Diabetic foot ulcers segmentation challenge report: Benchmark and analysis.

Monitoring the healing progress of diabetic foot ulcers is a challenging process. Accurate segmentation of foot ulcers can help podiatrists to quantitatively measure the size of wound regions to assist prediction of healing status. The main challenge in this field is the lack of publicly available manual delineation, which can be time consuming and laborious. Recently, methods based on deep learning have shown excellent results in automatic segmentation of medical images, however, they require large-scale datasets for training, and there is limited consensus on which methods perform the best. The 2022 Diabetic Foot Ulcers segmentation challenge was held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention, which sought to address these issues and stimulate progress in this research domain. A training set of 2000 images exhibiting diabetic foot ulcers was released with corresponding segmentation ground truth masks. Of the 72 (approved) requests from 47 countries, 26 teams used this data to develop fully automated systems to predict the true segmentation masks on a test set of 2000 images, with the corresponding ground truth segmentation masks kept private. Predictions from participating teams were scored and ranked according to their average Dice similarity coefficient of the ground truth masks and prediction masks. The winning team achieved a Dice of 0.7287 for diabetic foot ulcer segmentation. This challenge has now entered a live leaderboard stage where it serves as a challenging benchmark for diabetic foot ulcer segmentation.

NanoDefiner Framework and e-Tool Revisited According to the European Commission's Nanomaterial Definition 2022/C 229/01.

The new recommended definition of a nanomaterial, 2022/C 229/01, adopted by the European Commission in 2022, will have a considerable impact on European Union legislation addressing chemicals, and therefore tools to implement this new definition are urgently needed. The updated NanoDefiner framework and its e-tool implementation presented here are such instruments, which help stakeholders to find out in a straightforward way whether a material is a nanomaterial or not. They are two major outcomes of the NanoDefine project, which is explicitly referred to in the new definition. This work revisits the framework and e-tool, and elaborates necessary adjustments to make these outcomes applicable for the updated recommendation. A broad set of case studies on representative materials confirms the validity of these adjustments. To further foster the sustainability and applicability of the framework and e-tool, measures for the FAIRification of expert knowledge within the e-tool's knowledge base are elaborated as well. The updated framework and e-tool are now ready to be used in line with the updated recommendation. The presented approach may serve as an example for reviewing existing guidance and tools developed for the previous definition 2011/696/EU, particularly those adopting NanoDefine project outcomes.

Static and Dynamic Accuracy and Occlusion Robustness of SteamVR Tracking 2.0 in Multi-Base Station Setups.

The tracking of objects and person position, orientation, and movement is relevant for various medical use cases, e.g., practical training of medical staff or patient rehabilitation. However, these demand high tracking accuracy and occlusion robustness. Expensive professional tracking systems fulfill these demands, however, cost-efficient and potentially adequate alternatives can be found in the gaming industry, e.g., SteamVR Tracking. This work presents an evaluation of SteamVR Tracking in its latest version 2.0 in two experimental setups, involving two and four base stations. Tracking accuracy, both static and dynamic, and occlusion robustness are investigated using a VIVE Tracker (3.0). A dynamic analysis further compares three different velocities. An error evaluation is performed using a Universal Robots UR10 robotic arm as ground-truth system under nonlaboratory conditions. Results are presented using the Root Mean Square Error. For static experiments, tracking errors in the submillimeter and subdegree range are achieved by both setups. Dynamic experiments achieved errors in the submillimeter range as well, yet tracking accuracy suffers from increasing velocity. Four base stations enable generally higher accuracy and robustness, especially in the dynamic experiments. Both setups enable adequate accuracy for diverse medical use cases. However, use cases demanding very high accuracy should primarily rely on SteamVR Tracking 2.0 with four base stations.

Deep learning in diabetic foot ulcers detection: A comprehensive evaluation.

There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art deep learning object detection frameworks applied to this problem. DFUC2020 provided participants with a comprehensive dataset consisting of 2,000 images for training and 2,000 images for testing. This paper summarizes the results of DFUC2020 by comparing the deep learning-based algorithms proposed by the winning teams: Faster R-CNN, three variants of Faster R-CNN and an ensemble method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For each deep learning method, we provide a detailed description of model architecture, parameter settings for training and additional stages including pre-processing, data augmentation and post-processing. We provide a comprehensive evaluation for each method. All the methods required a data augmentation stage to increase the number of images available for training and a post-processing stage to remove false positives. The best performance was obtained from Deformable Convolution, a variant of Faster R-CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhance the F1-Score but not the mAP.

Nano or Not Nano? A Structured Approach for Identifying Nanomaterials According to the European Commission's Definition.

