Uwe Petersohn

A divide and conquer strategy for the maximum likelihood localization of low intensity objects.

In cell biology and other fields the automatic accurate localization of sub-resolution objects in images is an important tool. The signal is often corrupted by multiple forms of noise, including excess noise resulting from the amplification by an electron multiplying charge-coupled device (EMCCD). Here we present our novel Nested Maximum Likelihood Algorithm (NMLA), which solves the problem of localizing multiple overlapping emitters in a setting affected by excess noise, by repeatedly solving the task of independent localization for single emitters in an excess noise-free system. NMLA dramatically improves scalability and robustness, when compared to a general purpose optimization technique. Our method was successfully applied for in vivo localization of fluorescent proteins.

Putting abox updates into action

When trying to apply recently developed approaches for updating Description Logic ABoxes in the context of an action programming language, one encounters two problems. First, updates generate so-called Boolean ABoxes, which cannot be handled by traditional Description Logic reasoners. Second, iterated update operations result in very large Boolean ABoxes, which, however, contain a huge amount of redundant information. In this paper, we address both issues from a practical point of view.

Wavelet Subspace Analysis of Intraoperative Thermal Imaging for Motion Filtering

Intraoperative thermography allows fast capturing of small temperature variations during neurosurgical operations. External influences induce periodic vibrational motion to the whole camera system superimposing signals of high-frequent neuronal activity, heart rate activity and injected perfusion tracers by motion artifacts. In this work, we propose a robust method to eliminate the effects induced by the vibrational motion allowing further inference of clinical information. For this purpose, an efficient wavelet shrinkage scheme is developed based on subspace analysis in 1D wavelet domain to recognize and remove motion related patterns. The approach does not require any specific motion modeling or image warping, making it fast and preventing image deformations. Promising results of a simulation study and by intraoperative measurements make this method a reliable and efficient method improving subsequent perfusion and neuronal activity analysis.

Motion correction of thermographic images in neurosurgery: Performance comparison

In this paper, the correction of motion-induced high frequency artifacts on sequences of thermographic images by a real-time capable approach based on Cellular Nonlinear Networks (CNN) is evaluated. For comparison, an offline analysis of frequency subspaces is presented and results will be compared. Both simulated data sets as well as data sets recorded during neurosurgery are considered as inputs and two measures are evaluated.

Learning Thermal Process Representations for Intraoperative Analysis of Cortical Perfusion During Ischemic Strokes

Thermal imaging is a non-invasive and marker-free approach for intraoperative measurements of small temperature variations. In this work, we demonstrate the abilities of active dynamic thermal imaging for analysis of tissue perfusion state in case of cerebral ischemia. For this purpose, a NaCl irrigation is applied to the exposed cortex during hemicraniectomy. The caused temperature changes are measured by a thermal imaging system whilst tissue heating is modeled by a double exponential function. Modeled temperature decay constants allow us to characterize tissue perfusion with respect to its dynamic thermal properties. As intraoperative imaging prevents the usage of computational intense parameter optimization schemes we discuss a deep learning framework that approximates these constants given a simple temperature sequence. The framework is compared to common Levenberg-Marquardt based parameter optimization approaches. The proposed deep parameter approximation framework shows good performance compared to numerical optimization with random initialization. We further validated the approximated parameters by an intraoperative case suffering acute cerebral ischemia. The results indicate that even approximated temperature decay constants allow us to quantify cortical perfusion. Latter yield a standardized representation of cortical thermodynamic properties and might guide further research regarding specific intraoperative therapies and characterization of pathologies with atypical cortical perfusion.

Motion correction of thermographic images in neurosurgery

Neurosurgery relies strongly on medical imaging techniques. However, many state-of-the-art diagnostic tools such as computer tomography (CT) and magneto-resonance imaging (MRI) cannot be applied during ongoing surgery in general. Our aim is to realize a multi-purpose imaging platform capable of real-time assistance to the surgeon. Therefore, we apply thermography: a non-invasive, contactless, marker-free technique that has been used in various medical applications, albeit some challenges remain. In this paper, we propose a Cellular Nonlinear Network-based image pre-processing for feature enhancing and extraction followed by a cepstrum-based motion correction algorithm allowing a real-time removal of breathing artifacts from thermographic images.

