There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource

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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Medical image registration is an important task in medical image processing. It refers to the process of aligning data sets, possibly from different modalities (e.g., magnetic resonance and computed tomography), different time points (e.g., follow-up scans), and/or different subjects (in case of population studies). A large number of methods for image registration are described in the literature. Unfortunately, there is not one method that works for all applications. We have therefore developed elastix, a publicly available computer program for intensity-based medical image registration. The software consists of a collection of algorithms that are commonly used to solve medical image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. The command-line interface enables automated processing of large numbers of data sets, by means of scripting. The usage of elastix for comparing different registration methods is illustrated with three example experiments, in which individual components of the registration method are varied.

elastix : A Toolbox for Intensity-Based Medical Image Registration

Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

Ductal carcinoma in situ (DCIS) of the breast represents a heterogeneous group of neoplastic lesions in the breast ducts. The goal for management of DCIS is to prevent the development of invasive breast cancer. This manuscript focuses on the NCCN Guidelines Panel recommendations for the workup, primary treatment, risk reduction strategies, and surveillance specific to DCIS.

https://www.unitypoint.org/desmoines/filesimages/Cancer%20Education/Oncology%20Nursing%20Conference/2015/2014%20NCCN%20Clin%20Pract%20Guidelines%20Male%20Sex%20Dysf.pdf

Clinical practice guidelines in oncology

Zur Unterstutzung onkologischer Interventionen konnen durch die Registrierung praoperativer Bildaten zu intraoperativen Cone-Beam-Computertomographieaufnahmen (CBCT) zusatzliche Informationen uber die Anatomie und Morphologie des Patienten erhalten werden. In der vorliegenden Arbeit wird eine neuartige Metrik fur die gradientenbasierte Bildregistrierung vorgestellt.

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3D Histograms of Oriented Gradients zur Registrierung von regulären CT mit interventionellen CBCT Daten

Zusammenfassung. Wir stellen eine neuartige Methode zur Segmentierung volumetrischer Bilddaten vor, die Techniken von statistischen Formmodellen mit deformierbaren Oberflachen kombiniert. Die internen Krafte der deformierbaren Oberflache berechnen sich dabei aus einem mitlaufenden Formmodell, wahrend die externen Krafte aus einem graph-basierten Verfahren zur optimalen Oberflachenerkennung gewonnen werden. Testsegmentierungen der Leber auf uber 50 CT-Datensatzen zeigen eine mittlere Oberflachenabweichung von 1.6 ± 0.7mm zu manuellen Referenzsegmentierungen und machen deutlich, dass mehr Freiheit fur Formmodelle die Ergebnisse oft signifikant verbessern kann.

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Freiheit für Formmodelle! Eine robuste Erweiterung der Deformationsgleichungen

Planung und Simulation von Patchimplantaten zur intrakardialen Korrektur angeborener Herzfehler

To obtain a better insight in tissue microstructures using diffusion MRI, a high resolution and dense sampling of q-space is required. In clinical settings, however, this can often not be achieved due to limited acquisition time. Reduced field-of-view (FOV) approaches counteract this limitation but may pose a challenge for the post-processing steps such as motion and artifact correction. We present an evaluation of the potential problems that arise with reduced FOV data during the standard post-processing. The acquisition with reduced FOV is extracted from a full FOV dataset. We select three different registration tools to perform the standard data post-processing pipeline. We first evaluate the spatial error and then measure its impact on the tensor reconstruction as well as on the derived fractional anisotropy (FA). With reduced FOV images, the multi-scale registration methods showed high sensitivity to parameter selection and produced up to 30 % outliers. With an optimized parameter set, all registration methods yielded spatial errors of 1 mm (±0.572). The spatial error resulted in a mean error of 0.03 (±0.013) in the estimated FA values, and was thus of the same magnitude as group differences as they are typically reported in DTI studies. Regions with large FA differences were located especially in the corpus callosum. The evaluation indicates that diffusion-weighted MR acquisitions with reduced FOV require careful selection of registration parameters and also cautious interpretation when quantifying derived indices.

A Quantitative Evaluation of Errors Induced by Reduced Field-of-View in Diffusion Tensor Imaging

Minimally invasive surgeries have restricted surgical ports, demanding a high skill level from the surgeon. Surgical simulation potentially reduces this steep learning curve and additionally provides quantitative feedback. Markerless depth sensors show great promise for quantification, but most such sensors are not designed for accurate reconstruction of complex anatomical forms in close-range.This work compares three commercially available depth sensors, namely the Intel D405, D415, and the Stereolabs Zed-Mini in the range of 12-20 cm, for use in surgical simulation. Three environments are designed that closely mimic surgical simulation, comprising planar surfaces, rigid objects, and mitral valve models of silicone and realistic porcine tissue. The cameras are evaluated on Z-accuracy, temporal noise, fill rate, checker distance, point cloud comparisons, and visual inspection of surgical scenes, across several camera settings.The Intel cameras show sub-mm accuracy in most static environments. The D415 fails in reconstructing valve models, while the Zed-Mini provides lesser temporal noise and higher fill rate. The D405 could reconstruct anatomical structures like the mitral valve leaflet and a ring prosthesis, but performs poorly for reflective surfaces like surgical tools and thin structures like sutures.If a high temporal resolution is needed and lower spatial resolution is acceptable, the Zed-Mini is the best choice, whereas the Intel D405 is the most suited for close-range applications. The D405 shows potential for applications like deformable registration of surfaces, but is not yet suitable for applications like real-time tool tracking or surgical skill assessment. 

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Ivo Wolf

Papers

3D Histograms of Oriented Gradients zur Registrierung von regulären CT mit interventionellen CBCT Daten

Freiheit für Formmodelle! Eine robuste Erweiterung der Deformationsgleichungen

Planung und Simulation von Patchimplantaten zur intrakardialen Korrektur angeborener Herzfehler

A Quantitative Evaluation of Errors Induced by Reduced Field-of-View in Diffusion Tensor Imaging

Comparative evaluation of three commercially available markerless depth sensors for close-range use in surgical simulation