A Framework for Semi-automatic Fiducial Localization in Volumetric Images

Fiducial localization in volumetric images is a common task performed by image-guided navigation and augmented reality systems. These systems often rely on fiducials for image-space to physical-space registration, or as easily identifiable structures for registration validation purposes. Automated methods for fiducial localization in volumetric im- ages are available. Unfortunately, these methods are not generalizable as they explicitly utilize strong a priori knowledge such as fiducial intensity values in CT, or known spatial configurations as part of the algorithm. Thus, manual localization has remained the most general approach, read- ily applicable across fiducial types and imaging modalities. The main drawbacks of manual localization are the variability and accuracy errors associated with visual localization. We describe a semi-automatic fiducial localization approach that combines the strengths of the human opera- tor and an underlying computational system. The operator identifies the rough location of the fiducial, and the computational system accurately localizes it via intensity based registration using the mutual information similarity measure. This approach is generic, implicitly accommodating for all fiducial types and imaging modalities. The framework was evalu- ated using five fiducial types and three imaging modalities. We obtained a maximal localization accuracy error of 0.35mm, with a maximal preci- sion variability of 0.5mm

Navigated interventions in the head and neck area: standardized assessment of a new handy field generator

Electromagnetic (EM) tracking enables localization of surgical instruments within the magnetic field emitted by an EM field generator (FG). Usually, the larger a FG is, the larger its tracking volume is. However, the company NDI (Northern Digital Inc., Waterloo, ON, Canada) recently introduced the Planar 10-11 FG, which combines a compact construction (97mm x 112mm x 31mm) with a relatively large, cylindrical tracking volume (diameter: 340mm, height: 340mm). Using the standardized assessment protocol of Hummel et al., the FG was tested with regard to its tracking accuracy and to its robustness with respect to external sources of disturbance. The mean positional error (5cm distance metric according to Hummel protocol) was 0.59mm, with a mean jitter of 0.26mm in the standard setup. The mean orientational error was found to be 0.10{\deg}. The highest positional error (4.82mm) due to metallic sources of disturbance was caused by the steel SST 303. In contrast, steel SST 416 caused the lowest positional error (0.10mm). Overall, the Planar 10-11 FG tends to achieve better tracking accuracy results compared to other NDI FGs. Due to its compact construction and portability, the FG could contribute to increased clinical use of EM tracking systems.Comment: This is the preprint version of the BVM paper already published in the conference proceedings of "Bildverarbeitung in der Medizin 2019". Paper written in Germa

Case report: Blindness associated with Learedius learedi trematode infection in a green sea turtle, Chelonia mydas, of the northern Red Sea

Spirorchiid blood flukes are widespread in sea turtles, causing disease and mortality in their populations, with high prevalence in several ocean basins. Besides being leading parasitic causes of sea turtle strandings in several parts of the world, these infectious agents can cause endocarditis, vasculitis, thrombosis, miliary egg granulomas, and aneurysms, which ultimately may compromise the survival of green sea turtles. More severe cases may also result in multifocal granulomatous meningitis or pneumonia, both of which can be fatal. Herein, we report the first case of severe trematode infection, Caused by Learedius learedi, in a green sea turtle in the northern Red Sea; this infection is associated with bilateral blindness. Necropsy revealed multiple granulomas with intralesional trematode eggs in the optic nerve, eyes, spleen, heart, and lungs. The parasite was identified as Learedius learedi through specific primers of the ribosomal genome and COI sequences obtained from GenBank. Altogether, these findings emphasize the importance of recognizing the systemic nature of this particular fluke infection to ultimately protect the lives of these marine animals and ensure the sustainability of these species in the wild

IBEX:A versatile multiplex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues

The diverse composition of mammalian tissues poses challenges for understanding the cell–cell interactions required for organ homeostasis and how spatial relationships are perturbed during disease. Existing methods such as single-cell genomics, lacking a spatial context, and traditional immunofluorescence, capturing only two to six molecular features, cannot resolve these issues. Imaging technologies have been developed to address these problems, but each possesses limitations that constrain widespread use. Here we report a method that overcomes major impediments to highly multiplex tissue imaging. “Iterative bleaching extends multiplexity” (IBEX) uses an iterative staining and chemical bleaching method to enable high-resolution imaging of >65 parameters in the same tissue section without physical degradation. IBEX can be employed with various types of conventional microscopes and permits use of both commercially available and user-generated antibodies in an “open” system to allow easy adjustment of staining panels based on ongoing marker discovery efforts. We show how IBEX can also be used with amplified staining methods for imaging strongly fixed tissues with limited epitope retention and with oligonucleotide-based staining, allowing potential cross-referencing between flow cytometry, cellular indexing of transcriptomes and epitopes by sequencing, and IBEX analysis of the same tissue. To facilitate data processing, we provide an open-source platform for automated registration of iterative images. IBEX thus represents a technology that can be rapidly integrated into most current laboratory workflows to achieve high-content imaging to reveal the complex cellular landscape of diverse organs and tissues

Artificial Sequences and Complexity Measures

In this paper we exploit concepts of information theory to address the fundamental problem of identifying and defining the most suitable tools to extract, in a automatic and agnostic way, information from a generic string of characters. We introduce in particular a class of methods which use in a crucial way data compression techniques in order to define a measure of remoteness and distance between pairs of sequences of characters (e.g. texts) based on their relative information content. We also discuss in detail how specific features of data compression techniques could be used to introduce the notion of dictionary of a given sequence and of Artificial Text and we show how these new tools can be used for information extraction purposes. We point out the versatility and generality of our method that applies to any kind of corpora of character strings independently of the type of coding behind them. We consider as a case study linguistic motivated problems and we present results for automatic language recognition, authorship attribution and self consistent-classification.Comment: Revised version, with major changes, of previous "Data Compression approach to Information Extraction and Classification" by A. Baronchelli and V. Loreto. 15 pages; 5 figure

Common Limitations of Image Processing Metrics:A Picture Story

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.Comment: This is a dynamic paper on limitations of commonly used metrics. The current version discusses metrics for image-level classification, semantic segmentation, object detection and instance segmentation. For missing use cases, comments or questions, please contact [email protected] or [email protected]. Substantial contributions to this document will be acknowledged with a co-authorshi

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.Comment: Shared first authors: Annika Reinke, Minu D. Tizabi; shared senior authors: Paul F. J\"ager, Lena Maier-Hei