CNN-based Lung CT Registration with Multiple Anatomical Constraints

Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance as conventional registration methods because they are either limited to small deformation or they fail to handle a superposition of large and small deformations without producing implausible deformation fields with foldings inside. In this paper, we identify important strategies of conventional registration methods for lung registration and successfully developed the deep-learning counterpart. We employ a Gaussian-pyramid-based multilevel framework that can solve the image registration optimization in a coarse-to-fine fashion. Furthermore, we prevent foldings of the deformation field and restrict the determinant of the Jacobian to physiologically meaningful values by combining a volume change penalty with a curvature regularizer in the loss function. Keypoint correspondences are integrated to focus on the alignment of smaller structures. We perform an extensive evaluation to assess the accuracy, the robustness, the plausibility of the estimated deformation fields, and the transferability of our registration approach. We show that it achieves state-of-the-art results on the COPDGene dataset compared to conventional registration method with much shorter execution time. In our experiments on the DIRLab exhale to inhale lung registration, we demonstrate substantial improvements (TRE below $1.2$ mm) over other deep learning methods. Our algorithm is publicly available at https://grand-challenge.org/algorithms/deep-learning-based-ct-lung-registration/

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods

A robust algorithm for optic disc segmentation and fovea detection in retinal fundus images

Accurate optic disc (OD) segmentation and fovea detection in retinal fundus images are crucial for diagnosis in ophthalmology. We propose a robust and broadly applicable algorithm for automated, robust, reliable and consistent fovea detection based on OD segmentation. The OD segmentation is performed with morphological operations and Fuzzy C Means Clustering combined with iterative thresholding on a foreground segmentation. The fovea detection is based on a vessel segmentation via morphological operations and uses the resulting OD segmentation to determine multiple regions of interest. The fovea is determined from the largest, vessel-free candidate region. We have tested the novel method on a total of 190 images from three publicly available databases DRIONS, Drive and HRF. Compared to results of two human experts for DRIONS database, our OD segmentation yielded a dice coefficient of 0.83. Note that missing ground truth and expert variability is an issue. The new scheme achieved an overall success rate of 99.44% for OD detection and an overall success rate of 96.25% for fovea detection, which is superior to state-of-the-art approaches

Alles, was Recht ist - Rahmenbedingungen der Buchbranche

Der vorliegende Band entstand im Wintersemester 2010/11 als Begleitband zu einer Vortragsreihe. Der Tagungsband Alles was Recht ist – Rahmenbedingungen der Buchbranche thematisiert Rechtsfragen der Buchbranche aus unterschiedlichen Blickwinkeln: Schriftsteller kommen ebenso zu Wort wie auch Verleger, die Zeitschriftenkrise wissenschaftlicher Bibliotheken wird behandelt ebenso wie der deutsche E-Book-Markt im Vergleich mit dem amerikanischen. Das Recht am eigenen Bild, die Frage nach Schutzmechanismen für digitale Inhalte und das Urheberrecht in seiner Entwicklung sind weitere Themen.The present volume was created in the winter term 2010/11 as a companion volume of a lecture series. The conference proceedings Alles was Recht ist – Rahmenbedingungen der Buchbranche (Everything that is Right - framework conditions of the book industry) addresses legal issues of the book industry from different points of view: Authors and publishers both get a chance to speak, and the magazine crisis of scientific libraries is discussed like the German e-book market is compared to the American one. The right to one's own image, the question concerning protection measures for digital contents and the copyright law in its development are further topics

Running in the wheel: Defining individual severity levels in mice.

