Placental vessel segmentation and registration in fetoscopy: Literature review and MICCAI FetReg2021 challenge findings.

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. For the segmentation task, overall baseline performed was the top performing (aggregated mIoU of 0.6763) and was the best on the vessel class (mIoU of 0.5817) while team RREB was the best on the tool (mIoU of 0.6335) and fetus (mIoU of 0.5178) classes. For the registration task, overall the baseline performed better than team SANO with an overall mean 5-frame SSIM of 0.9348. Qualitatively, it was observed that team SANO performed better in planar scenarios, while baseline was better in non-planner scenarios. The detailed analysis showed that no single team outperformed on all 6 test fetoscopic videos. The challenge provided an opportunity to create generalized solutions for fetoscopic scene understanding and mosaicking. In this paper, we present the findings of the FetReg2021 challenge, alongside reporting a detailed literature review for CAI in TTTS fetoscopy. Through this challenge, its analysis and the release of multi-center fetoscopic data, we provide a benchmark for future research in this field.

SATO (IDEAS expAnded wiTh BCIO): Workflow for designers of patient-centered mobile health behaviour change intervention applications.

Designing effective theory-driven digital behaviour change interventions (DBCI) is a challenging task. To ease the design process, and assist with knowledge sharing and evaluation of the DBCI, we propose the SATO (IDEAS expAnded wiTh BCIO) design workflow based on the IDEAS (Integrate, Design, Assess, and Share) framework and aligned with the Behaviour Change Intervention Ontology (BCIO). BCIO is a structural representation of the knowledge in behaviour change domain supporting evaluation of behaviour change interventions (BCIs) but it is not straightforward to utilise it during DBCI design. IDEAS (Integrate, Design, Assess, and Share) framework guides multi-disciplinary teams through the mobile health (mHealth) application development life-cycle but it is not aligned with BCIO entities. SATO couples BCIO entities with workflow steps and extends IDEAS Integrate stage with consideration of customisation and personalisation. We provide a checklist of the activities that should be performed during intervention planning with concrete examples and a tutorial accompanied with case studies from the Cancer Better Life Experience (CAPABLE) European project. In the process of creating this workflow, we found the necessity to extend the BCIO to support the scenarios of multiple clinical goals in the same application. To ensure the SATO steps are easy to follow for the incomers to the field, we performed a preliminary evaluation of the workflow with two knowledge engineers, working on novel mHealth app design tasks.

Can gamification reduce the burden of self-reporting in mHealth applications? A feasibility study using machine learning from smartwatch data to estimate cognitive load.

The effectiveness of digital treatments can be measured by requiring patients to self-report their state through applications, however, it can be overwhelming and causes disengagement. We conduct a study to explore the impact of gamification on self-reporting. Our approach involves the creation of a system to assess cognitive load (CL) through the analysis of photoplethysmography (PPG) signals. The data from 11 participants is utilized to train a machine learning model to detect CL. Subsequently, we create two versions of surveys: a gamified and a traditional one. We estimate the CL experienced by other participants (13) while completing surveys. We find that CL detector performance can be enhanced via pre-training on stress detection tasks. For 10 out of 13 participants, a personalized CL detector can achieve an F1 score above 0.7. We find no difference between the gamified and non-gamified surveys in terms of CL but participants prefer the gamified version.

Environmental and Production Aspects of Using Fertilizers Based on Waste Elemental Sulfur and Organic Materials.