Large margin principle in hyperrectangle learning

In this paper, we propose a new meta learning approach to incorporate the large margin principle into hyperrectangle based learning. The goal of Large Margin Rectangle Learning (LMRL) is to combine the natural interpretability of hyperrectangle models like decision trees and rule learners with the risk minimization property related to the large margin principle. Our approach consists of two basic steps: supervised clustering and decision boundary creation. In the first step, we apply a supervised clustering algorithm to generate an initial rectangle based generalization of the training data. Subsequently, these labeled clusters are used to produce a large margin hyperrectangle model. Besides the overall approach, we also developed Large Margin Supervised Clustering (LMSC), an attempt to introduce the large margin principle directly into the supervised clustering process. Corresponding experiments not only provided empirical evidence for the supposed margin-accuracy relation, but also showed that LMRL performs equally well or better than compared decision tree and rule learner. Altogether, this new learning approach is a promising way to create more accurate interpretable models.

Unscharfe und fallbasierte Inferenzmethoden zur Vorhersage von Algenmassenentwicklungen

Okologische Systeme sind gekennzeichnet durch grose Komplexitat, hohe naturliche Dynamik, Stochastik und Periodik der Prozesse. Das Wissen uber viele Prozesse ist luckenhaft und unsicher. Differentialgleichungsmodellen, statistischen Ansatzen und klassischen Expertensystemen sind somit Grenzen gesetzt. Einen Ausweg bieten unscharfe Methoden. Der Vergleich eines PROLOG-FUZZY-Metainterpreters mit zwei Modellen auf der Basis der Dempster-Shafer-Evidenztheorie zeigt die prinzipielle Anwendbarkeit beider Methoden. Abschliesend wird die Erweiterung der wissensbasierten Algenmassenprognostik mit einem Analogie-Verfahren auf der Basis zeitlicher Zusammenhange von Ereignissen im Gewasser diskutiert.

Fast Mapping of the Eloquent Cortex by Learning L2 Penalties

The resection of brain tumors beneath eloquent areas of the human brain requires precise delineation of eloquent areas for maximum removal of tumor mass while minimizing the risk for postoperative functional deficits. Non-invasive mapping of eloquent areas can be carried out by intraoperative thermal imaging since neural activity alters the cortical temperature distribution. These characteristic changes in cortical temperature can be modeled by a response function. A prominent choice for this response function is the haemodynamic response function. However, the signal is typically superimposed by various effects such as motion artifacts, physiological effects, sensor drifts as well as autoregulation which have to be compensated.

Intraoperative mapping of the sensory cortex by time-resolved thermal imaging

Abstract The resection of brain tumor requires a precise distinction between eloquent areas of the brain and pathological tumor tissue in order to improve the extent of resection as well as the patient’s progression free survival time. In this study, we discuss mathematical tools necessary to recognize neural activity using thermal imaging cameras. The main contribution to thermal radiation of the exposed human cortex is regional cerebral blood flow (CBF). In fact, neurovascular coupling links neural activity to changes in regional CBF which in turn affects the cortical temperature. We propose a statistically sound framework to visualize neural activity of the primary somatosensory cortex. The framework incorporates a priori known experimental conditions such as the thermal response to neural activity as well as unrelated effects induced by random neural activity and autoregulation. These experimental conditions can be adopted to certain electrical stimulation protocols so that the framework allows to unveil arbitrary evoked neural activity. The method was applied to semisynthetic as well as two intraoperative cases with promising results as we were able to map the eloquent sensory cortex with high sensitivity. Furthermore, the results were validated by anatomical localization and electrophysiological measurements.