas being released (available at this link: http://hdl.handle.net/1773/46259 ) with this paper will allow for general use of automated atlas-based segmentation of the skull base.

The Future of Skull Base Surgery: A View Through Tinted Glasses.

In the present report, we have broadly outlined the potential advances in the field of skull base surgery, which might occur within the next 20 years based on the many areas of current research in biology and technology. Many of these advances will also be broadly applicable to other areas of neurosurgery. We have grounded our predictions for future developments in an exploration of what patients and surgeons most desire as outcomes for care. We next examined the recent developments in the field and outlined several promising areas of future improvement in skull base surgery, per se, as well as identifying the new hospital support systems needed to accommodate these changes. These include, but are not limited to, advances in imaging, Raman spectroscopy and microscopy, 3-dimensional printing and rapid prototyping, master-slave and semiautonomous robots, artificial intelligence applications in all areas of medicine, telemedicine, and green technologies in hospitals. In addition, we have reviewed the therapeutic approaches using nanotechnology, genetic engineering, antitumor antibodies, and stem cell technologies to repair damage caused by traumatic injuries, tumors, and iatrogenic injuries to the brain and cranial nerves. Additionally, we have discussed the training requirements for future skull base surgeons and stressed the need for adaptability and change. However, the essential requirements for skull base surgeons will remain unchanged, including knowledge, attention to detail, technical skill, innovation, judgment, and compassion. We believe that active involvement in these rapidly evolving technologies will enable us to shape some of the future of our discipline to address the needs of both patients and our profession.

A Model-Based Recurrent Neural Network With Randomness for Efficient Control With Applications.

Recently, Recurrent Neural Network (RNN) control schemes for redundant manipulators have been extensively studied. These control schemes demonstrate superior computational efficiency, control precision, and control robustness. However, they lack planning completeness. This paper explains why RNN control schemes suffer from the problem. Based on the analysis, this work presents a new random RNN control scheme, which 1) introduces randomness into RNN to address the planning completeness problem, 2) improves control precision with a new optimization target, 3) improves planning efficiency through learning from exploration. Theoretical analyses are used to prove the global stability, the planning completeness, and the computational complexity of the proposed method. Software simulation is provided to demonstrate the improved robustness against noise, the planning completeness and the improved planning efficiency of the proposed method over benchmark RNN control schemes. Real-world experiments are presented to demonstrate the application of the proposed method.

Comparison of Micro-Computed Tomography and Clinical Computed Tomography Protocols for Visualization of Nasal Cartilage Before Surgical Planning for Rhinoplasty.

IMPORTANCE: There is no imaging standard to model nasal cartilage for the planning of rhinoplasty procedures. Preoperative visualization of cartilage may improve objective evaluation of nasal deformities, surgical planning, and surgical reconstruction. OBJECTIVES: To evaluate the feasibility of visualizing nasal cartilage using high resolution micro-computed tomography (CT) compared with the criterion standard of pathologic findings in a cadaveric specimen and to evaluate its accuracy compared with various clinical CT protocols. DESIGN, SETTING, AND PARTICIPANTS: Anatomic study at the University of Washington using single human cadaveric nasal specimens performed from July 10, 2017, to March 30, 2018. INTERVENTIONS: A micro-CT acquisition with 60-micron resolution was obtained of a nasal specimen. The specimen was then scanned with 5 different clinical CT protocols to span both clinical care and machine limits. The specimen was then sectioned in 5-mm axial slices for pathologic analysis. MAIN OUTCOMES AND MEASURES: Micro-CT images were registered to pathologic specimen cross-sections using a graphite fiducial system. Cartilage substructures were manually segmented and analyzed. A library of matched images across the micro-CT and various clinical CT protocols was then developed. Region of interest analysis was performed for each of the cartilage structures and their boundaries on clinical CT protocols and micro-CT, with the outcome of mean (SD) density using Hounsfield units. RESULTS: A single human cadaveric nasal specimen was used to obtain the following results. Lower lateral cartilage, upper lateral cartilage, and septal cartilage were accurately delineated on the micro-CT images compared with pathologic findings. The mean absolute deviation from pathologic findings was 0.30 mm for septal cartilage thickness, 0.98 mm for maximal upper lateral cartilage length, and 1.40 mm for maximal lower lateral cartilage length. On clinical CT protocols, only septal cartilage was well discriminated from boundary. Higher radiation dose resulted in more accurate density measurements of cartilage, but it did not ultimately improve ability to discriminate cartilage. CONCLUSIONS AND RELEVANCE: The results of this anatomic study may represent a notable step toward advancing knowledge of the capabilities and pitfalls of nasal cartilage visualization on CT. Nasal cartilage visualization was feasible on the micro-CT compared with pathologic findings. Future research may further examine the barriers to accurately visualizing upper lateral cartilage and lower lateral cartilage, a prerequisite for clinical application. LEVEL OF EVIDENCE: NA.

