Sebastian Schäfer

Bio: Sebastian Schäfer is an academic researcher from Otto-von-Guericke University Magdeburg. The author has contributed to research in topics: Motion compensation & Image registration. The author has an hindex of 3, co-authored 8 publications receiving 56 citations.

Motion compensation of ultrasonic perfusion images

Abstract: Contrast-enhanced ultrasound (CEUS) is a rapid and inexpensive medical imaging technique to assess tissue perfusion with a high temporal resolution. It is composed of a sequence with ultrasound brightness values and a contrast sequence acquired simultaneously. However, the image acquisition is disturbed by various motion influences. Registration is needed to obtain reliable information of spatial correspondence and to analyze perfusion characteristics over time. We present an approach to register an ultrasonography sequence by using a feature label map. This label map is generated from the b-mode data sequence by a Markov-Random-Field (MRF) based analysis, where each location is assigned to one of the user-defined regions according to its statistical parameters. The MRF reduces the chance that outliers are represented in the label map and provides stable feature labels over the time frames. A registration consisting of rigid and non-rigid transformations is determined consecutively using the generated label map of the respective frames for similarity calculation. For evaluation, the standard deviation within specific regions in intestinal CEUS images has been measured before and after registration resulting in an average decrease of 8.6 %. Additionally, this technique has proven to be more robust against noise influence compared to similarity calculation based on image intensities only. The latter leads only to 7.6 % decrease of the standard deviation.

Registration of ultrasonography sequences based on temporal regions

Abstract: 2D Ultrasonography images with parallel contrast enhanced sequences for analysis constitute a rapid and inexpensive imaging technique with high temporal resolution to assess perfusion of tissue. However, motion from various influences corrupts the inter-pixel correspondences between different time frames and therefore hampers computer-assisted analysis of perfusion parameters. We present a user-supported method applying a temporal similarity matrix to remove frames with uncorrectable out-of-plane motion. For the remaining regions of frames, motion influence can be compensated for by image registration. Subsequently B-Spline based registration is applied using the temporal regions with automatic determination of a suitable reference frame image. Evaluation with ground truth data of six datasets comparing a medical expert frame analysis to the proposed technique yields 85.1 % sensitivity and 81.7 % specificity in average. On average 6 % of the frames have been erroneously included in temporal regions, although they contain out-of-plane motion.

Registration of Temporal Ultrasonic Image Sequences Using Markov Random Fields

Abstract: Ultrasound perfusion imaging is a rapid and inexpensive technique which enables observation of a dynamic process with high temporal resolution. The image acquisition is disturbed by various motion influences due to the acquisition procedure and patient motion. To extract valid information about perfusion for quantification and diagnostic purposes this influence must be compensated. In this work an approach to account for non-linear motion using a markov random field (MRF) based optimization scheme for registration is presented. Optimal transformation parameters are found all at once in a single optimization framework. Spatial and temporal constraints ensure continuity of a displacement field which is used for image transformation. Simulated datasets with known transformation fields are used to evaluate the presented method and demonstrate the potential of the system. Experiments with patient datasets show that superior results could be achieved compared to a pairwise image registration approach. Furthermore, it is shown that the method is suited to include prior knowledge about the data as the MRF system is able to model dependencies between the parameters of the optimization process.

Models and methods for analyzing DCE-MRI: A review

Abstract: Purpose: To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches. Methods: DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA’s perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration–time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases. Results: Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors. Conclusions: Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

Visual Analytics: A Comprehensive Overview

Abstract: With the ever-increasing amount of data, the world has stepped into the era of “Big Data”. Presently, the analysis of massive and complex data and the extraction of relevant information, have been become essential tasks in many fields of studies, such as health, biology, chemistry, social science, astronomy, and physics. However, compared with the development of data storage and management technologies, our ability to gain useful information from the collected data does not match our ability to collect the data. This gap has led to a surge of research activity in the field of visual analytics. Visual analytics employs interactive visualization to integrate human judgment into algorithmic data-analysis processes. In this paper, the aim is to draw a complete picture of visual analytics to direct future research by examining the related research in various application domains. As such, a novel categorization of visual-analytics applications from a technical perspective is proposed, which is based on the dimensionality of visualization and the type of interaction. Based on this categorization, a comprehensive survey of visual analytics is performed, which examines its evolution from visualization and algorithmic data analysis, and investigates how it is applied in various application domains. In addition, based on the observations and findings gained in this survey, the trends, major challenges, and future directions of visual analytics are discussed.

Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data

Abstract: Facetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images of human tumors and tissues. Such images represent the cutting edge of digital histology and promise to revolutionize how diseases such as cancer are studied, diagnosed, and treated. Highly multiplexed tissue images are complex, comprising 109 or more pixels, 60-plus channels, and millions of individual cells. This makes manual analysis challenging and error-prone. Existing automated approaches are also inadequate, in large part, because they are unable to effectively exploit the deep knowledge of human tissue biology available to anatomic pathologists. To overcome these challenges, Facetto enables a semi-automated analysis of cell types and states. It integrates unsupervised and supervised learning into the image and feature exploration process and offers tools for analytical provenance. Experts can cluster the data to discover new types of cancer and immune cells and use clustering results to train a convolutional neural network that classifies new cells accordingly. Likewise, the output of classifiers can be clustered to discover aggregate patterns and phenotype subsets. We also introduce a new hierarchical approach to keep track of analysis steps and data subsets created by users; this assists in the identification of cell types. Users can build phenotype trees and interact with the resulting hierarchical structures of both high-dimensional feature and image spaces. We report on use-cases in which domain scientists explore various large-scale fluorescence imaging datasets. We demonstrate how Facetto assists users in steering the clustering and classification process, inspecting analysis results, and gaining new scientific insights into cancer biology.