Automated Design of Deep Learning Methods for Biomedical Image Segmentation

Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning. While semantic segmentation algorithms enable 3D image analysis and quantification in many applications, the design of respective specialised solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We propose nnU-Net, a deep learning framework that condenses the current domain knowledge and autonomously takes the key decisions required to transfer a basic architecture to different datasets and segmentation tasks. Without manual tuning, nnU-Net surpasses most specialised deep learning pipelines in 19 public international competitions and sets a new state of the art in the majority of the 49 tasks. The results demonstrate a vast hidden potential in the systematic adaptation of deep learning methods to different datasets. We make nnU-Net publicly available as an open-source tool that can effectively be used out-of-the-box, rendering state of the art segmentation accessible to non-experts and catalyzing scientific progress as a framework for automated method design.Comment: * Fabian Isensee and Paul F. J\"ager share the first authorshi

IL-15 modulates the effect of retinoic acid, promoting inflammation rather than oral tolerance to dietary antigens

3 páginas.-- Evaluation of: DePaolo RW, Abadie V, Tang F et al. Co-adjuvant effects of retinoic acid and IL-15 induce inflammatory immunity to dietary antigens. Nature 471(7337), 220–224 (2011).The physiological immune response in the intestine against dietary proteins and commensal flora is characterized by regulatory mechanisms (tolerance) that prevent harmful consequences. Intestinal dendritic cells (DCs) have a central role in the development of immunosuppressive regulatory T cells owing to their ability to produce TGF-b and retinoic acid (RA). However, the article under evaluation shows an unexpected effect of RA – that of promoting a proinflammatory phenotype in intestinal DCs involved in the generation of inflammatory immune responses to dietary antigens. By using a double transgenic murine model that resembles human celiac disease, it was demonstrated that RA synergizes with IL‑15 in promoting the breakdown of gluten tolerance and the development of enteropathy. The tissue microenvironment modulates DC function, and immune therapies that are based on RA aiming to restore oral tolerance should be used with caution because the presence of IL‑15 (and/or other proinflammatory cytokines) may have undesirable effects.Peer reviewe

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

Evidence for the formation of comet 67P/Churyumov-Gerasimenko through gravitational collapse of a bound clump of pebbles

The processes that led to the formation of the planetary bodies in the Solar System are still not fully understood. Using the results obtained with the comprehensive suite of instruments on-board ESA’s Rosetta mission, we present evidence that comet 67P/Churyumov-Gerasimenko likely formed through the gentle gravitational collapse of a bound clump of mm-sized dust aggregates (“pebbles”), intermixed with microscopic ice particles. This formation scenario leads to a cometary make-up that is simultaneously compatible with the global porosity, homogeneity, tensile strength, thermal inertia, vertical temperature profiles, sizes and porosities of emitted dust, and the steep increase in water-vapour production rate with decreasing heliocentric distance, measured by the instruments on-board the Rosetta spacecraft and the Philae lander. Our findings suggest that the pebbles observed to be abundant in protoplanetary discs around young stars provide the building material for comets and other minor bodies

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