Automated Surgical Approach Planning for Complex Skull Base Targets: Development and Validation of a Cost Function and Semantic At-las.

Successful multidisciplinary treatment of skull base pathology requires precise preoperative planning. Current surgical approach (pathway) selection for these complex procedures depends on an individual surgeon's experiences and background training. Because of anatomical variation in both normal tissue and pathology (eg, tumor), a successful surgical pathway used on one patient is not necessarily the best approach on another patient. The question is how to define and obtain optimized patient-specific surgical approach pathways? In this article, we demonstrate that the surgeon's knowledge and decision making in preoperative planning can be modeled by a multiobjective cost function in a retrospective analysis of actual complex skull base cases. Two different approaches- weighted-sum approach and Pareto optimality-were used with a defined cost function to derive optimized surgical pathways based on preoperative computed tomography (CT) scans and manually designated pathology. With the first method, surgeon's preferences were input as a set of weights for each objective before the search. In the second approach, the surgeon's preferences were used to select a surgical pathway from the computed Pareto optimal set. Using preoperative CT and magnetic resonance imaging, the patient-specific surgical pathways derived by these methods were similar (85% agreement) to the actual approaches performed on patients. In one case where the actual surgical approach was different, revision surgery was required and was performed utilizing the computationally derived approach pathway.

A Novel Recurrent Neural Network for Improving Redundant Manipulator Motion Planning Completeness.

Recurrent Neural Networks (RNNs) demonstrated advantages on control precision, system robustness and computational efficiency, and have been widely applied to redundant manipulator control optimization. Existing RNN control schemes locally optimize trajectories and are efficient and reliable on obstacle avoidance. However, for motion planning, they suffer from local minimum and do not have planning completeness. This work explained the cause of the planning incompleteness and addressed the problem with a novel RNN control scheme. The paper presented the proposed method in detail and analyzed the global stability and the planning completeness in theory. The proposed method was compared with other three control schemes on the precision, the robustness and the planning completeness in software simulation and the results shows the proposed method has improved precision and robustness, and planning completeness.

Semi-autonomous image-guided brain tumour resection using an integrated robotic system: A bench-top study.

BACKGROUND: Complete brain tumour resection is an extremely critical factor for patients' survival rate and long-term quality of life. This paper introduces a prototype medical robotic system that aims to automatically detect and clean up brain tumour residues after the removal of tumour bulk through conventional surgery. METHODS: We focus on the development of an integrated surgical robotic system for image-guided robotic brain surgery. The Behavior Tree framework is explored to coordinate cross-platform medical subtasks. RESULTS: The integrated system was tested on a simulated laboratory platform. Results and performance indicate the feasibility of supervised semi-automation for residual brain tumour ablation in a simulated surgical cavity with sub-millimetre accuracy. The modularity in the control architecture allows straightforward integration of further medical devices. CONCLUSIONS: This work presents a semi-automated laboratory setup, simulating an intraoperative robotic neurosurgical procedure with real-time endoscopic image guidance and provides a foundation for the future transition from engineering approaches to clinical application.

Gaussian Process Regression for Sensorless Grip Force Estimation of Cable Driven Elongated Surgical Instruments.

Haptic feedback is a critical but a clinically missing component in robotic Minimally Invasive Surgeries. This paper proposes a Gaussian Process Regression(GPR) based scheme to address the gripping force estimation problem for clinically commonly used elongated cable-driven surgical instruments. Based on the cable-driven mechanism property studies and surgical robotic system properties, four different Gaussian Process Regression filters were designed and analyzed, including: one GPR filter with 2-dimensional inputs, one GPR filter with 3-dimensional inputs, one GPR Unscented Kalman Filter (UKF) with 2-dimensional inputs, and one GPR UKF with 3-dimensional inputs. The four proposed methods were compared with the dynamic model based UKF filter on a 10mm gripper on the Raven-II surgical robot platform. The experimental results demonstrated that the four proposed methods outperformed the dynamic model based method on precision and reliability without parameter tuning. And surprisingly, among the four methods, the simplest GPR Filter with 2-dimensional inputs has the best performance.

Anatomical Region Segmentation for Objective Surgical Skill Assessment with Operating Room Motion Data.

