3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction

Past few years have witnessed the prevalence of deep learning in many application scenarios, among which is medical image processing. Diagnosis and treatment of brain tumors requires an accurate and reliable segmentation of brain tumors as a prerequisite. However, such work conventionally requires brain surgeons significant amount of time. Computer vision techniques could provide surgeons a relief from the tedious marking procedure. In this paper, a 3D U-net based deep learning model has been trained with the help of brain-wise normalization and patching strategies for the brain tumor segmentation task in the BraTS 2019 competition. Dice coefficients for enhancing tumor, tumor core, and the whole tumor are 0.737, 0.807 and 0.894 respectively on the validation dataset. These three values on the test dataset are 0.778, 0.798 and 0.852. Furthermore, numerical features including ratio of tumor size to brain size and the area of tumor surface as well as age of subjects are extracted from predicted tumor labels and have been used for the overall survival days prediction task. The accuracy could be 0.448 on the validation dataset, and 0.551 on the final test dataset.Comment: Third place award of the 2019 MICCAI BraTS challenge survival task [BraTS 2019](https://www.med.upenn.edu/cbica/brats2019.html

TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks

Glioma is one of the most common types of brain tumors; it arises in the glial cells in the human brain and in the spinal cord. In addition to having a high mortality rate, glioma treatment is also very expensive. Hence, automatic and accurate segmentation and measurement from the early stages are critical in order to prolong the survival rates of the patients and to reduce the costs of the treatment. In the present work, we propose a novel end-to-end cascaded network for semantic segmentation that utilizes the hierarchical structure of the tumor sub-regions with ResNet-like blocks and Squeeze-and-Excitation modules after each convolution and concatenation block. By utilizing cross-validation, an average ensemble technique, and a simple post-processing technique, we obtained dice scores of 88.06, 80.84, and 80.29, and Hausdorff Distances (95th percentile) of 6.10, 5.17, and 2.21 for the whole tumor, tumor core, and enhancing tumor, respectively, on the online test set.Comment: Accepted at MICCAI BrainLes 201

Convolutional 3D to 2D Patch Conversion for Pixel-wise Glioma Segmentation in MRI Scans

Structural magnetic resonance imaging (MRI) has been widely utilized for analysis and diagnosis of brain diseases. Automatic segmentation of brain tumors is a challenging task for computer-aided diagnosis due to low-tissue contrast in the tumor subregions. To overcome this, we devise a novel pixel-wise segmentation framework through a convolutional 3D to 2D MR patch conversion model to predict class labels of the central pixel in the input sliding patches. Precisely, we first extract 3D patches from each modality to calibrate slices through the squeeze and excitation (SE) block. Then, the output of the SE block is fed directly into subsequent bottleneck layers to reduce the number of channels. Finally, the calibrated 2D slices are concatenated to obtain multimodal features through a 2D convolutional neural network (CNN) for prediction of the central pixel. In our architecture, both local inter-slice and global intra-slice features are jointly exploited to predict class label of the central voxel in a given patch through the 2D CNN classifier. We implicitly apply all modalities through trainable parameters to assign weights to the contributions of each sequence for segmentation. Experimental results on the segmentation of brain tumors in multimodal MRI scans (BraTS'19) demonstrate that our proposed method can efficiently segment the tumor regions

The DWD climate predictions website: Towards a seamless outlook based on subseasonal, seasonal and decadal predictions

The climate predictions website of the Deutscher Wetterdienst (DWD, https://www.dwd.de/climatepredictions) presents a consistent operational outlook for the coming weeks, months and years, focusing on the needs of German users. At global scale, subseasonal predictions from the European Centre of Medium-Range Weather Forecasts as well as seasonal and decadal predictions from the DWD are used. Statistical downscaling is applied to achieve high resolution over Germany. Lead-time dependent bias correction is performed on all time scales. Additionally, decadal predictions are recalibrated. The website offers ensemble mean and probabilistic predictions for temperature and precipitation combined with their skill (mean squared error skill score, ranked probability skill score). Two levels of complexity are offered: basic climate predictions display simple, regionally averaged information for Germany, German regions and cities as maps, time series and tables. The skill is presented as traffic light. Expert climate predictions show complex, gridded predictions for Germany (at high resolution), Europe and the world as maps and time series. The skill is displayed as the size of dots. Their color is related to the signal in the prediction. The website was developed in cooperation with users from different sectors via surveys, workshops and meetings to guarantee its understandability and usability. The users realize the potential of climate predictions, but some need advice in using probabilistic predictions and skill. Future activities will include the further development of predictions to improve skill (multi-model ensembles, teleconnections), the introduction of additional products (data provision, extremes) and the further clarification of the information (interactivity, video clips)

[Work in progress] Scalable, out-of-the box segmentation of individual particles from mineral samples acquired with micro CT

Minerals are indispensable for a functioning modern society. Yet, their supply is limited causing a need for optimizing their exploration and extraction both from ores and recyclable materials. Typically, these processes must be meticulously adapted to the precise properties of the processed particles, an extensive characterization of their shapes, appearances as well as the overall material composition. Current approaches perform this analysis based on bulk segmentation and characterization of particles imaged with a micro CT, and rely on rudimentary postprocessing techniques to separate touching particles. However, due to their inability to reliably perform this separation as well as the need to retrain or reconfigure methods for each new image, these approaches leave untapped potential to be leveraged. Here, we propose ParticleSeg3D, an instance segmentation method that is able to extract individual particles from large micro CT images taken from mineral samples embedded in an epoxy matrix. Our approach is based on the powerful nnU-Net framework, introduces a particle size normalization, makes use of a border-core representation to enable instance segmentation and is trained with a large dataset containing particles of numerous different materials and minerals. We demonstrate that ParticleSeg3D can be applied out-of-the box to a large variety of particle types, including materials and appearances that have not been part of the training set. Thus, no further manual annotations and retraining are required when applying the method to new mineral samples, enabling substantially higher scalability of experiments than existing methods. Our code and dataset are made publicly available

