Reconstructing and analyzing the invariances of low-dose CT image denoising networks.

Background: Deep learning-based methods led to significant advancements in many areas of medical imaging, most of which are concerned with the reduction of artifacts caused by motion, scatter, or noise. However, with most neural networks being black boxes, they remain notoriously difficult to interpret, hindering their clinical implementation. In particular, it has been shown that networks exhibit invariances w.

Benchmarking deep learning-based low-dose CT image denoising algorithms.

Background: Long-lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography (CT) acquisitions without severe deterioration of image quality. To this end, various techniques have been employed over the years including iterative reconstruction methods and noise reduction algorithms.

Purpose: Recently, deep learning-based methods for noise reduction became increasingly popular and a multitude of papers claim ever improving performance both quantitatively and qualitatively.

Neuropathic pain caused by miswiring and abnormal end organ targeting.

Nerve injury leads to chronic pain and exaggerated sensitivity to gentle touch (allodynia) as well as a loss of sensation in the areas in which injured and non-injured nerves come together. The mechanisms that disambiguate these mixed and paradoxical symptoms are unknown. Here we longitudinally and non-invasively imaged genetically labelled populations of fibres that sense noxious stimuli (nociceptors) and gentle touch (low-threshold afferents) peripherally in the skin for longer than 10 months after nerve injury, while simultaneously tracking pain-related behaviour in the same mice.

Large-scale interactive retrieval in art collections using multi-style feature aggregation.

Finding objects and motifs across artworks is of great importance for art history as it helps to understand individual works and analyze relations between them. The advent of digitization has produced extensive digital art collections with many research opportunities. However, manual approaches are inadequate to handle this amount of data, and it requires appropriate computer-based methods to analyze them.

Improving Deep Metric Learning by Divide and Conquer.

Deep metric learning (DML) is a cornerstone of many computer vision applications. It aims at learning a mapping from the input domain to an embedding space, where semantically similar objects are located nearby and dissimilar objects far from another. The target similarity on the training data is defined by user in form of ground-truth class labels.

Deep learning of cuneiform sign detection with weak supervision using transliteration alignment.

The cuneiform script provides a glimpse into our ancient history. However, reading age-old clay tablets is time-consuming and requires years of training. To simplify this process, we propose a deep-learning based sign detector that locates and classifies cuneiform signs in images of clay tablets.

Sharing Matters for Generalization in Deep Metric Learning.

Learning the similarity between images constitutes the foundation for numerous vision tasks. The common paradigm is discriminative metric learning, which seeks an embedding that separates different training classes. However, the main challenge is to learn a metric that not only generalizes from training to novel, but related, test samples.

Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats.

The majority of stroke patients develop post-stroke fatigue, a symptom which impairs motivation and diminishes the success of rehabilitative interventions. We show that large cortical strokes acutely reduce activity levels in rats for 1-2 weeks as a physiological response paralleled by signs of systemic inflammation. Rats were exposed early (1-2 weeks) or late (3-4 weeks after stroke) to an individually monitored enriched environment to stimulate self-controlled high-intensity sensorimotor training.

Optogenetically stimulating intact rat corticospinal tract post-stroke restores motor control through regionalized functional circuit formation.

Current neuromodulatory strategies to enhance motor recovery after stroke often target large brain areas non-specifically and without sufficient understanding of their interaction with internal repair mechanisms. Here we developed a novel therapeutic approach by specifically activating corticospinal circuitry using optogenetics after large strokes in rats. Similar to a neuronal growth-promoting immunotherapy, optogenetic stimulation together with intense, scheduled rehabilitation leads to the restoration of lost movement patterns rather than induced compensatory actions, as revealed by a computer vision-based automatic behavior analysis.

Optimization of the Surgical Approach in AVMs Using MRI and 4D DSA Fusion Technique : A technical note.

Purpose: Previously published data demonstrated the possibility of displaying the angioarchitecture of intracranial vascular malformations using time-resolved 3D imaging (4D digital subtraction angiography [DSA]). The purpose of our study was to prove the technical feasibility of creating fused images of time-resolved 3D reconstructions and MPRAGE MRI data sets and to check the reliability of the correct anatomical display of the angioma nidus and the venous drainage in the fused images of patients with intracranial arteriovenous malformations (AVM).

Patients And Methods: In this study 20 patients with intracranial AVM underwent pretherapeutic DSA and time-resolved 3D DSA in addition to MRI including MPRAGE sequences.

Beyond the Sum of Parts: Voting with Groups of Dependent Entities.

The high complexity of multi-scale, category-level object detection in cluttered scenes is efficiently handled by Hough voting methods. However, the main shortcoming of the approach is that mutually dependent local observations are independently casting their votes for intrinsically global object properties such as object scale. Object hypotheses are then assumed to be a mere sum of their part votes.

Event detection by feature unpredictability in phase-contrast videos of cell cultures.

In this work we propose a novel framework for generic event monitoring in live cell culture videos, built on the assumption that unpredictable observations should correspond to biological events. We use a small set of event-free data to train a multioutput multikernel Gaussian process model that operates as an event predictor by performing autoregression on a bank of heterogeneous features extracted from consecutive frames of a video sequence. We show that the prediction error of this model can be used as a probability measure of the presence of relevant events, that can enable users to perform further analysis or monitoring of large-scale non-annotated data.

Neuronal repair. Asynchronous therapy restores motor control by rewiring of the rat corticospinal tract after stroke.

The brain exhibits limited capacity for spontaneous restoration of lost motor functions after stroke. Rehabilitation is the prevailing clinical approach to augment functional recovery, but the scientific basis is poorly understood. Here, we show nearly full recovery of skilled forelimb functions in rats with large strokes when a growth-promoting immunotherapy against a neurite growth-inhibitory protein was applied to boost the sprouting of new fibers, before stabilizing the newly formed circuits by intensive training.

Learning the compositional nature of visual object categories for recognition.

Real-world scene understanding requires recognizing object categories in novel visual scenes. This paper describes a composition system that automatically learns structured, hierarchical object representations in an unsupervised manner without requiring manual segmentation or manual object localization. A central concept for learning object models in the challenging, general case of unconstrained scenes, large intraclass variations, large numbers of categories, and lacking supervision information is to exploit the compositional nature of our (visual) world.