Background Most existing objective surgical motion analysis schemes are limited to structured surgical tasks or recognition of motion patterns for certain categories of surgeries. Analyzing instrument motion data with respect to anatomical structures can break the limit, and an anatomical region segmentation algorithm is required for the analysis. Methods An atlas was generated by manually segmenting the skull base into nine regions, including left/right anterior/posterior ethmoid sinuses, frontal sinus, left and right maxillary sinuses, nasal airway, and sphenoid sinus. These regions were selected based on anatomical and surgical significance in skull base and sinus surgery. Six features, including left and right eye center, nasofrontal beak, anterior tip of nasal spine, posterior edge of hard palate at midline, and clival body at foramen magnum, were used for alignment. The B-spline deformable registration was adapted to fine tune the registration, and bony boundaries were automatically extracted for final precision improvement. The resultant deformation field was applied to the atlas, and the motion data were clustered according to the deformed atlas. Results Eight maxillofacial computed tomography scans were used in experiments. One was manually segmented as the atlas. The others were segmented by the proposed method. Motion data were clustered into nine groups for every dataset and outliers were filtered. Conclusions The proposed algorithm improved the efficiency of motion data clustering and requires limited human interaction in the process. The anatomical region segmentations effectively filtered out the portion of motion data that are out of surgery sites and grouped them according to anatomical similarities.

Efficient orbital structures segmentation with prior anatomical knowledge.

We present a fully automatic method for segmenting orbital structures (globes, optic nerves, and extraocular muscles) in CT images. Prior anatomical knowledge, such as shape, intensity, and spatial relationships of organs and landmarks, were utilized to define a volume of interest (VOI) that contains the desired structures. Then, VOI was used for fast localization and successful segmentation of each structure using predefined rules. Testing our method with 30 publicly available datasets, the average Dice similarity coefficient for right and left sides of [0.81, 0.79] eye globes, [0.72, 0.79] optic nerves, and [0.73, 0.76] extraocular muscles were achieved. The proposed method is accurate, efficient, does not require training data, and its intuitive pipeline allows the user to modify or extend to other structures.

An Automated Methodology for Assessing Anatomy-Specific Instrument Motion during Endoscopic Endonasal Skull Base Surgery.

Objectives Describe instrument motion during live endoscopic skull base surgery (ESBS) and evaluate kinematics within anatomic regions. Design Case series. Setting Tertiary academic center. Participants A single skull base surgeon performed six anterior skull base approaches to the pituitary. Main Outcomes and Measures Time-stamped instrument coordinates were recorded using an optical tracking system. Kinematics (i.e., mean cumulative instrument travel, velocity, acceleration, and angular velocity) was calculated by anatomic region including nasal vestibule, anterior and posterior ethmoid, sphenoid, and lateral opticocarotid recess (lOCR) regions. Results We observed mean (standard deviation, SD) velocities of 6.14 cm/s (1.55) in the nasal vestibule versus 1.65 cm/s (0.34) near the lOCR. Mean (SD) acceleration was 7,480 cm/s 2 (5790) in the vestibule versus 928 cm/s 2 (662) near the lOCR. Mean (SD) angular velocity was 17.2 degrees/s (8.31) in the vestibule and 5.37 degrees/s (1.09) near the lOCR. We observed a decreasing trend in the geometric mean velocity, acceleration, and angular velocity when approaching the pituitary ( p < 0.001). Conclusion Using a novel method for analyzing instrument motion during live ESBS, we observed a decreasing trend in kinematics with proximity to the pituitary. Additional characterization of surgical instrument motion is paramount for optimizing patient safety and training.

Quantitative Analysis of Transnasal Anterior Skull Base Approach: Report of Technology for Intraoperative Assessment of Instrument Motion.

OBJECTIVE: To develop a method to measure intraoperative surgical instrument motion. This model will be applicable to the study of surgical instrument kinematics including surgical training, skill verification, and the development of surgical warning systems that detect aberrant instrument motion that may result in patient injury. DESIGN: We developed an algorithm to automate derivation of surgical instrument kinematics in an endoscopic endonasal skull base surgery model. Surgical instrument motion was recorded during a cadaveric endoscopic transnasal approach to the pituitary using a navigation system modified to record intraoperative time-stamped Euclidian coordinates and Euler angles. Microdebrider tip coordinates and angles were referenced to the cadaver's preoperative computed tomography scan allowing us to assess surgical instrument kinematics over time. A representative cadaveric endoscopic endonasal approach to the pituitary was performed to demonstrate feasibility of our algorithm for deriving surgical instrument kinematics. CONCLUSIONS: Technical feasibility of automatically measuring intraoperative surgical instrument motion and deriving kinematics measurements was demonstrated using standard navigation equipment.

Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015.

PURPOSE: Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms. METHODS: In this work, we describe and present the results of the Head and Neck Auto-Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands. RESULTS: This paper presents the quantitative results of this challenge using multiple established error metrics and a well-defined ranking system. The strengths and weaknesses of the different auto-segmentation approaches are analyzed and discussed. CONCLUSIONS: The Head and Neck Auto-Segmentation Challenge 2015 was a good opportunity to assess the current state-of-the-art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure-specific segmentation algorithms.