Identifying nanomaterials (NMs) according to European Union legislation is challenging, as there is an enormous variety of materials, with different physico-chemical properties. The NanoDefiner Framework and its Decision Support Flow Scheme (DSFS) allow choosing the optimal method to measure the particle size distribution by matching the material properties and the performance of the particular measurement techniques. The DSFS leads to a reliable and economic decision whether a material is an NM or not based on scientific criteria and respecting regulatory requirements. The DSFS starts beyond regulatory requirements by identifying non-NMs by a proxy approach based on their volume-specific surface area. In a second step, it identifies NMs. The DSFS is tested on real-world materials and is implemented in an e-tool. The DSFS is compared with a decision flowchart of the European Commission's (EC) Joint Research Centre (JRC), which rigorously follows the explicit criteria of the EC NM definition with the focus on identifying NMs, and non-NMs are identified by exclusion. The two approaches build on the same scientific basis and measurement methods, but start from opposite ends: the JRC Flowchart starts by identifying NMs, whereas the NanoDefiner Framework first identifies non-NMs.

NanoDefiner e-Tool: An Implemented Decision Support Framework for Nanomaterial Identification.

The European Commission's recommendation on the definition of nanomaterial (2011/696/EU) established an applicable standard for material categorization. However, manufacturers face regulatory challenges during registration of their products. Reliable categorization is difficult and requires considerable expertise in existing measurement techniques (MTs). Additionally, organizational complexity is increased as different authorities' registration processes require distinct reporting. The NanoDefine project tackled these obstacles by providing the NanoDefiner e-tool: A decision support expert system for nanomaterial identification in a regulatory context. It provides MT recommendations for categorization of specific materials using a tiered approach (screening/confirmatory), and was constructed with experts from academia and industry to be extensible, interoperable, and adaptable for forthcoming revisions of the nanomaterial definition. An implemented MT-driven material categorization scheme allows detailed description. Its guided workflow is suitable for a variety of user groups. Direct feedback and explanation enable transparent decisions. Expert knowledge is held in a knowledge base for representation of MT performance criteria and physicochemical particle type properties. Continuous revision ensured data quality and validity. Recommendations were validated by independent case studies on industry-relevant particulate materials. Besides supporting material identification and registration, the free and open-source e-tool may serve as template for other expert systems within the nanoscience domain.

Server-Focused Security Assessment of Mobile Health Apps for Popular Mobile Platforms.

BACKGROUND: The importance of mobile health (mHealth) apps is growing. Independent of the technologies used, mHealth apps bring more functionality into the hands of users. In the health context, mHealth apps play an important role in providing information and services to patients, offering health care professionals ways to monitor vital parameters or consult patients remotely. The importance of confidentiality in health care and the opaqueness of transport security in apps make the latter an important research subject. OBJECTIVE: This study aimed to (1) identify relevant security concerns on the server side of mHealth apps, (2) test a subset of mHealth apps regarding their vulnerability to those concerns, and (3) compare the servers used by mHealth apps with servers used in all domains. METHODS: Server security characteristics relevant to the security of mHealth apps were assessed, presented, and discussed. To evaluate servers, appropriate tools were selected. Apps from the Android and iOS app stores were selected and tested, and the results for functional and other backend servers were evaluated. RESULTS: The 60 apps tested communicate with 823 servers. Of these, 291 were categorized as functional backend servers, and 44 (44/291, 15.1%) of these received a rating below the A range (A+, A, and A-) by Qualys SSL Labs. A chi-square test was conducted against the number of servers receiving such ratings from SSL Pulse by Qualys SSL Labs. It was found that the tested servers from mHealth apps received significantly fewer ratings below the A range (P<.001). The internationally available apps from the test set performed significantly better than those only available in the German stores (alpha=.05; P=.03). Of the 60 apps, 28 (28/60, 47%) were found using at least one functional backend server that received a rating below the A range from Qualys SSL Labs, endangering confidentiality, authenticity, and integrity of the data displayed. The number of apps that used at least one entirely unsecured connection was 20 (20/60, 33%) when communicating with functional backend servers. It was also found that a majority of apps used advertising, tracking, or external content provider servers. When looking at all nonfunctional backend servers, 48 (48/60, 80%) apps used at least one server that received a rating below the A range. CONCLUSIONS: The results show that although servers in the mHealth domain perform significantly better regarding their security, there are still problems with the configuration of some. The most severe problems observed can expose patient communication with health care professionals, be exploited to display false or harmful information, or used to send data to an app facilitating further damage on the device. Following the recommendations for mHealth app developers, the most regularly observed security issues can be avoided or mitigated.

A Comparative Evaluation of SteamVR Tracking and the OptiTrack System for Medical Device Tracking.

Tracking of medical devices can be used in diverse situations, e.g., training as well as image guidance for surgery and surgery planning. Therefore, position and orientation of a device, for instance, an ultrasound probe, need to be identified as precisely as possible. This enables correct representation of digital 3D models in medical image processing platforms such as 3D Slicer or MevisLab. In this manuscript, a comparative evaluation of the low-cost Swept Angle Laser Tracking (SALT) system SteamVR Tracking and the multi-camera-based Opti-Track System is presented. Their potential for medical device tracking is demonstrated in the use case of ultrasound probe tracking for simulation purposes. An evaluation of tracking errors is performed using a Universal Robotics UR5 industrial robot under non-laboratory conditions, involving common issues such as reflections and occlusions. A discussion on the tracking accuracy of both systems is given. The communication of tracking data is established for 3D Slicer and MeVisLab with the use of the PLUS Toolkit via the OpenIGTLink protocol.