83 research outputs found
Real-Time Polarimetry of Hyperpolarized 13C Nuclear Spins Using an Atomic Magnetometer
We introduce a method for nondestructive quantification of nuclear spin polarization, of relevance to hyperpolarized spin tracers widely used in magnetic resonance from spectroscopy to in vivo imaging. In a bias field of around 30 nT we use a high-sensitivity miniaturized 87Rb-vapor magnetometer to measure the field generated by the sample, as it is driven by a windowed dynamical decoupling pulse sequence that both maximizes the nuclear spin lifetime and modulates the polarization for easy detection. We demonstrate the procedure applied to a 0.08 M hyperpolarized [1-13C]-pyruvate solution produced by dissolution dynamic nuclear polarization, measuring polarization repeatedly during natural decay at Earth's field. Application to real-time and continuous quality monitoring of hyperpolarized substances is discussed
TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
Automatic surgical phase recognition is a challenging and crucial task with
the potential to improve patient safety and become an integral part of
intra-operative decision-support systems. In this paper, we propose, for the
first time in workflow analysis, a Multi-Stage Temporal Convolutional Network
(MS-TCN) that performs hierarchical prediction refinement for surgical phase
recognition. Causal, dilated convolutions allow for a large receptive field and
online inference with smooth predictions even during ambiguous transitions. Our
method is thoroughly evaluated on two datasets of laparoscopic cholecystectomy
videos with and without the use of additional surgical tool information.
Outperforming various state-of-the-art LSTM approaches, we verify the
suitability of the proposed causal MS-TCN for surgical phase recognition.Comment: 10 pages, 2 figure
A learning robot for cognitive camera control in minimally invasive surgery
Background!#!We demonstrate the first self-learning, context-sensitive, autonomous camera-guiding robot applicable to minimally invasive surgery. The majority of surgical robots nowadays are telemanipulators without autonomous capabilities. Autonomous systems have been developed for laparoscopic camera guidance, however following simple rules and not adapting their behavior to specific tasks, procedures, or surgeons.!##!Methods!#!The herein presented methodology allows different robot kinematics to perceive their environment, interpret it according to a knowledge base and perform context-aware actions. For training, twenty operations were conducted with human camera guidance by a single surgeon. Subsequently, we experimentally evaluated the cognitive robotic camera control. A VIKY EP system and a KUKA LWR 4 robot were trained on data from manual camera guidance after completion of the surgeon's learning curve. Second, only data from VIKY EP were used to train the LWR and finally data from training with the LWR were used to re-train the LWR.!##!Results!#!The duration of each operation decreased with the robot's increasing experience from 1704 s ± 244 s to 1406 s ± 112 s, and 1197 s. Camera guidance quality (good/neutral/poor) improved from 38.6/53.4/7.9 to 49.4/46.3/4.1% and 56.2/41.0/2.8%.!##!Conclusions!#!The cognitive camera robot improved its performance with experience, laying the foundation for a new generation of cognitive surgical robots that adapt to a surgeon's needs
Comparative validation of single-shot optical techniques for laparoscopic 3-D surface reconstruction
Intra-operative imaging techniques for obtaining the shape and morphology of soft-tissue surfaces in vivo are a key enabling technology for advanced surgical systems. Different optical techniques for 3-D surface reconstruction in laparoscopy have been proposed, however, so far no quantitative and comparative validation has been performed. Furthermore, robustness of the methods to clinically important factors like smoke or bleeding has not yet been assessed. To address these issues, we have formed a joint international initiative with the aim of validating different state-of-the-art passive and active reconstruction methods in a comparative manner. In this comprehensive in vitro study, we investigated reconstruction accuracy using different organs with various shape and texture and also tested reconstruction robustness with respect to a number of factors like the pose of the endoscope as well as the amount of blood or smoke present in the scene. The study suggests complementary advantages of the different techniques with respect to accuracy, robustness, point density, hardware complexity and computation time. While reconstruction accuracy under ideal conditions was generally high, robustness is a remaining issue to be addressed. Future work should include sensor fusion and in vivo validation studies in a specific clinical context. To trigger further research in surface reconstruction, stereoscopic data of the study will be made publically available at www.open-CAS.com upon publication of the paper
2018 Robotic Scene Segmentation Challenge
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models. However, the limited background variation and simple motion rendered the dataset uninformative in learning about which techniques would be suitable for segmentation in real surgery. In 2017, at the same workshop in Quebec we introduced the robotic instrument segmentation dataset with 10 teams participating in the challenge to perform binary, articulating parts and type segmentation of da Vinci instruments. This challenge included realistic instrument motion and more complex porcine tissue as background and was widely addressed with modifications on U-Nets and other popular CNN architectures. In 2018 we added to the complexity by introducing a set of anatomical objects and medical devices to the segmented classes. To avoid over-complicating the challenge, we continued with porcine data which is dramatically simpler than human tissue due to the lack of fatty tissue occluding many organs
Why is the Winner the Best?
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work
Why is the winner the best?
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The 'typical' lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work
Formalization of Smart Metering requirements
Today's industries and households have a growing need for resources like electricity obtained and measured via house connections. For the future, european and national regulations require providers to enable additional services such as accurate monthly bills and consumption information regarding the actual time of use. Those regulations and future use cases like charging stations for E-Mobility or the need for accurate real-time consumption information in Smart Grids require the classical meters to be replaced by Smart Meters that utilize embedded systems providing network connectivity to the backend. These functionalities raise additional security concerns that are addressed by regulations and laws of the metrology domain. However, the current directives in this area rely only on verbal definitions of these security requirements. This paper analyses security requirements from the Measuring Instruments Directive 2004/22/EC (MID) and demonstrates a possible formal repres entation ruling out the drawbacks of textual descriptions and enabling formal reasoning and proving of lawful requirements
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