Test-time Unsupervised Domain Adaptation

Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain). This motivates the active field of domain adaptation. While some approaches to the problem require labeled data from the target domain, others adopt an unsupervised approach to domain adaptation (UDA). Evaluating UDA methods consists of measuring the model's ability to generalise to unseen data in the target domain. In this work, we argue that this is not as useful as adapting to the test set directly. We therefore propose an evaluation framework where we perform test-time UDA on each subject separately. We show that models adapted to a specific target subject from the target domain outperform a domain adaptation method which has seen more data of the target domain but not this specific target subject. This result supports the thesis that unsupervised domain adaptation should be used at test-time, even if only using a single target-domain subjectComment: Accepted at MICCAI 202

Comparison of a classical with a highly formularized body condition scoring system for dairy cattle

Body condition scoring is a common tool to assess the subcutaneous fat reserves of dairy cows. Because of its subjectivity, which causes limits in repeatability, it is often discussed controversially. Aim of the current study was to evaluate the impact of considering the cows overall appearance on the scoring process and on the validity of the results. Therefore, two different methods to reveal body condition scores (BCS), ‘independent BCS' (iBCS) and ‘dependent BCS' (dBCS), were used to assess 1111 Swiss Brown Cattle. The iBCS and the dBCS systems were both working with the same flowchart with a decision tree structure for visual and palpatory assessment using a scale from 2 to 5 with increment units of 0.25. The iBCS was created strictly complying with the defined frames of the decision tree structure. The system was chosen due to its formularized approach to reduce the influence of subjective impressions. By contrast, the dBCS system, which was in line with common practice, had a more open approach, where - besides the decision tree - the overall impression of the cow's physical appearance was taken into account for generating the final score. Ultrasound measurement of the back fat thickness (BFT) was applied as a validation method. The dBCS turned out to be the better predictor of BFT, explaining 67.3% of the variance. The iBCS was only able to explain 47.3% of the BFT variance. Within the whole data set, only 31.3% of the animals received identical dBCS and iBCS. The pin bone region caused the most deviations between dBCS and iBCS, but also assessing the pelvis line, the hook bones and the ligaments led to divergences in around 20% of the scored animals. The study showed that during the assessment of body condition a strict adherence to a decision tree is a possible source of inexact classifications. Some body regions, especially the pin bones, proved to be particularly challenging for scoring due to difficulties in assessing them. All the more, the inclusion of the overall appearance of the cow into the assessment process counteracted these errors and led to a fair predictability of BFT with the flowchart-based BCS. This might be particularly important, if different cattle types and breeds are assesse

Unifying biological field observations to detect and compare ocean acidification impacts across marine species and ecosystems: what to monitor and why

Abstract. Approximately one-quarter of the CO2 emitted to the atmosphere annually from human activities is absorbed by the ocean, resulting in a reduction of seawater pH and shifts in seawater carbonate chemistry. This multidecadal process, termed “anthropogenic ocean acidification” (OA), has been shown to have detrimental impacts on marine ecosystems. Recent years have seen a globally coordinated effort to measure the changes in seawater chemistry caused by OA, with best practices now available for these measurements. In contrast to these substantial advances in observing physicochemical changes due to OA, quantifying their biological consequences remains challenging, especially from in situ observations under real-world conditions. Results from 2 decades of controlled laboratory experiments on OA have given insight into the likely processes and mechanisms by which elevated CO2 levels affect biological process, but the manifestation of these process across a plethora of natural situations has yet to be fully explored. This challenge requires us to identify a set of fundamental biological and ecological indicators that are (i) relevant across all marine ecosystems, (ii) have a strongly demonstrated link to OA, and (iii) have implications for ocean health and the provision of ecosystem services with impacts on local marine management strategies and economies. This paper draws on the understanding of biological impacts provided by the wealth of previous experiments, as well as the findings of recent meta-analyses, to propose five broad classes of biological indicators that, when coupled with environmental observations including carbonate chemistry, would allow the rate and severity of biological change in response to OA to be observed and compared. These broad indicators are applicable to different ecological systems, and the methods for data analysis suggested here would allow researchers to combine biological response data across regional and global scales by correlating rates of biological change with the rate of change in carbonate chemistry parameters. Moreover, a method using laboratory observation to design an optimal observing strategy (frequency and duration) and observe meaningful biological rates of change highlights the factors that need to be considered when applying our proposed observation strategy. This innovative observing methodology allows inclusion of a wide diversity of marine ecosystems in regional and global assessments and has the potential to increase the contribution of OA observations from countries with developing OA science capacity

Impacts of feeding less food-competing feedstuffs to livestock on global food system sustainability

Funding Christian Schader, Adrian Muller, Nadia El-Hage Scialabba, Judith Hecht, Anne Isensee, Harinder P.S. Makkar, Peter Klocke, Florian Leiber, Matthias Stolze, Urs Niggli thank FAO for funding this research. K.E. gratefully acknowledges funding from ERC-2010-Stg-263522 LUISE. Additional data and method details are provided in the supplementary materials. The contribution of P.S. is supported by funding from the Belmont Forum-FACCE-JPI Project ‘Delivering Food on Limited Land’ (DEVIL), with the UK contribution supported by NERC (NE/M021327/1).Peer reviewedPublisher PD