Grading of Aortic Stenosis: Is it More Complicated in Women?

Aortic stenosis (AS) is the most common valvular heart disease and the main indication for valvular replacement in older women. Correct AS grading is mandatory for an adequate selection of patients for both surgical and transcatheter aortic valve replacement. Women and men have different AS severity grades at the same level of aortic valve calcification. Moreover, besides having smaller cardiac volumes, left ventricular outflow tract and aortic size, women have a specific pattern of left ventricular structural and functional remodelling in response to the AS-related chronic pressure overload. Here, the sex-specific cardiac changes in AS that make AS grading more challenging in women, with consequences for the management and outcome of this group of patients, are reviewed.publishedVersio

NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mapping

We present a novel 3D mapping method leveraging the recent progress in neural implicit representation for 3D reconstruction. Most existing state-of-the-art neural implicit representation methods are limited to object-level reconstructions and can not incrementally perform updates given new data. In this work, we propose a fusion strategy and training pipeline to incrementally build and update neural implicit representations that enable the reconstruction of large scenes from sequential partial observations. By representing an arbitrarily sized scene as a grid of latent codes and performing updates directly in latent space, we show that incrementally built occupancy maps can be obtained in real-time even on a CPU. Compared to traditional approaches such as Truncated Signed Distance Fields (TSDFs), our map representation is significantly more robust in yielding a better scene completeness given noisy inputs. We demonstrate the performance of our approach in thorough experimental validation on real-world datasets with varying degrees of added pose noise.Comment: 3DV 2021. Equal contribution between the first two authors. Code: https://github.com/ethz-asl/neuralblo

Local and Global Information in Obstacle Detection on Railway Tracks

Reliable obstacle detection on railways could help prevent collisions that result in injuries and potentially damage or derail the train. Unfortunately, generic object detectors do not have enough classes to account for all possible scenarios, and datasets featuring objects on railways are challenging to obtain. We propose utilizing a shallow network to learn railway segmentation from normal railway images. The limited receptive field of the network prevents overconfident predictions and allows the network to focus on the locally very distinct and repetitive patterns of the railway environment. Additionally, we explore the controlled inclusion of global information by learning to hallucinate obstacle-free images. We evaluate our method on a custom dataset featuring railway images with artificially augmented obstacles. Our proposed method outperforms other learning-based baseline methods

SegMap: 3D Segment Mapping using Data-Driven Descriptors

When performing localization and mapping, working at the level of structure can be advantageous in terms of robustness to environmental changes and differences in illumination. This paper presents SegMap: a map representation solution to the localization and mapping problem based on the extraction of segments in 3D point clouds. In addition to facilitating the computationally intensive task of processing 3D point clouds, working at the level of segments addresses the data compression requirements of real-time single- and multi-robot systems. While current methods extract descriptors for the single task of localization, SegMap leverages a data-driven descriptor in order to extract meaningful features that can also be used for reconstructing a dense 3D map of the environment and for extracting semantic information. This is particularly interesting for navigation tasks and for providing visual feedback to end-users such as robot operators, for example in search and rescue scenarios. These capabilities are demonstrated in multiple urban driving and search and rescue experiments. Our method leads to an increase of area under the ROC curve of 28.3% over current state of the art using eigenvalue based features. We also obtain very similar reconstruction capabilities to a model specifically trained for this task. The SegMap implementation will be made available open-source along with easy to run demonstrations at www.github.com/ethz-asl/segmap. A video demonstration is available at https://youtu.be/CMk4w4eRobg

COVID-19 myocarditis and postinfection Bell’s palsy

Here we present the case of a 37-year-old previously healthy man who developed fever, headache and a unilateral, painful neck swelling while working offshore. He had no known contact with anyone with COVID-19; however, due to the ongoing pandemic, a nasopharyngeal swab was performed, which was positive for the virus. After transfer to hospital for assessment his condition rapidly deteriorated, requiring admission to intensive care for COVID-19 myocarditis. One week after discharge he re-presented with unilateral facial nerve palsy. Our case highlights an atypical presentation of COVID-19 and the multifaceted clinical course of this still poorly understood disease.publishedVersio