The fine-scale grading of the severity experienced by animals used in research constitutes a key element of the 3Rs (replace, reduce, and refine) principles and a legal requirement in the European Union Directive 2010/63/EU. Particularly, the exact assessment of all signs of pain, suffering, and distress experienced by laboratory animals represents a prerequisite to develop refinement strategies. However, minimal and noninvasive methods for an evidence-based severity assessment are scarce. Therefore, we investigated whether voluntary wheel running (VWR) provides an observer-independent behaviour-centred approach to grade severity experienced by C57BL/6J mice undergoing various treatments. In a mouse model of chemically induced acute colitis, VWR behaviour was directly related to colitis severity, whereas clinical scoring did not sensitively reflect severity but rather indicated marginal signs of compromised welfare. Unsupervised k-means algorithm-based cluster analysis of body weight and VWR data enabled the discrimination of cluster borders and distinct levels of severity. The validity of the cluster analysis was affirmed in a mouse model of acute restraint stress. This method was also applicable to uncover and grade the impact of serial blood sampling on the animal's welfare, underlined by increased histological scores in the colitis model. To reflect the entirety of severity in a multidimensional model, the presented approach may have to be calibrated and validated in other animal models requiring the integration of further parameters. In this experimental set up, however, the automated assessment of an emotional/motivational driven behaviour and subsequent integration of the data into a mathematical model enabled unbiased individual severity grading in laboratory mice, thereby providing an essential contribution to the 3Rs principles

Time to Integrate to Nest Test Evaluation in a Mouse DSS-Colitis Model

<div><p>Severity assessment in laboratory animals is an important issue regarding the implementation of the 3R concept into biomedical research and pivotal in current EU regulations. In mouse models of inflammatory bowel disease severity assessment is usually undertaken by clinical scoring, especially by monitoring reduction of body weight. This requires daily observance and handling of each mouse, which is time consuming, stressful for the animal and necessitates an experienced observer. The time to integrate to nest test (TINT) is an easily applicable test detecting disturbed welfare by measuring the time interval mice need to integrate nesting material to an existing nest. Here, TINT was utilized to assess severity in a mouse DSS-colitis model. TINT results depended on the group size of mice maintained per cage with most consistent time intervals measured when co-housing 4 to 5 mice. Colitis was induced with 1% or 1.5% DSS in group-housed WT and <i>Cd14</i>-deficient mice. Higher clinical scores and loss of body weight were detected in 1.5% compared to 1% DSS treated mice. TINT time intervals showed no dose dependent differences. However, increased clinical scores, body weight reductions, and increased TINT time intervals were detected in <i>Cd14</i>^<i>-/-</i> compared to WT mice revealing mouse strain related differences. Therefore, TINT is an easily applicable method for severity assessment in a mouse colitis model detecting CD14 related differences, but not dose dependent differences. As TINT revealed most consistent results in group-housed mice, we recommend utilization as an additional method substituting clinical monitoring of the individual mouse.</p></div

Intestinal inflammation induced by DSS-treatment.

<p>Hematoxylin and eosin staining of colon tissue obtained from (A-D) wild-type and (E-H) <i>Cd14</i>-deficient mice treated with 1% DSS for seven days (C-D and G-H, respectively). Untreated controls (A-B and E-F) did not show any signs of inflammation. Colitis was characterized by the presence of mixed cell infiltrates, hyperplasia, abnormal crypt architecture, edema and erosions (see boxed magnifications D and H). Original magnification 5x and 10x. Histological score quantifying the alterations observed in the colon (J).</p

Clinical disease activity score after DSS treatment.

<p>Assessment of severity in controls (A, B) and DSS treated mice (1% DSS C, D and 1.5% DSS E, F) determined by an overall clinical disease activity score (A, C, E) and specifically by the change in body weight (B, D, F). Untreated controls exhibited low clinical scores (A) and a steady body weight (B). Mice treated with 1% (C, D) or 1.5% DSS (E, F) demonstrated increasing clinical scores and loss of body weight. <i>Cd14</i>^<i>-/-</i> mice showed significantly higher clinical scores and a significantly higher reduction of body weight than WT mice.</p

Group size effect on TINT reliance.

<p>TINT time intervals determined in untreated WT mice on three consecutive days. A group size of 4 to 5 mice per cage resulted in consistent time intervals.</p

Histology score.

<p>Histology score.</p