Crop fertilization with sulfur is an important part of agricultural practices, as is the systematic increase in soil organic matter content. Materials of waste origin constitute a source of plant-available sulfur, as well as soil organic matter. The study was to verify the hypothesis assuming that combining waste sulfur pulp and its mixtures with organic materials enables simultaneous soil enrichment with readily available sulfur and organic matter. A 240-day incubation experiment was conducted, on two soils: very light and heavy; with two sulfur doses applied to each soil (20 and 40 mg S/kg d.m. for very light soil, and 30 and 60 mg S/kg d.m. for heavy soil). The sulfate sulfur content in the incubated soil material, treated with the addition of sulfur pulp and its mixtures with organic materials, increased significantly up to day 60 and then decreased. The application of these materials significantly increased the content of available sulfur and decreased the pH value of the incubated material. The effect of the introduced materials on dehydrogenase activity depended on soil granulometric composition (the impact of the applied materials on the activity of these enzymes in very light soil was small, and in heavy soil, their activity was usually limited by the presence of introduced materials). Application of the studied materials had little effect on the total organic carbon content in the incubated soil material (a significant change in the value of this parameter, in relation to the control soil, was recorded in some treatments of heavy soil).

Deep learning fetal ultrasound video model match human observers in biometric measurements.

Objective.This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circumference and femur length, and to estimate gestational age and fetal weight using fetal ultrasound videos.Approach.We developed a novel multi-task CNN-based spatio-temporal fetal US feature extraction and standard plane detection algorithm (called FUVAI) and evaluated the method on 50 freehand fetal US video scans. We compared FUVAI fetal biometric measurements with measurements made by five experienced sonographers at two time points separated by at least two weeks. Intra- and inter-observer variabilities were estimated.Main results.We found that automated fetal biometric measurements obtained by FUVAI were comparable to the measurements performed by experienced sonographers The observed differences in measurement values were within the range of inter- and intra-observer variability. Moreover, analysis has shown that these differences were not statistically significant when comparing any individual medical expert to our model.Significance.We argue that FUVAI has the potential to assist sonographers who perform fetal biometric measurements in clinical settings by providing them with suggestions regarding the best measuring frames, along with automated measurements. Moreover, FUVAI is able perform these tasks in just a few seconds, which is a huge difference compared to the average of six minutes taken by sonographers. This is significant, given the shortage of medical experts capable of interpreting fetal ultrasound images in numerous countries.

Preparation, Characterization of Granulated Sulfur Fertilizers and Their Effects on a Sandy Soils.

There is a potential for using sulfur waste in agriculture. The main objective of this study was to design a granular fertilizer based on waste elemental sulfur. Humic acids and halloysite were used to improve the properties and their influence on soil properties. This is the first report on the use of proposed materials for fertilizer production. The following granular fertilizers were prepared (the percentage share of component weight is given in brackets): fertilizer A (waste sulfur (95%) + halloysite (5%)), fertilizer B (waste sulfur (81%) + halloysite (5%) + humic acids (14%)), fertilizer C (waste sulfur (50%) + halloysite (50%)) and fertilizer D (waste sulfur (46%) + halloysite (46%) + humic acids (8%)). Basic properties of the obtained granulates were determined. Furthermore, the effect of the addition of the prepared fertilizers on soil pH, electrolytic conductivity, and sulfate content was examined in a 90-day incubation experiment. Enrichment with humic acids and the higher amount of halloysite increased the fertilizer properties (especially the share of larger granules and bulk density). In addition, it stabilized soil pH and increased the sulfur content (extracted with 0.01 mol·L-1 CaCl2 and Mehlich 3) in the soil.

An experimental assessment of five indices of retinal vessel tortuosity with the RET-TORT public dataset.

We compare the performance of five indices of retinal vessel tortuosity against sampling rates of vessel centerlines. We consider distance measure, tortuosity density, two curvature-based measures, and a recently introduced slope-chain coding for general curves, never before assessed comparatively with retinal vessels. To enable replication of our results, we use the public dataset for retinal tortuosity, RET-TORT. We find that (1) the tortuosity density index offers good performance overall, but is not always the best performer; (2) curvature-based methods are the best if high-frequency resampling is possible, but (3) are the most sensitive to variations of the number of samples; (4) slope-chain coding performs best at low sampling rates, but the length of the linear elements must be chosen carefully. In general, performance may vary considerably with resampling, suggesting that the choice of a tortuosity index for clinical inference requires attention to numerical details, and ideally standardization thereof.